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Appl. Sci., Volume 8, Issue 10 (October 2018) – 305 articles

Cover Story (view full-size image): Nanowire targets allow an effective penetration of the laser pulse into the target resulting in a significantly increase of absorption of the laser energy. Combining such targets with moderate energy laser pulses we were able to generate an enhanced hard X-ray up to gamma ray emission. We also demonstrate a strong flux of energetic electrons produced within our nanowire targets. This study paws a way for a development of a relatively compact and efficient hard X-ray source operating at a high repetition rate. View this paper.
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13 pages, 3436 KiB  
Article
Mono- and Bi-Molecular Adsorption of SF6 Decomposition Products on Pt Doped Graphene: A First-Principles Investigation
by Yongqian Wu, Shaojian Song, Dachang Chen and Xiaoxing Zhang
Appl. Sci. 2018, 8(10), 2010; https://doi.org/10.3390/app8102010 - 22 Oct 2018
Cited by 5 | Viewed by 3610
Abstract
Based on the first-principles of density functional theory, the SF6 decomposition products including single molecule (SO2F2, SOF2, SO2), double homogenous molecules (2SO2F2, 2SOF2, 2SO2) and double [...] Read more.
Based on the first-principles of density functional theory, the SF6 decomposition products including single molecule (SO2F2, SOF2, SO2), double homogenous molecules (2SO2F2, 2SOF2, 2SO2) and double hetero molecules (SO2 and SOF2, SO2 and SO2F2, SOF2 and SO2F2) adsorbed on Pt doped graphene were discussed. The adsorption parameters, electron transfer, electronic properties and energy gap was investigated. The adsorption of SO2, SOF2 and SO2F2 on the surface of Pt-doped graphene was a strong chemisorption process. The intensity of chemical interactions between the molecule and the Pt-graphene for the above three molecules was SO2F2 > SOF2 > SO2. The change of energy gap was also studied and according to the value of energy gap, the conductivity of Pt-graphene before and after adsorbing different gas molecules can be evaluated. Full article
(This article belongs to the Section Nanotechnology and Applied Nanosciences)
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<p>Geometric structures after optimization: (<b>a</b>) SO<sub>2</sub> molecule; (<b>b</b>) SOF<sub>2</sub> molecule; (<b>c</b>) SO<sub>2</sub>F<sub>2</sub> molecule. (Yellow = Sulfur; Red = Oxygen; Cyan = Fluorine, the labels are also applied to the following figures.).</p>
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<p>Geometric structures after optimization: (<b>a</b>) Int-graphene; (<b>b</b>) Top view of Pt-graphene; (<b>c</b>) Side view of Pt-graphene. (Grey = Carbon; Dark blue = Platinum, the labels are also applied to the following figures.).</p>
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<p>The adsorption structures for single SO<sub>2</sub>, SOF<sub>2</sub> and SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene. (<b>a</b>) SO<sub>2</sub>; (<b>b</b>) SOF<sub>2</sub>; (<b>c</b>) SO<sub>2</sub>F<sub>2</sub>.</p>
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<p>The adsorption structures for double SO<sub>2</sub>, SOF<sub>2</sub> and SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene. (<b>a</b>) 2SO<sub>2</sub>; (<b>b</b>) 2SOF<sub>2</sub>; (<b>c</b>) 2SO<sub>2</sub>F<sub>2</sub>.</p>
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<p>The adsorption structures for double foreign SO<sub>2</sub>, SOF<sub>2</sub> and SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene. (<b>a</b>) SO<sub>2</sub> and SOF<sub>2</sub>; (<b>b</b>) SO<sub>2</sub> and SO<sub>2</sub>F<sub>2</sub>; (<b>c</b>) SOF<sub>2</sub> and SO<sub>2</sub>F<sub>2</sub>.</p>
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<p>The change of density of states (DOS) (<b>a</b>) before and after (<b>b</b>) single SO<sub>2</sub>, (<b>c</b>) single SOF<sub>2</sub> and (<b>d</b>) single SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene.</p>
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<p>The change of DOS (<b>a</b>) before and after (<b>b</b>) double SO<sub>2</sub>, (<b>c</b>) double SOF<sub>2</sub> and (<b>d</b>) double SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene.</p>
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<p>The change of DOS (<b>a</b>) before and after (<b>b</b>) SO<sub>2</sub> &amp; SOF<sub>2</sub>, (<b>c</b>) SO<sub>2</sub> &amp; SO<sub>2</sub>F<sub>2</sub> and (<b>d</b>) SOF<sub>2</sub> &amp; SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene.</p>
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<p>The change of DOS (<b>a</b>) before and after (<b>b</b>) SO<sub>2</sub> &amp; SOF<sub>2</sub>, (<b>c</b>) SO<sub>2</sub> &amp; SO<sub>2</sub>F<sub>2</sub> and (<b>d</b>) SOF<sub>2</sub> &amp; SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene.</p>
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<p>Distribution of HOMO (red-green) and LUMO (blue-yellow) (<b>a</b>) before and after (<b>b</b>) single SO<sub>2</sub>, (<b>c</b>) single SOF<sub>2</sub> and (<b>d</b>) single SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene.</p>
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<p>Distribution of HOMO (red-green) and LUMO (blue-yellow) (<b>a</b>) before and after (<b>b</b>) SO<sub>2</sub> &amp; SOF<sub>2</sub>, (<b>c</b>) SO<sub>2</sub> &amp; SO<sub>2</sub>F<sub>2</sub> and (<b>d</b>) SOF<sub>2</sub> &amp; SO<sub>2</sub>F<sub>2</sub> adsorption on Pt-graphene.</p>
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10 pages, 4721 KiB  
Article
Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
by Kazuki Koseki, Takuma Matsuo and Shuichi Arikawa
Appl. Sci. 2018, 8(10), 2009; https://doi.org/10.3390/app8102009 - 22 Oct 2018
Cited by 8 | Viewed by 4280
Abstract
A super pressure balloon (SPB) is an aerostatic balloon that can fly at a constant altitude for an extended period. Japan Aerospace Exploration Agency (JAXA) has been developing a light-weight, high strength balloon made of thin polyethylene films and diamond-shaped net with high [...] Read more.
A super pressure balloon (SPB) is an aerostatic balloon that can fly at a constant altitude for an extended period. Japan Aerospace Exploration Agency (JAXA) has been developing a light-weight, high strength balloon made of thin polyethylene films and diamond-shaped net with high tensile fibers. Previous investigations proved that strength requirements on SPB members are satisfied even though the net covering the SPB sometimes becomes damaged during the inflation test. This may be due to non-uniform expansion, which causes stress concentration, however, no method exists to confirm this hypothesis. In this study, we tested a new method called Simplified Digital Image Correlation method (SiDIC) to check if it can measure the displacement of the SPB by using a rubber balloon. After measuring the measurement accuracy of the Digital Image Correlation method (DIC) and SiDIC, we applied both DIC and SiDIC to a rubber balloon covered just with the net. Interestingly, SiDIC entailed a smaller amount of data but could measure the deformation more accurately than DIC. In addition, assuming the stress concentration, one part of the net was bonded to the balloon to restrict the deformation. SiDIC properly identified the undeformed region. Full article
(This article belongs to the Special Issue Advances in Digital Image Correlation (DIC))
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<p>Process for calculation of deformation by DIC. (<b>a</b>) Reference image; (<b>b</b>) Deformed image.</p>
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<p>Coordinates of net intersections. (<b>a</b>) Reference image; (<b>b</b>) Deformed image.</p>
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<p>Experimental setups for the displacement measurement. (<b>a</b>) Random spray pattern; (<b>b</b>) Net + spray pattern; (<b>c</b>) Plastic net.</p>
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<p>Black dotted balloon deformation.</p>
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<p>X-direction deformation and deviation between the distribution map for the region of interest theoretical and experimentally measured values of strain.</p>
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<p>Horizontal displacement determined by DIC superimposed onto the photograph of the balloon.</p>
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<p>Comparison of horizontal displacement distributions obtained by DIC and SiDIC for the control path highlighted in <a href="#applsci-08-02009-f006" class="html-fig">Figure 6</a>.</p>
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<p>Horizontal displacement distribution by (<b>a</b>) DIC; (<b>b</b>) SiDIC, on the photograph of net covered balloon.</p>
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<p>Comparison of horizontal displacement distributions obtained by DIC and SiDIC for the control paths highlighted in <a href="#applsci-08-02009-f008" class="html-fig">Figure 8</a>: (<b>a</b>) Central dotted line; (<b>b</b>) Upper dotted line.</p>
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<p>Misrecognition of the deformation of the net by DIC.</p>
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<p>Measurement around no deformation.</p>
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<p>Displacement distribution region performed by SiDIC in the x-direction.</p>
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26 pages, 1052 KiB  
Article
Business Process Configuration According to Data Dependency Specification
by Luisa Parody, María Teresa Gómez-López, Angel Jesús Varela-Vaca and Rafael M. Gasca
Appl. Sci. 2018, 8(10), 2008; https://doi.org/10.3390/app8102008 - 22 Oct 2018
Cited by 4 | Viewed by 3850
Abstract
Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a [...] Read more.
Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative. Full article
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<p>Conf-BP Architecture.</p>
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<p>Parts of the Configurable Declarative Description [<a href="#B15-applsci-08-02008" class="html-bibr">15</a>].</p>
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<p>Example of Trip Planner Formalization using DOOPT-DEC [<a href="#B15-applsci-08-02008" class="html-bibr">15</a>].</p>
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<p>Imperative Representation of the Declarative Model.</p>
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<p><span class="html-italic">“Flight Search”</span> and <span class="html-italic">“Car Rental 1 Search”</span> Model Possibilities.</p>
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<p>Configuration problem Transformation.</p>
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<p>Flexibility of the BP in terms of the execution time of the Activities.</p>
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<p>Trace example of the Algorithm to create a BPMN-Graph using the COP results.</p>
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<p>Parallel Relationship.</p>
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<p>Exclusive Relationship.</p>
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<p>Inclusive Relationship.</p>
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<p>From graph to BPMN Model Example.</p>
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<p>Trip Planner Model Result.</p>
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<p>Execution Time of Algorithm 1 and Algorithm 2.</p>
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11 pages, 3324 KiB  
Article
Effects of Individual and Environmental Factors on GPS-Based Time Allocation in Urban Microenvironments Using GIS
by Audrius Dėdelė, Auksė Miškinytė, Irma Česnakaitė and Regina Gražulevičienė
Appl. Sci. 2018, 8(10), 2007; https://doi.org/10.3390/app8102007 - 22 Oct 2018
Cited by 4 | Viewed by 3350
Abstract
Time-activity patterns are an essential part of personal exposure assessment to various environmental factors. People move through different environments during the day and they have different daily activity patterns which are significantly influenced by individual characteristics and the residential environment. In this study, [...] Read more.
Time-activity patterns are an essential part of personal exposure assessment to various environmental factors. People move through different environments during the day and they have different daily activity patterns which are significantly influenced by individual characteristics and the residential environment. In this study, time spent in different microenvironments (MEs) were assessed for 125 participants for 7 consecutive days to evaluate the impact of individual characteristics on time-activity patterns in Kaunas, Lithuania. The data were collected with personal questionnaires and diaries. The global positioning system (GPS) sensor integrated into a smartphone was used to track daily movements and to assess time-activity patterns. The study results showed that behavioral and residential greenness have a statistically significant impact on time spent indoors. These results underline the high influence of the individual characteristics and environmental factors on time spent indoors, which is an important determinant for exposure assessment and health impact assessment studies. Full article
(This article belongs to the Section Environmental Sciences)
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<p>The study design.</p>
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<p>The waist belt for the smartphone.</p>
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<p>The GPS tracking data of one participant (different colors represents different microenvironments); background shading is the orthophoto map of Kaunas city.</p>
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13 pages, 4479 KiB  
Article
Sparse Haar-Like Feature and Image Similarity-Based Detection Algorithm for Circular Hole of Engine Cylinder Head
by Wenzhang Zhou, Yong Chen and Siyuan Liang
Appl. Sci. 2018, 8(10), 2006; https://doi.org/10.3390/app8102006 - 22 Oct 2018
Cited by 3 | Viewed by 4355
Abstract
If the circular holes of an engine cylinder head are distorted, cracked, defective, etc., the normal running of the equipment will be affected. For detecting these faults with high accuracy, this paper proposes a detection method based on feature point matching, which can [...] Read more.
If the circular holes of an engine cylinder head are distorted, cracked, defective, etc., the normal running of the equipment will be affected. For detecting these faults with high accuracy, this paper proposes a detection method based on feature point matching, which can reduce the detection error caused by distortion and light interference. First, the effective and robust feature vectors of pixels are extracted based on improved sparse Haar-like features. Then we calculate the similarity and find the most similar matching point from the image. In order to improve the robustness to the illumination, this paper uses the method based on image similarity to map the original image, so that the same region under different illumination conditions has similar spatial distribution. The experiments show that the algorithm not only has high matching accuracy, but also has good robustness to the illumination. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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<p>Four Haar-like feature operators. (<b>a</b>) Two-rectangle feature where rectangular regions are horizontally adjacent; (<b>b</b>) Two-rectangle feature where rectangular regions are vertically adjacent; (<b>c</b>) Three-rectangle feature; (<b>d</b>) Four-rectangle feature.</p>
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<p>Improved Haar-like feature operators.</p>
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<p>The results of Haar-like feature matching. (<b>a</b>) Low brightness; (<b>b</b>) High brightness.</p>
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<p>Improved sparse Haar-like feature operators.</p>
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<p>The relationship between convolution kernel size and neighborhood texture.</p>
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<p>Image pyramid and convolution kernel pyramid. (<b>a</b>) Image pyramid; (<b>b</b>) Convolution kernel pyramid.</p>
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<p>Similarity images of the images with different illuminations.</p>
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<p>Experimental platform for visual inspection of engine cylinder head.</p>
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<p>Matching results of different illumination images by four matching algorithms.</p>
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<p>Matching results of different illumination images by robust enhancement algorithm.</p>
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<p>Matching results of the circular holes on the surface of engine cylinder head.</p>
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<p>Average pixel errors of the radii of detected circles on the surface of engine cylinder head.</p>
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33 pages, 18846 KiB  
Article
Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation Using an Analytical Mapping Method and Quantitative Evaluation
by Zhijun Zhang, Yaru Niu, Ziyi Yan and Shuyang Lin
Appl. Sci. 2018, 8(10), 2005; https://doi.org/10.3390/app8102005 - 22 Oct 2018
Cited by 25 | Viewed by 8299
Abstract
Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable [...] Read more.
Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system. Full article
(This article belongs to the Special Issue Human Friendly Robotics)
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Graphical abstract

Graphical abstract
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<p>The framework of the proposed whole-body imitation system.</p>
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<p>Names and numbers of skeleton points captured by the Kinect II.</p>
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<p>Upper- and lower-limb joints and joint angles of the NAO robot.</p>
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<p>Joint structures and link frames of the robot and the human skeleton model.</p>
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<p>The link frame of the left upper torso of the human skeleton model and the shoulder joint angles for mapping.</p>
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<p>The link frame of the left upper arm of the human skeleton model and the elbow joint angles for mapping.</p>
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<p>The link frame of the left lower torso of the human skeleton model and the hip joint angles for mapping.</p>
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<p>The link frame of the left thigh of the human skeleton model and the knee joint angle for mapping.</p>
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<p>The calculation of the displacement and rotation angle between two adjacent locomotion loops. The blue coordinate axes correspond to the Kinect frame, and the red coordinate axes correspond to the human base frame.</p>
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<p>Snapshots of the robot and the human demonstrator in the first experiment. The red numbers are the ordinal numbers of the motion loops, of which the average duration is 0.26 s.</p>
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<p>Left key joint angle trajectories of the human and the robot in the first experiment. (<b>a</b>)–(<b>f</b>) depict the joint angle trajectories of LShoulderPitch, LShoulderRoll, LElbowRoll, LHipRoll, LHipPitch and LKneePitch, respectively.</p>
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<p>Right key joint angle trajectories of the human and the robot in the first experiment. (<b>a</b>)–(<b>f</b>) depict the joint angle trajectories of RShoulderPitch, RShoulderRoll, RElbowRoll, RHipRoll, RHipPitch and RKneePitch, respectively.</p>
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<p>Similarity index trajectories in the first experiment as calculated using the two evaluation methods.</p>
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<p>Left-thigh similarity index trajectories in the first experiment as calculated using the two evaluation methods.</p>
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<p>Left-tibia similarity index trajectories in the first experiment as calculated using the two evaluation methods.</p>
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<p>Snapshots of some difficult single-support motions that the robot can perform by imitating human demonstrators with different body shapes.</p>
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<p>The real-time imitation system used in the task-oriented teleoperation experiment.</p>
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<p>Description of the teleoperation task; (<b>a</b>) shows that the robot cannot reach the green container in the double-support mode; (<b>b</b>) shows that when the robot leans rightward in the single-support mode, it can easily reach the container; (<b>c</b>) shows that the robot cannot reach the black container in the double-support mode; (<b>d</b>) shows that when the robot walks leftward, it can easily reach the container.</p>
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<p>Snapshots of the robot and the human in the task-oriented teleoperation experiment.</p>
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<p>The snapshots of the hello motion performed by five demonstrators.</p>
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<p>The imitation learning process of the hello motion. The first figure of each joint angle (i.e., RShoulderPitch, RShoulderRoll, RElbowYaw or RElbowRoll) depicts the scaling result of the raw datasets. The second figure depicts the DTW and scaling result of the datasets. The third one depicts the GMM encoding result. The fourth one depicts the generalized angle trajectory produced by GMR.</p>
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<p>The snapshots of the reproduced hello motion performed by NAO.</p>
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19 pages, 7166 KiB  
Article
Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
by Hadi Sanati, David Wood and Qiao Sun
Appl. Sci. 2018, 8(10), 2004; https://doi.org/10.3390/app8102004 - 22 Oct 2018
Cited by 48 | Viewed by 6037
Abstract
The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface [...] Read more.
The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface damage but most require close proximity between the sensor and the blade. Thermography, as a non-contact method, may avoid this problem. Both passive and active pulsed and step heating and cooling thermography techniques were investigated for different purposes. A section of a severely damaged blade and a small “plate” cut from the undamaged laminate section of the blade with holes of varying diameter and depth drilled from the rear to provide “known” defects were monitored. The raw thermal images captured by both active and passive thermography demonstrated that image processing was required to improve the quality of the thermal data. Different image processing algorithms were used to increase the thermal contrasts of subsurface defects in thermal images obtained by active thermography. A method called “Step Phase and Amplitude Thermography”, which applies a transform-based algorithm to step heating and cooling data was used. This method was also applied, for the first time, to the passive thermography results. The outcomes of the image processing on both active and passive thermography indicated that the techniques employed could considerably increase the quality of the images and the visibility of internal defects. The signal-to-noise ratio of raw and processed images was calculated to quantitatively show that image processing methods considerably improve the ratios. Full article
(This article belongs to the Special Issue Wind Turbine Aerodynamics)
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<p>(<b>a</b>) The damaged wind turbine blade. (<b>b</b>,<b>c</b>) The defect plate with flat-bottomed holes. All holes were drilled from the rear and did not penetrate the outer surface.</p>
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<p>Passive thermography experiment.</p>
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<p>Pulsed thermography experiment.</p>
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<p>Step heating thermography experiment.</p>
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<p>(<b>a</b>) Positions of temperature profiles. Temperature profiles using (<b>b</b>) pulsed and (<b>c</b>) step heating thermography. The rows are identified in (<b>a</b>).</p>
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<p>Effect of depth and size of the defect on the temperature distribution in a sample heated up by a halogen lamp for 75 s.</p>
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<p>Four matched filters including (<b>a</b>) Spectral Angle Map (SAM), (<b>b</b>) Adaptive Coherence Estimator (ACE), (<b>c</b>) <span class="html-italic">t</span>-statistic and (<b>d</b>) <span class="html-italic">F</span>-statistic when the specimen was being step heated.</p>
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<p>(<b>a</b>) Normalized signal values along the defect rows and (<b>b</b>) background noise around A2.</p>
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<p>(<b>a</b>) Raw thermogram and (<b>b</b>) phase image (acquisition time = 53.2 s) obtained from the thermal image sequence recorded during cooling after flashing the surface.</p>
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<p>(<b>a</b>) Amplitude image and (<b>b</b>) phase image of the thermograms captured during cooling after 75 s of heating.</p>
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<p>(<b>a</b>) Amplitude image and (<b>b</b>) phase image of thermograms captured during heating for 75 s.</p>
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<p>(<b>a</b>) Normalized amplitude value and (<b>b</b>) normalized phase value distributions of the defects where the thermograms were obtained during cooling and heating, respectively.</p>
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<p>(<b>a</b>) Thermographic results of the experiment around 9 a.m., (<b>b</b>) noon and (<b>c</b>) 6 p.m. (sunrise and sunset were around 5.53 a.m. and 9.30 p.m., respectively). The vertical arrows and dashed lines indicate the shear webs.</p>
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<p>(<b>a</b>) Phase images of the passive thermograms captured during the morning at a frequency of 0.00184 Hz and (<b>b</b>) amplitude image of the passive thermograms recorded during the morning at a frequency of 0.0165 Hz.</p>
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17 pages, 2849 KiB  
Article
No-Reference Blurred Image Quality Assessment by Structural Similarity Index
by Haopeng Zhang, Bo Yuan, Bo Dong and Zhiguo Jiang
Appl. Sci. 2018, 8(10), 2003; https://doi.org/10.3390/app8102003 - 22 Oct 2018
Cited by 30 | Viewed by 5353
Abstract
No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) [...] Read more.
No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality immediately without prior training or learning. Experimental results on four popular datasets show that the proposed metric outperforms SSIM and well-matched to state-of-the-art NR IQA models. Furthermore, we apply NSSIM with known IQA approaches to blurred image restoration and demonstrate that NSSIM is statistically superior to peak signal-to-noise ratio (PSNR), SSIM and consistent with the state-of-the-art NR IQA models. Full article
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<p>A sharp image (<b>left</b>) possesses conspicuous quality decline than a blurred image (<b>middle</b>) after blur processing (<b>right</b>).</p>
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<p>Re-blur Process.</p>
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<p>Feature extraction and structural similarity measurement. We extract four features, i.e., luminance, contrast, structural and blurriness, of the down-sampled 2D mode images and score structural similarity index by computing four features together.</p>
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<p>Sharp (<b>left</b>) and Gaussian blurred (<b>right</b>) images with their grayscale histograms.</p>
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<p>Trend of image quality and blurriness with Gaussian blur times that varies from 0 to 11.</p>
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<p>Sample images in the four datasets together with their subjective DMOS scores. Each set of images contains five Gaussian blur images those have gradient DMOS scores.</p>
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<p>Analysis of performance of SROCC, PLCC and RMSE with <math display="inline"><semantics> <mi mathvariant="bold-italic">λ</mi> </semantics></math> varying from 0 to 5 on LIVE II dataset (145 Gaussian blur images), where <math display="inline"><semantics> <mi mathvariant="bold-italic">λ</mi> </semantics></math> represents the exponent coefficient of blurriness comparison function in Equation (<a href="#FD10-applsci-08-02003" class="html-disp-formula">10</a>).</p>
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<p>Scatter plots of DMOS vs NSSIM predicted scores on four datasets.</p>
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<p>Group 1 restorations: The original image is 480 × 720 × 3 which is provided by LIVE II dataset while the blurred image is produced by a Gaussian low-pass filter of 11 × 11 with deviation 1.5. The restorations are produced by Sroubek [<a href="#B30-applsci-08-02003" class="html-bibr">30</a>] and Kotera [<a href="#B31-applsci-08-02003" class="html-bibr">31</a>], respectively.</p>
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<p>Group 2 restorations: The original image is 512 × 512 × 3 which is provided by IVC dataset while the blurred image is produced by a Gaussian low-pass filter of <math display="inline"><semantics> <mrow> <mn>11</mn> <mo>×</mo> <mn>11</mn> </mrow> </semantics></math> with deviation 1.5. The restorations are produced by Sroubek [<a href="#B30-applsci-08-02003" class="html-bibr">30</a>] and Kotera [<a href="#B31-applsci-08-02003" class="html-bibr">31</a>], respectively.</p>
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23 pages, 1039 KiB  
Article
Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
by Sung Min Cho, Sunghyun Jin and HeeSeok Kim
Appl. Sci. 2018, 8(10), 2002; https://doi.org/10.3390/app8102002 - 22 Oct 2018
Cited by 6 | Viewed by 3153
Abstract
Unified point addition for computing elliptic curve point addition and doubling is considered to be resistant to simple power analysis. Recently, new side-channel attacks, such as recovery of secret exponent by triangular trace analysis and horizontal collision correlation analysis, have been successfully applied [...] Read more.
Unified point addition for computing elliptic curve point addition and doubling is considered to be resistant to simple power analysis. Recently, new side-channel attacks, such as recovery of secret exponent by triangular trace analysis and horizontal collision correlation analysis, have been successfully applied to elliptic curve methods to investigate their resistance to side-channel attacks. These attacks turn out to be very powerful since they only require leakage of a single power consumption trace. In this paper, using these side-channel attack analyses, we introduce two vulnerabilities of unified point addition on the binary Huff curve. Also, we propose a new unified point addition method for the binary Huff curve. Furthermore, to secure against these vulnerabilities, we apply an equivalence class to the side-channel atomic algorithm using the proposed unified point addition method. Full article
(This article belongs to the Special Issue Side Channel Attacks)
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<p>The scheme of the experimental setup used for measuring power consumption.</p>
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<p>A single power consumption trace of field multiplications for binary Huff curve software implementation on an ARM cortex-m4 processor. The power consumption trace is composed of subtraces corresponding to field multiplications.</p>
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<p>Beginning of a field multiplication power consumption trace. Each <span class="html-italic">w</span>-bit multiplication subtrace in a field multiplication can be identified using simple power analysis (SPA) and cross-correlation.</p>
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<p>Squared pairwise t-differences (SOST; line) and points of interest (POIs; red circle). (<b>a</b>) Points having higher SOST values than the heuristic threshold are chosen for HCCA’s POIs. (<b>b</b>) Unlike HCCA, ROSETTA’s POIs, upon which the output value of <span class="html-italic">w</span>-bit multiplication is processed, are chosen heuristically.</p>
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<p>Results of the secret bit value guess by (<b>a</b>) HCCA and (<b>b</b>) ROSETTA. The blue line is the secret bit value guess and the horizontal red line is the threshold value for the secret bit value discrimination; points with a black circle indicate where the attack failed.</p>
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9 pages, 976 KiB  
Article
Quantification of the Transmission Properties of Anisotropic Metasurfaces Illuminated by Finite-Size Beams
by Mohamed Boutria, Abdoulaye Ndao and Fadi I. Baida
Appl. Sci. 2018, 8(10), 2001; https://doi.org/10.3390/app8102001 - 22 Oct 2018
Cited by 1 | Viewed by 2785
Abstract
The aim of this paper is to present an analytical method to quantitatively address the influence of a focusing illumination on the optical response properties of a metasurface illuminated by a finite-size beam. Most theoretical and numerical studies are performed by considering an [...] Read more.
The aim of this paper is to present an analytical method to quantitatively address the influence of a focusing illumination on the optical response properties of a metasurface illuminated by a finite-size beam. Most theoretical and numerical studies are performed by considering an infinite periodic structure illuminated by a plane wave. In practice, one deals with a finite-size illumination and structure. The combination of the angular spectrum expansion with a monomodal modal method is used to determine the beam size needed to acquire efficient properties of a metasurface that behaves as an anisotropic plate. Interesting results show that the beam-size can be as small as 5 × 5 periods to recover the results of a plane wave. Other results also show that the beam-size can be used as an extrinsic parameter to enhance the anisotropic metasurface performance and to adjust its expected properties finely (birefringence and/or transmission coefficient). These findings are important for the design of real (finite) structures and can be adapted for experimental conditions to achieve optimized results and take full advantage of the metamaterial properties. Full article
(This article belongs to the Special Issue Sub-wavelength Optics)
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<p>Schematic of the model with the different frames involved in the theoretical model: <math display="inline"><semantics> <mrow> <mi>O</mi> <mi>x</mi> <mi>y</mi> <mi>z</mi> </mrow> </semantics></math> the metasurface frame, <math display="inline"><semantics> <mrow> <mi>O</mi> <mi>X</mi> <mi>Y</mi> <mi>Z</mi> </mrow> </semantics></math> the incident beam frame, <math display="inline"><semantics> <mrow> <mi>O</mi> <msup> <mi>X</mi> <mrow> <mo>″</mo> </mrow> </msup> <msup> <mi>Y</mi> <mrow> <mo>″</mo> </mrow> </msup> <msup> <mi>Z</mi> <mrow> <mo>″</mo> </mrow> </msup> </mrow> </semantics></math> the reflected beam frame and <math display="inline"><semantics> <mrow> <mi>O</mi> <msup> <mi>X</mi> <mo>′</mo> </msup> <msup> <mi>Y</mi> <mo>′</mo> </msup> <msup> <mi>Z</mi> <mo>′</mo> </msup> </mrow> </semantics></math> the transmitted beam frame. <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>m</mi> </msub> </semantics></math> is the angle of incidence.</p>
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<p>(<b>a</b>) Schema of a unit cell of the anisotropic metasurface. The rectangular apertures are engraved in a perfectly electric conductor layer of thickness <span class="html-italic">h</span>. The two rectangle dimensions are different in order to induce artificial anisotropy. (<b>b</b>) Typical transmission spectra for the two orthogonal polarization states along <math display="inline"><semantics> <mrow> <mi>O</mi> <mi>X</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>O</mi> <mi>Y</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>m</mi> </msub> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math> in the case of an incident plane wave.</p>
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<p>Variations of (<b>a</b>) the transmission efficiency <span class="html-italic">T</span>, (<b>b</b>) the polarization degree <span class="html-italic">P</span>, (<b>c</b>) the phase change <math display="inline"><semantics> <mi>ξ</mi> </semantics></math> and (<b>d</b>) the <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>O</mi> <mi>M</mi> </mrow> </semantics></math> defined by Equation (<a href="#FD10-applsci-08-02001" class="html-disp-formula">10</a>) in the case of the half-wave plate operating at <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1.194</mn> <mi>p</mi> </mrow> </semantics></math> as a function of the illumination Gaussian beam size <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>p</mi> </mrow> </semantics></math>. The beam is impinging the structure under normal incidence and is polarized at 45<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> from the <span class="html-italic">x</span>-axis in order to get transmission across the two perpendicular rectangles. The angular spectrum expansion of the beam is described with <math display="inline"><semantics> <mrow> <mn>128</mn> <mo>×</mo> <mn>128</mn> </mrow> </semantics></math> harmonics. The inset in (d) corresponds to a zoom made around <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>O</mi> <mi>M</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Same study as in <a href="#applsci-08-02001-f003" class="html-fig">Figure 3</a>, but for the quarter-wave plate operating at <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1.182</mn> <mi>p</mi> </mrow> </semantics></math>.</p>
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<p>Incident (<b>a</b>) and transmitted electric field intensities in a transversal plane located at <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>30</mn> <mi>p</mi> </mrow> </semantics></math> from the anisotropic metasurface in the case of <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> (<b>b</b>) and <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math> (<b>c</b>) plates. Figures (<b>a</b>,<b>b</b>) to (<b>g</b>,<b>h</b>) refer to the <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> plate while figures (<b>i</b>,<b>j</b>) to (<b>o</b>,<b>p</b>) are attributed to the <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math> plate. The total dimension of each figure is <math display="inline"><semantics> <mrow> <mn>32</mn> <mi>p</mi> <mo>×</mo> <mn>32</mn> <mi>p</mi> </mrow> </semantics></math>. The waist of the beam is located at <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, and its value is fixed to <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>p</mi> </mrow> </semantics></math>.</p>
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11 pages, 1892 KiB  
Article
The Effect of Intermediate Principal Stress on Compressive Strength of Different Cement Content of Cement-Stabilized Macadam and Different Gradation of AC-13 Mixture
by Hong-xin Guan, Hao-qing Wang, Hao Liu, Jia-jun Yan and Miao Lin
Appl. Sci. 2018, 8(10), 2000; https://doi.org/10.3390/app8102000 - 22 Oct 2018
Cited by 12 | Viewed by 3290
Abstract
Since the effect of intermediate principal stress on the strength of pavement materials is not entirely clear so far, a proprietary true triaxial apparatus was developed to simulate the spatial status of principal stresses to conduct compressive strength tests on different gradations of [...] Read more.
Since the effect of intermediate principal stress on the strength of pavement materials is not entirely clear so far, a proprietary true triaxial apparatus was developed to simulate the spatial status of principal stresses to conduct compressive strength tests on different gradations of AC-13, different cement contents of cement-stabilized macadam. With the same minimum principal stress, the triaxial compressive strengths of cube specimens under different intermediate principal stresses were compared. The results indicate that, as the intermediate principal stress increases, the compressive strength of the specimen increases and then decreases; different gradations of AC-13 do not show much difference in triaxial compressive strength while different cement contents of cement-stabilized macadam indicate considerable difference. Analysis results suggest significant effect of intermediate principal stress on the compressive strength of pavement materials: for AC-13, the coarser the gradation, the greater the effect of intermediate principal strength on its strength; for cement-stabilized Macadam, the higher the cement content, the greater the effect of intermediate principal stress. Strength model analysis results suggest that Double-Shear-Corner Model is more suitable to characterize cement-stabilized macadam’s strength performance compared to the Mohr–Coulomb model and Double-Shear Model. Full article
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<p>Self-developed true triaxial test machine.</p>
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<p>Schematic diagram for loading on specimen.</p>
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<p>The Mohr stress circles and shear strength envelope of AC-13C.</p>
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<p>AC-13 triaxial compressive strength changing with intermediate principal stress.</p>
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<p>Intermediate principle stress influence factor b vs. strength of different gradation AC-13 mixture.</p>
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<p>The principal stress effect on the cement stabilized macadam strength of different cement dosage.</p>
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12 pages, 1976 KiB  
Article
Effects of Silk-Worm Excrement Biochar Combined with Different Iron-Based Materials on the Speciation of Cadmium and Lead in Soil
by Pengyang Bian, Jingjing Zhang, Chaolan Zhang, He Huang, Qun Rong, Haixia Wu, Xue Li, Mengmeng Xu, Yu Liu and Siwei Ren
Appl. Sci. 2018, 8(10), 1999; https://doi.org/10.3390/app8101999 - 22 Oct 2018
Cited by 11 | Viewed by 4088
Abstract
A 56d incubation experiment was conducted to explore the effects of the silk-worm excrement biochar (500 °C, BC) combined with different iron-based materials (FeCl3, FeSO4, and reduced iron powder) on the speciation of cadmium (Cd) and lead (Pb) in a [...] Read more.
A 56d incubation experiment was conducted to explore the effects of the silk-worm excrement biochar (500 °C, BC) combined with different iron-based materials (FeCl3, FeSO4, and reduced iron powder) on the speciation of cadmium (Cd) and lead (Pb) in a contaminated soil. Application rate of BC and iron-based materials is 1% (W/W) and 0.2% (W/W) of the soil, respectively. At the same time, the soil physicochemical properties, such as pH, cation exchange capacity (CEC), and the structure of soil, were determined in order to explore the influence mechanism of amendments to forms of Cd and Pb in soil. The results show that the stabilization effects on Cd is (BC + FeSO4) > (BC + FeCl3) > (BC + Fe) > (BC) and Pb is (BC + Fe) > (BC + FeSO4) > (BC + FeCl3) > (BC) at the end of incubation, compared with the effect of the control group. The treatment of (BC + FeSO4) is the most effective in terms of the stabilization of Cd and Pb, which makes the percentages of organic-bound and residual Cd and Pb increase by 40.90% and 23.51% respectively. In addition, with different ways of treatment, the pH value and CEC of soil see a remarkable increase by 1.65–2.01 units and 2.01–2.58 cmol·kg−1 respectively. X-ray diffraction (XRD) patterns show that the soil imprisons Cd and Pb in different mineral phases. As such the treatment of (BC + FeSO4) can significantly improve soil environment, increase soil pH value & CEC and exert a relatively good stabilization effect on both Cd and Pb. Full article
(This article belongs to the Special Issue Sustainable Environmental Remediation)
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<p>The effect of different treatments on chemical form of Cd in soil.</p>
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<p>The effect of different treatments on chemical form of Pb in soil.</p>
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<p>The effect of different treatments on soil pH. Note: different letters for the <a href="#applsci-08-01999-f003" class="html-fig">Figure 3</a> indicates that the differences are statistically significant (<span class="html-italic">P</span> &lt; 0.05).</p>
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<p>The effect of different treatments on soil cation exchange capacity (CEC). Note: different letters for the <a href="#applsci-08-01999-f004" class="html-fig">Figure 4</a> indicates that the differences are statistically significant (<span class="html-italic">P</span> &lt; 0.05).</p>
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<p>X-ray diffraction (XRD) patterns of the soil under different treatments. 1: Pb<sub>5</sub>O<sub>4</sub>, 2: Cd(OH)NO<sub>3</sub>, 3: Pb<sub>3</sub>(CO<sub>3</sub>)<sub>2</sub>(OH)<sub>2</sub>, 4: Cd(OH)<sub>2</sub>, BC: biochar, CK: control sample.</p>
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7 pages, 241 KiB  
Article
Self-Consistent Derivation of the Modified Gross–Pitaevskii Equation with Lee–Huang–Yang Correction
by Luca Salasnich
Appl. Sci. 2018, 8(10), 1998; https://doi.org/10.3390/app8101998 - 21 Oct 2018
Cited by 12 | Viewed by 3922
Abstract
We consider a dilute and ultracold bosonic gas of weakly-interacting atoms. Within the framework of quantum field theory, we derive a zero-temperature modified Gross–Pitaevskii equation with beyond-mean-field corrections due to quantum depletion and anomalous density. This result is obtained from the stationary equation [...] Read more.
We consider a dilute and ultracold bosonic gas of weakly-interacting atoms. Within the framework of quantum field theory, we derive a zero-temperature modified Gross–Pitaevskii equation with beyond-mean-field corrections due to quantum depletion and anomalous density. This result is obtained from the stationary equation of the Bose–Einstein order parameter coupled to the Bogoliubov–de Gennes equations of the out-of-condensate field operator. We show that, in the presence of a generic external trapping potential, the key steps to get the modified Gross–Pitaevskii equation are the semiclassical approximation for the Bogoliubov–de Gennes equations, a slowly-varying order parameter and a small quantum depletion. In the uniform case, from the modified Gross–Pitaevskii equation, we get the familiar equation of state with Lee–Huang–Yang correction. Full article
(This article belongs to the Special Issue Optical Properties of Confined Quantum Systems)
15 pages, 3460 KiB  
Article
Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
by Yu Zhan, Enda Zhang, Yiming Ge and Changsheng Liu
Appl. Sci. 2018, 8(10), 1997; https://doi.org/10.3390/app8101997 - 20 Oct 2018
Cited by 22 | Viewed by 4887
Abstract
Laser welding is widely used in titanium alloy welding due to its high energy density, small heat affected zone, and rapid processing ability. However, problems with laser welding, such as deformation and cracking caused by residual stress, need to be resolved. In this [...] Read more.
Laser welding is widely used in titanium alloy welding due to its high energy density, small heat affected zone, and rapid processing ability. However, problems with laser welding, such as deformation and cracking caused by residual stress, need to be resolved. In this paper, the residual stress in laser welding of TC4 titanium alloy was studied using an ultrasonic laser. The residual stress in titanium alloy plates is considered a plane stress state. A pre-stress loading method is proposed and acoustoelastic coefficients are obtained. Based on the known acoustoelastic coefficients, the transverse and longitudinal residual stresses in laser welding are measured using an ultrasonic laser. The results show that longitudinal residual stress is greater than the transverse stress. The distribution regularity of the residual stress is similar to normal welding, but the tensile stress zone is much narrower. Then, the influence of heat input and welding speed on residual stress is discussed. With increasing heat input, the welding zone widens, and the peak value of the residual stress increases. A higher welding speed should be chosen when the welding power is constant. This research has important significance for the measurement and control of residual stress in the laser welding process. Full article
(This article belongs to the Special Issue Laser Ultrasonics)
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<p>Experimental system: (<b>a</b>) ultrasonic laser and (<b>b</b>) pre-stress loading apparatus.</p>
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<p>Travel time of surface waves in the base material without stress. The change in distance is <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>l</mi> <mo>=</mo> <mn>12.82</mn> <mtext> </mtext> <mi>mm</mi> </mrow> </semantics></math>, Channel 1 is the vibration signal, Channel 2 is the time reference signal, a and b are the cursors for reading travel time.</p>
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<p>Linear fitting curve of the experimental results, the slope represents the velocity of surface wave propagation in the TC4 base material.</p>
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<p>The relationship between velocity ratio and tensile stress. The slopes of the linear fitting curve correspond to the acoustoelastic coefficients.</p>
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<p>(<b>a</b>) Experimental specimen and (<b>b</b>) layout of measuring line when measuring the velocity <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>y</mi> </msub> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is a measurement point; <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is the measuring line; <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>3</mn> </msub> </mrow> </semantics></math> are the auxiliary lines; and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>2</mn> </msub> </mrow> </semantics></math> are the positions of the laser beam.</p>
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<p>The residual stress distribution in TC4 laser welding at a welding power of 2800 W, welding speed of 6.0 m/min, and heat input of 28 kJ/m. The longitudinal residual stresses measured by the hole-drilling method and ultrasonic laser method were consistent.</p>
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<p>Diagram of sensitive grid arrangement <math display="inline"><semantics> <mi>θ</mi> </semantics></math> is the angle between the principal stress <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>x</mi> </msub> </mrow> </semantics></math> and the strain gauge <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, and the angle between the strain gauge <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>3</mn> </msub> </mrow> </semantics></math> is <math display="inline"><semantics> <mrow> <mn>135</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Longitudinal residual stress distribution with different heat inputs.</p>
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<p>The relationship between maximum longitudinal residual stress and heat input. Three approximate linear stages are presented and the maximum residual stress changes slowly in the second stage.</p>
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<p>The longitudinal residual stress distribution with the same welding power: (<b>a</b>) 2000 W, (<b>b</b>) 1400 W, and (<b>c</b>) 2800 W.</p>
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<p>Comparison of the longitudinal residual stress before and after heat treatment. The maximum of longitudinal residual stress was reduced from 423 MPa to 92 MPa, a decrease of 78%.</p>
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24 pages, 2350 KiB  
Review
A Brief Review of Specialty Optical Fibers for Brillouin-Scattering-Based Distributed Sensors
by Peter Dragic and John Ballato
Appl. Sci. 2018, 8(10), 1996; https://doi.org/10.3390/app8101996 - 20 Oct 2018
Cited by 26 | Viewed by 5816
Abstract
Specialty optical fibers employed in Brillouin-based distributed sensors are briefly reviewed. The optical and acoustic waveguide properties of silicate glass optical fiber first are examined with the goal of constructing a designer Brillouin gain spectrum. Next, materials and their effects on the relevant [...] Read more.
Specialty optical fibers employed in Brillouin-based distributed sensors are briefly reviewed. The optical and acoustic waveguide properties of silicate glass optical fiber first are examined with the goal of constructing a designer Brillouin gain spectrum. Next, materials and their effects on the relevant Brillouin scattering properties are discussed. Finally, optical fiber configurations are reviewed, with attention paid to fibers for discriminative or other enhanced sensing configurations. The goal of this brief review is to reinforce the importance of fiber design to distributed sensor systems, generally, and to inspire new thinking in the use of fibers for this sensing application. Full article
(This article belongs to the Special Issue Optical Correlation-domain Distributed Fiber Sensors)
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<p>Refractive index profile (RIP) for a conventional step-index optical fiber.</p>
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<p>RIP and longitudinal acoustic velocity profile for the fiber of the example.</p>
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<p>Simulated Brillouin gain spectrum (BGS) calculated (1534 nm) from the profiles in <a href="#applsci-08-01996-f002" class="html-fig">Figure 2</a>. The positions of the longitudinal mode interactions are also shown. The insets plot the LP<sub>01</sub> optical mode intensity (red) and the acoustic displacement <span class="html-italic">u<sub>z</sub></span>(<span class="html-italic">r</span>) (blue). Vertical dashed lines in the insets delineate the core region.</p>
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<p>Simulated refractive index of binary germanosilicate as a function of the GeO<sub>2</sub> concentration (in mol %) for a base density of 3650 km/m<sup>3</sup> (blue curve). The effect of raising or lowering the density in the model affects the curvature of the functions.</p>
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<p><span class="html-italic">g<sub>B</sub></span> for the binary phosphosilicate glass as a function of mol % of P<sub>2</sub>O<sub>5</sub> relative to that of pure SiO<sub>2</sub> in units of dB. An absolute minimum is observed in the plot. Even a few mol % of P<sub>2</sub>O<sub>5</sub> can cause a significant drop in <span class="html-italic">g<sub>B</sub></span>.</p>
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<p>Brillouin spectral width simulation for pure silica at wavelengths of 633 nm and 1534 nm. Far from the peak, the spectral width is proportional to <span class="html-italic">ν<sub>B</sub></span><sup>2</sup>. The spectral width is proportional to the acoustic attenuation coefficient.</p>
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<p>Example of a BGS tailored to have two acoustic mode interactions of similar strength. The responses of the modes to changes in <span class="html-italic">T</span> or <span class="html-italic">ε</span> differ. This illustration may represent a typical fiber whose temperature or applied strain has been increased in going from the blue to the red curve.</p>
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<p>Example acoustic and refractive index profiles for the equalization of Brillouin gain between the L<sub>01</sub> and higher order acoustic modes. In this configuration, the optical and acoustic boundaries are decoupled [<a href="#B138-applsci-08-01996" class="html-bibr">138</a>].</p>
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31 pages, 14005 KiB  
Article
An Image-Based Fall Detection System for the Elderly
by Kun-Lin Lu and Edward T.-H. Chu
Appl. Sci. 2018, 8(10), 1995; https://doi.org/10.3390/app8101995 - 20 Oct 2018
Cited by 30 | Viewed by 9415
Abstract
Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for [...] Read more.
Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities. Full article
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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<p>System architecture of the Image-based FAll Detection System (IFADS).</p>
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<p>The flow of the state when the person is far from the chair.</p>
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<p>The flow of the state when the person is near or beside the chair.</p>
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<p>The flow of the state when the person is sitting, in progress, or in danger.</p>
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<p>The flow of fall detection when the person’s state is in danger or missing.</p>
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<p>An illustration of the walking cases.</p>
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<p>An illustration of the sitting cases.</p>
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<p>An illustration of the cases with a fall.</p>
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<p>Different rotation angles of falls.</p>
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<p>The results of the common situation test cases.</p>
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<p>The results of the common situation test cases.</p>
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<p>The test results of the effect of colors on IFADS’s accuracy.</p>
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<p>The test results of the effect of colors on IFADS’s accuracy.</p>
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<p>The test results of the effect of the person–chair ratio on IFADS’s accuracy.</p>
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<p>The test results of the effect of the person–chair ratio on IFADS’s accuracy.</p>
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<p>The results of the bench with no seatback test cases.</p>
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<p>An illustration of the squatting cases.</p>
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<p>The results of the squatting test cases.</p>
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<p>An illustration of a common situation and a high-angle shot.</p>
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<p>The results of the high-angle shot test case.</p>
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<p>The results of the video case studies of falls while sitting.</p>
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<p>The results of the video case studies.</p>
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<p>(<b>a</b>) The person is unable to be detected; (<b>b</b>) The person is detected as another object.</p>
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14 pages, 4406 KiB  
Article
Co-Occurrence Network of High-Frequency Words in the Bioinformatics Literature: Structural Characteristics and Evolution
by Taoying Li, Jie Bai, Xue Yang, Qianyu Liu and Yan Chen
Appl. Sci. 2018, 8(10), 1994; https://doi.org/10.3390/app8101994 - 20 Oct 2018
Cited by 24 | Viewed by 7651
Abstract
The subjects of literature are the direct expression of the author’s research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics [...] Read more.
The subjects of literature are the direct expression of the author’s research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics literature and its structural characteristics and evolution were analysed in this paper. First, 242,891 articles from 47 top bioinformatics periodicals were chosen as the object of the study. Second, the co-occurrence relationship among high-frequency words of these articles was analysed by word segmentation and high-frequency word selection. Then, a co-occurrence network of high-frequency words in bioinformatics literature was built. Finally, the conclusions were drawn by analysing its structural characteristics and evolution. The results showed that the co-occurrence network of high-frequency words in the bioinformatics literature was a small-world network with scale-free distribution, rich-club phenomenon and disassortative matching characteristics. At the same time, the high-frequency words used by authors changed little in 2–3 years but varied greatly in four years because of the influence of the state-of-the-art technology. Full article
(This article belongs to the Special Issue Applied Sciences Based on and Related to Computer and Control)
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<p>Process of constructing the co-occurrence network of high-frequency words.</p>
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<p>Co-occurrence network of high-frequency words within the bioinformatics literature (<span class="html-italic">K</span> = 500, <span class="html-italic">E</span> = 200).</p>
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<p>Cumulative distribution of the co-occurrence network of high-frequency words.</p>
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<p>Rich-club coefficient.</p>
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<p>Average degree of the neighbours.</p>
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<p>Changing trends of the co-occurrence network of high-frequency words in the bioinformatics literature from 2013 to 2018 with <span class="html-italic">K</span> = 500, <span class="html-italic">E</span> = 200. <a href="#applsci-08-01994-f006" class="html-fig">Figure 6</a> contained six graphs, which were listed by year: (<b>a</b>) 2013, (<b>b</b>) 2014, (<b>c</b>) 2015, (<b>d</b>) 2016, (<b>e</b>) 2017, and (<b>f</b>) 2018.</p>
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<p>Pseudocode of segmenting words.</p>
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<p>Pseudocode of counting word frequency.</p>
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<p>Pseudocode of obtaining high-frequency words.</p>
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<p>Pseudocode of obtaining high-frequency words.</p>
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<p>Co-occurrence network of high-frequency words within the bioinformatics literature (<span class="html-italic">K</span> = 1000, <span class="html-italic">E</span> = 500).</p>
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15 pages, 5388 KiB  
Article
Characterization of Human Dermal Papilla Cells in Alginate Spheres
by Nanda Maya Mali, Yong-Hee Kim, Jung Min Park, Donghyun Kim, Wook Heo, Buu Le Dao, Jeong Ok Lim and Ji Won Oh
Appl. Sci. 2018, 8(10), 1993; https://doi.org/10.3390/app8101993 - 19 Oct 2018
Cited by 12 | Viewed by 7327
Abstract
Maintenance of trichogenecity of dermal papilla cells (DPCs) have been a problem during cell therapy for androgenic alopecia, as they lose their regenerative potential in in vitro culture. Various spheroid culture techniques are used to increase and maintain trichogenecity of these cells. However, [...] Read more.
Maintenance of trichogenecity of dermal papilla cells (DPCs) have been a problem during cell therapy for androgenic alopecia, as they lose their regenerative potential in in vitro culture. Various spheroid culture techniques are used to increase and maintain trichogenecity of these cells. However, there are some critical drawbacks in these methods. Applying a hydrocell plate for sphere formation or hanging drop methods by hand would be difficult to control the size and cell density inside it. It would be difficult to commercialize or mass production for clinical therapy. In aim to address and overcome these drawbacks, we have introduced alginate sphere. The alginate sphere of DPCs were prepared by electrospinning at different voltages to control the size of sphere. Then the obtained alginate spheres were evaluated for cellular dynamics and density of DPCs under different conditions. In this study, we found that DPCs do not proliferate in alginate sphere. However, the number of DPCs were maintained and found to be in dormant state. Further, the dormant DPCs in the alginate sphere have upregulated DPC signature genes (SOX2, ALPL, WIF1, Noggin, BMP4 and VCAN) and proliferative capacity. Thus, we speculate that alginate sphere environment maintains the dormancy of DPCs with increased trichogenecity. Full article
(This article belongs to the Special Issue Synthesis and Application of Microcapsules)
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<p>The culture characteristics of human DPCs and alginate spheres. (<b>A</b>) Human hair follicle in vivo (red arrow showed dermal papilla in hair bulb region). (<b>B</b>,<b>C</b>) Isolated human dermal papillae in vivo. (<b>D</b>) Five days after cultivation of the human dermal papilla (red arrow-dermal papilla and blue arrow-explant cells from dermal papilla). (<b>E</b>) Passage 3 of human DPCs. (100 ×). (<b>F</b>) Conventional 2D culture state of human DPCs with CMDil. (<b>G</b>) Alginate sphere form of human DPCs with CMDil. (<b>H</b>) Cellular morphology after electrospinning. (<b>I</b>) The maximal size of the alginate spheres with human DPCs. (<b>J</b>) Tear drop shape of alginate with human DPCs. Scale bars in (<b>A</b>–<b>G</b>,<b>J</b>): 100 µm, (<b>H</b>,<b>I</b>) 500 µm.</p>
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<p>Short-term and long-term cultivation of dermal papilla alginate spheres in different sizes. (<b>A</b>–<b>D</b>) Different sizes of alginate spheres with human DPCs. (<b>A</b>) Small size (100–200 µm). (<b>B</b>) Middle size (300–500 µm) (<b>C</b>) Moderate size (500–1000 µm) (<b>D</b>) Large size (more than 1500 µm). (<b>E</b>) Live cell number in different sizes of alginate spheres. (<b>F</b>) The number of total cells included live and dead cells in different size of alginate spheres. (<b>G</b>) The ratio of live cells to total cells in different sizes of alginate spheres. (<b>H</b>) The distribution of cell numbers in different sizes of alginate spheres. (<b>I</b>–<b>K</b>) Live cell number in small-size, middle size and moderate size respectively. (<b>L</b>) Normalized live cell number with the volume of the alginate spheres in different sizes of spheres. (Cell number/mm<sup>3</sup>). Note. *: <span class="html-italic">p</span>-value &lt;0.05, **: <span class="html-italic">p</span>-value &lt;0.01, compared to day 0 (control). Red color: Small size (100–200 µm), green color: Middle size (300–500 µm), Blue color: Moderate size (500–1000 µm). Scale bar: 100 µm.</p>
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<p>Short-term and long-term cultivation of dermal papilla alginate spheres with different cellular densities. (<b>A</b>–<b>D</b>): Different densities of alginate spheres. (<b>A</b>) low density, (<b>B</b>) middle density, (<b>C</b>) moderate density and (<b>D</b>) high density of human DPCs in alginate spheres. (<b>E</b>) The live cell number per alginate sphere at different cellular densities. (<b>F</b>) The total cells included both the live cells and dead cells per alginate sphere at different cellular densities. (<b>G</b>) The ratio of live cells to total cells per alginate sphere at different cellular densities. (<b>H</b>–<b>K</b>): The live cell number with low density (<b>H</b>), middle density (<b>I</b>), moderate density (<b>J</b>), high density (<b>K</b>) of cells in a sphere (Cell number/mm<sup>3</sup>). Note. *: <span class="html-italic">p</span>-value &lt;0.05, **: <span class="html-italic">p</span>-value &lt;0.01. Compared to day 0 (control) as assessed by t-test. Scale bars: A–D: 100 µm.</p>
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<p>Cultivation of explants and cellular convergence rate of dermal papilla alginate spheres. (<b>A</b>–<b>C</b>) Colony forming properties of alginate spheres with human DPCs. (<b>D</b>,<b>E</b>) CMDil trace of cell culture from alginate sphere explants of human DPCs: light microscopy (<b>D</b>) and fluorescent microscopy (<b>E</b>). The subpanel of (<b>D</b>) is tone-modulated to show the diluted cells originated from CMDil retaining cells. The normalized live cell number with the volume of alginate spheres at different cellular densities (<b>F</b>) and different sizes (<b>H</b>). (<b>G</b>) The normalized live cell number with the volume of alginate sphere of different cellular densities at day 70, day 80 and day 90. (<b>I</b>) The normalized live cell number with the volume of alginate spheres under every experimental condition.</p>
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<p>The gene expression level of dermal papilla alginate spheres by qPCR. The human dermal papilla cell marker genes expressed in different passage state and alginate spheres, cultivate in different days. Note. P1, P3, P10: passage state 1, 3 and 10 respectively. D10, D50, D80: post-spherical formation day 10, 50 and 80 respectively.</p>
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<p>The figure showed the human dermal papilla in vivo, <span class="html-italic">in vitro</span> culture (2D) and alginate spheres (3D) and their characteristics of stemness, dormancy and proliferation state.</p>
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22 pages, 7352 KiB  
Article
A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
by YiNa Jeong, SuRak Son, SangSik Lee and ByungKwan Lee
Appl. Sci. 2018, 8(10), 1992; https://doi.org/10.3390/app8101992 - 19 Oct 2018
Cited by 5 | Viewed by 3593
Abstract
This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep [...] Read more.
This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops. Full article
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<p>Structure of the spatially constrained convolutional neural network (SC-CNN).</p>
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<p>The structure of the total crop-diagnosis platform (TCP). CSM: the crop self-diagnosis module; DSM: the data storage module; OFSG: the optimized farm sensor gateway; IPS: an interconnection protocol sub-module; SDFS: a sensor data fragment sub-module.</p>
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<p>Structure of the optimized farm sensor gateway (OFSG).</p>
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<p>Structure of the interconnection protocol sub-module (IPS). PSDU: physical service data unit; CRC: cyclic redundancy check; DSP: dispatch code; HC: header compression; UDP: user datagram protocol; CIF: compressed integration field; FCS: frame check sequence.</p>
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<p>The compression technique of IPv6 over Low power Wireless Personal Area Network (6LoWPAN).</p>
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<p>The fragment using a compressed integration field (CIF).</p>
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<p>The structure of the data storage module (DSM).</p>
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<p>The structure of the agricultural partial diagnosis sub-module (APDS).</p>
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<p>The one example of the six neural network models in the APDS.</p>
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<p>The example of the integrated model in the total environment diagnosis sub-module (TEDS).</p>
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<p>The structure of a neural network model in the TEDS.</p>
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<p>The number of data fragmentation times in the existing gateway.</p>
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<p>Data transmission quantity in the existing gateway.</p>
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<p>The number of data fragmentation times in the OFSG.</p>
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<p>Data transmission quantity in the OFSG.</p>
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<p>The average repeated learning times and the average error rate of neural network models.</p>
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<p>The data accuracy of an activation function.</p>
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<p>Graph of the learning time according to the training samples. CNN: convolutional neural network; LSTM: the long short-term memory models.</p>
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<p>Graph of the diagnosis success rate according to the test samples.</p>
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15 pages, 1545 KiB  
Article
Imaging Material Texture of As-Deposited Selective Laser Melted Parts Using Spatially Resolved Acoustic Spectroscopy
by Rikesh Patel, Matthias Hirsch, Paul Dryburgh, Don Pieris, Samuel Achamfuo-Yeboah, Richard Smith, Roger Light, Steve Sharples, Adam Clare and Matt Clark
Appl. Sci. 2018, 8(10), 1991; https://doi.org/10.3390/app8101991 - 19 Oct 2018
Cited by 34 | Viewed by 6052
Abstract
Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. [...] Read more.
Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects `controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with R a < 0.1 μ m)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of S a 6 μ m, were obtained. Full article
(This article belongs to the Special Issue Laser Ultrasonics)
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Graphical abstract

Graphical abstract
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<p>Schematic of a spatially resolved acoustic spectroscopy (SRAS) system capable of rapidly imaging surface acoustic wave (SAW) velocities on smooth flat surfaces. A grating pattern is imaged onto a sample using a pulsed IR beam, which generates a non-dispersive surface wave packet. The packet contains a frequency (<math display="inline"><semantics> <msub> <mi>f</mi> <mi>s</mi> </msub> </semantics></math>) controlled by the period of the incident pattern (<math display="inline"><semantics> <msub> <mi>λ</mi> <mi>g</mi> </msub> </semantics></math>) and the velocity of the surface wave at the point of generation (<math display="inline"><semantics> <msub> <mi>v</mi> <mi>s</mi> </msub> </semantics></math>)—the velocity is affected by surface grain orientation or defects. The generated wave perturbs a detection beam which is detected by the knife edge detector—the two out-of-phase intensity signals are passed through a differential amplifier and captured using a fast acquisition board or oscilloscope and PC.</p>
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<p>An illustrative laser ultrasound detection outline on a (<b>a</b>) smooth, (<b>b</b>) rough surface. On a smooth surface (<b>a</b>), the small deflection of a singular return beam can be measured using a knife edge detector, however, on a rough surface (<b>b</b>), the change in intensity seen on either photodiode due to the deflection of the speckle field is not detectable. The speckle knife edge detector (SKED) `splits’ the incident speckles into left/right channels across its detector array, allowing it to detect the small deflections in the speckle field.</p>
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<p>Photographs (<b>a</b>) and (<b>b</b>) show the current implementation of the SKED device inside a 35 mm cage plate holder. The length of the device is roughly 30 mm. The render (<b>c</b>) of the SKED chip shows the array of <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> </mrow> </semantics></math> active circuit photodetectors.</p>
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<p>A photograph of the rough-surface SRAS setup showing key component and the laser beam paths. The reflected speckles are captured through a large 50.8 mm lens, with the pattern imaged onto the SKED chip array. Precise control of the detection beam focus is required to ensure the speckles appear larger than two photodetectors on the SKED—this is confirmed by using observing configuration pattern of the SKED device via a USB output.</p>
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<p>(<b>a</b>) Graph showing the signal to noise of a 10 MHz ultrasound wave detected using the knife edge detector and SKED. Measurements were taken on each of the samples shown in (<b>b</b>). The mean signal of 707 points on each sample were used to calculate the SNR. The signal decreases rapidly between 100 nm <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>R</mi> <mi>a</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 200 nm when using the KED to detect, whereas the signal is still observable at <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>a</mi> </msub> <mo>≈</mo> <mn>2</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m.</p>
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<p>A large grain Ti6246 block (<b>a</b>) was scanned using both the smooth surface SRAS setup and rough surface SRAS setup. The values shown in (<b>a</b>) indicates the roughness (in <math display="inline"><semantics> <msub> <mi>R</mi> <mi>a</mi> </msub> </semantics></math>) in that area of the sample. The measured SAW velocity (m/s) of the sample is shown in (<b>b</b>) using the smooth surface setup (<b>c</b>) using the rough surface setup. A box in (<b>b</b>) indicates the area of the sample that were scanned using both systems. Images (<b>d</b>,<b>e</b>) show the maximum signal amplitude (arb. units) detected using the smooth and rough surface setups respectively—both images show a decrease in response in the rougher region (which supports the findings from the previous experiment), however, a measurable signal is still observable when using the rough surface setup in the rough region.</p>
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<p>(<b>a</b>) Photograph of the Ti64 selective laser melting (SLM) sample under inspection, (<b>b</b>) a surface profile map of the SLM sample measured using the focus variation microscope, (<b>c</b>) height parameters measured using the microscope, which includes the highlighted <math display="inline"><semantics> <msub> <mi>S</mi> <mi>a</mi> </msub> </semantics></math> value (∼6 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m).</p>
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<p>(<b>a</b>) Optical micrograph of the Ti64 SLM sample (<b>b</b>) SRAS velocity map of a 3 mm × 5 mm area on the Ti64 SLM sample. The sample was scanned using a step size of 25 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m in both directions, therefore the image produced is oversampled. In areas where a low or no signal is measured, it is coloured grey. Similar features can be seen in both images, which includes surface cavities and the line mark.</p>
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<p>Photographs of the SLM-integrated SRAS system, (<b>a</b>) fully enclosed (class 1 laser safe), (<b>b</b>) plan view of the SRAS setup, with components and beam path indicated, (<b>c</b>) a Ti64 SLM strip under inspection on a large build plate, (<b>d</b>) the signal obtained on the SLM sample on an oscilloscope. Aluminium plates, rubber seals and interlocks were designed into the enclosure as this SLM-SRAS chamber is intended to be a demonstration unit. The optical setup was positioned in a similar area to the actual SLM machine’s optics train. New software had been written to control acquisition and translation. The optics seen in the build chamber (<b>c</b>) were used to monitor the incident light and are not required to perform the SRAS inspection.</p>
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20 pages, 1272 KiB  
Article
Practical Quantum Bit Commitment Protocol Based on Quantum Oblivious Transfer
by Yaqi Song and Li Yang
Appl. Sci. 2018, 8(10), 1990; https://doi.org/10.3390/app8101990 - 19 Oct 2018
Cited by 7 | Viewed by 3496
Abstract
Oblivious transfer (OT) and bit commitment (BC) are two-party cryptographic protocols which play crucial roles in the construction of various cryptographic protocols. We propose three practical quantum cryptographic protocols in this paper. We first construct a practical quantum random oblivious transfer (R-OT) protocol [...] Read more.
Oblivious transfer (OT) and bit commitment (BC) are two-party cryptographic protocols which play crucial roles in the construction of various cryptographic protocols. We propose three practical quantum cryptographic protocols in this paper. We first construct a practical quantum random oblivious transfer (R-OT) protocol based on the fact that non-orthogonal states cannot be reliably distinguished. Then, we construct a fault-tolerant one-out-of-two oblivious transfer ( O T 1 2 ) protocol based on the quantum R-OT protocol. Afterwards, we propose a quantum bit commitment (QBC) protocol which executes the fault-tolerant O T 1 2 several times. Mayers, Lo and Chau (MLC) no-go theorem proves that QBC protocol cannot be unconditionally secure. However, we find that computing the unitary transformation of no-go theorem attack needs so many resources that it is not realistically implementable. We give a definition of physical security for QBC protocols and prove that the practical QBC we proposed is physically secure and can be implemented in the real world. Full article
(This article belongs to the Special Issue Optical High-speed Information Technology)
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<p>The error rate of Protocol 2 changing with the size of sets.</p>
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<p>The probability that an honest Bob gets conclusive bit changing with <math display="inline"><semantics> <mi>μ</mi> </semantics></math>.</p>
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<p>The probability that a malicious Bob gets a conclusive bit changing with <math display="inline"><semantics> <mi>μ</mi> </semantics></math>.</p>
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<p>The difference between an honest Bob’s probability of obtaining a conclusive bit and half of a malicious Bob’s probability of obtaining a conclusive bit changing with <math display="inline"><semantics> <mi>μ</mi> </semantics></math>.</p>
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<p>The solid line denotes the probability of an honest Bob obtains <span class="html-italic">i</span> conclusive bits when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>800</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>. It can be seen that an honest Bob can obtain more than 259 conclusive bits with a great probability. The dashed line denotes the probability of a malicious Bob obtains <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>+</mo> <mn>259</mn> </mrow> </semantics></math> conclusive bits.</p>
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20 pages, 6345 KiB  
Article
Viscosity and Waterproofing Performance Evaluation of Synthetic Polymerized Rubber Gel (SPRG) after Screw Mixing
by Dong Soo Ahn, Kyu Hwan Oh, Jin Sang Park and Sang Keun Oh
Appl. Sci. 2018, 8(10), 1989; https://doi.org/10.3390/app8101989 - 19 Oct 2018
Cited by 7 | Viewed by 4241
Abstract
As opposed to asphalt emulsion waterproofing membrane, Synthetic Rubber Polymer Gel (SPRG) waterproofing materials are not heated prior to installation in concrete structures. SPRG materials are typically required to undergo a screw-mixing process to temporarily reduce the high viscosity and facilitate membrane installation [...] Read more.
As opposed to asphalt emulsion waterproofing membrane, Synthetic Rubber Polymer Gel (SPRG) waterproofing materials are not heated prior to installation in concrete structures. SPRG materials are typically required to undergo a screw-mixing process to temporarily reduce the high viscosity and facilitate membrane installation on a concrete surface. However, there is no standard regulation on the duration of screw-mixing time during SPRG construction. Reported construction cases indicate that SPRG are left under constant screw mixing and are reused after hours or days of rest without being replaced with fresh products. When installed in this condition, SPRGs are subject to waterproofing performance degradation. In this study, SPRG viscosity properties are measured after five different screw-mixing procedures (no screw mixing, 10, 20, 30 and 60 min) and are set to rest in storage (2 h, 1, 2, 3, and 7 days). Specimens prepared under the respective screw mixing and storage times are evaluated for their changes in waterproofing properties through a series of ISO TS 16774 standard evaluation methods. A correlative comparison of the property evaluation results is presented to provide the changes to SPRG property and waterproofing performance. These results are then used to propose a general guideline for selecting optimal screw-mixing time with respect to maintaining adequate waterproofing performance and the viscosity recovery property of SPRG. Full article
(This article belongs to the Section Materials Science and Engineering)
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<p>Common Synthetic polymerized rubber gel injection in a below-grade concrete structure.</p>
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<p>Separate and integrated 2-ply type SPRG waterproofing systems.</p>
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<p>SPRG Gel screw-mixing process prior to application; (<b>a</b>) screw mixing SPRG; (<b>b</b>) reduced viscosity after mixing.</p>
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<p>Polymeric degradation due to continuous shear strain of screw-mixing process.</p>
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<p>Property changes in SPRG following the screw-mixing process; facilitated emulsion breaking following filler particle and globule dispersion due to screw mixing.</p>
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<p>Evaluation procedure flow chart.</p>
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<p>Brookfield viscometer; (<b>a</b>) apparatus; (<b>b</b>) measuring SPRG viscosity.</p>
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<p>Test method apparatus; (<b>a</b>) specimen placement; (<b>b</b>) water flow testing.</p>
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<p>Test method for adhesion on wet substrate; (<b>a</b>) specimen placement; (<b>b</b>) water flow testing.</p>
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<p>Hydrostatic pressure testing illustration.</p>
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<p>Illustration of substrate movement resistance testing.</p>
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<p>SPRG viscosity change over different storage periods based on respective screw-mixing time.</p>
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<p>Radar charts for comparative evaluation of overall waterproofing performance; (<b>a</b>) results for no screw mixing; (<b>b</b>) results for 10 min of screw mixing; (<b>c</b>) results for 20 min of screw mixing; (<b>d</b>) results for 30 min of screw mixing; (<b>e</b>) results for 60 min of screw mixing.</p>
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20 pages, 7511 KiB  
Article
Investigation on Coal Fragmentation by High-Velocity Water Jet in Drilling: Size Distributions and Fractal Characteristics
by Songqiang Xiao, Zhaolong Ge, Yiyu Lu, Zhe Zhou, Qian Li and Lei Wang
Appl. Sci. 2018, 8(10), 1988; https://doi.org/10.3390/app8101988 - 19 Oct 2018
Cited by 23 | Viewed by 3985
Abstract
Water jet drilling (WJD) technology is a highly efficient method to extract coalbed methane from reservoirs with low permeability. It is crucial to efficiently remove the coal fragments while drilling. In this study, to disclose coal fragmentation features and size distributions under water [...] Read more.
Water jet drilling (WJD) technology is a highly efficient method to extract coalbed methane from reservoirs with low permeability. It is crucial to efficiently remove the coal fragments while drilling. In this study, to disclose coal fragmentation features and size distributions under water jet impact in drilling, the image processing method was utilized to obtain the geometric dimensions of coal fragments. The size distributions, morphologies and fractal characteristics of coal fragmentation were studied based on generalized extreme value distribution and fractal theory. The effects of the jet impact velocity and coal strength on the fragmentation features were analyzed. The results show that fine particles dominate the coal fragments in WJD for coal seams with various strengths. In experiments conducted at the Fengchun coal mine, owing to the higher coal strength of the M7 coal seam, the fragmentation degree of coal subjected to water jets during WJD is lower in the M7 coal steam than in the M8 coal seam, which results in a large dominant fragment size and small fractal dimension under the same impact energy. It was found that the higher the jet impact velocity is, the higher the quantity of fragments generated from WJD and the smaller the particle size. The NUM-based cumulative probability distribution curves of coal fragments are more intensive in the range of relatively small particle sizes and then become sparser with the increase in particle size. When the impact velocity increases, (i) the size distribution curves move toward smaller particle sizes, and the dominant fragment size decreases; (ii) the shape (major axis/minor axis) of coal fragments move toward the upper left, and the curve shape for a high impact velocity attains unity more quickly; and (iii) the fractal dimension value increases linearly. In addition, the fractal dimensions are obviously affected by the dominant fragment size; they increase with the decrease in the dominant fragment size. This study can provide a basis for further research on coal fragment transportation in WJD and parameter selection for discharging coal fragments during drilling for CBM development. Full article
(This article belongs to the Special Issue Green Energy and Applications)
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<p>Schematic of WJD system in underground coal mine.</p>
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<p>Water-jet bits used in field experiment: (<b>a</b>) schematic diagram of nozzle layout in the water-jet bit, and (<b>b</b>) physical image of the bits.</p>
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<p>Field experimental site of WJD at the Fengchun coal mine: (<b>a</b>) Field experimental site of WJD and the geological histogram at the +300N1 rock crosscut; (<b>b</b>) Sketch map of field experimental site; (<b>c</b>) Target coal seams for WJD.</p>
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<p>Determination of fragment size and shape distributions using the image processing method. (<b>a</b>) Coal fragment photographs of each size degree, and (<b>b</b>) binary images.</p>
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<p>Minimum enclosing rectangles of coal fragments with different particle sizes: (<b>a</b>) 6.0–12.0 mm, and (<b>b</b>) &gt;12.0 mm.</p>
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<p>Fitting curves of the cumulative probability of coal fragments based on the GEV distribution function and Weibull distribution function (impact velocity = 253 m/s). (<b>a</b>) The fitting curves of all the particle sizes, (<b>b</b>) and (<b>c</b>) is the partial magnification of fitting curves for the large and small particle sizes, respectively.</p>
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<p>Experimental data of NUM-based cumulative probability for the equivalent diameter of coal fragments and fitted curves. (<b>a</b>) Size distributions of coal fragments under different jet impact velocities in the M8 coal seam. (<b>b</b>) Fragment size distributions of the various coal seams under a jet velocity of 268 m/s each.</p>
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<p>Distribution density of the equivalent diameter of coal fragments for various jet velocities and coal mass strengths. (<b>a</b>) Different jet velocities in the M8 coal seam. (<b>b</b>) Different strengths of coal seams at the same jet velocity of 268 m/s.</p>
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<p>Relationship between the dominant fragment size and the jet impact velocity.</p>
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<p>NUM-based cumulative probability for the shape of coal fragments. (<b>a</b>) Shape distributions of coal fragments under different jet impact velocities in the M8 coal seam. (<b>b</b>) Shape distributions of the various coal seams under the same jet velocity of 268 m/s.</p>
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<p>NUM-based cumulative probability for the shape of coal fragments at different sizes.</p>
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<p>Diagrams of the relative mass versus the relative fragment size for coal fragments generated from WJD holes in coal seams: (<b>a</b>) M8 coal seam and (<b>b</b>) M7 and M8 coal seams for an impact velocity of 268 m/s.</p>
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<p>Relationship of the fractal dimension and jet impact velocity.</p>
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<p>(<b>a</b>) Different failure patterns observed inside the rock samples and the failure mechanisms of rock under water jet impact: (<b>b</b>) shock stress wave effect, (<b>c</b>) pressured water wedge effect, and (<b>d</b>) water flow scouring effect [<a href="#B52-applsci-08-01988" class="html-bibr">52</a>,<a href="#B53-applsci-08-01988" class="html-bibr">53</a>,<a href="#B54-applsci-08-01988" class="html-bibr">54</a>,<a href="#B55-applsci-08-01988" class="html-bibr">55</a>].</p>
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<p>Schematic diagram of cleats and fracture structure in coal and rock mass.</p>
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18 pages, 3410 KiB  
Article
Evaluation of a Smart System for the Optimization of Logistics Performance of a Pruning Biomass Value Chain
by Techane Bosona, Girma Gebresenbet, Sven-Olof Olsson, Daniel Garcia and Sonja Germer
Appl. Sci. 2018, 8(10), 1987; https://doi.org/10.3390/app8101987 - 19 Oct 2018
Cited by 5 | Viewed by 4371
Abstract
The paper presents a report on the performance evaluation of a newly developed smart logistics system (SLS). Field tests were conducted in Spain, Germany, and Sweden. The evaluation focused on the performance of a smart box tool (used to capture information during biomass [...] Read more.
The paper presents a report on the performance evaluation of a newly developed smart logistics system (SLS). Field tests were conducted in Spain, Germany, and Sweden. The evaluation focused on the performance of a smart box tool (used to capture information during biomass transport) and a web-based information platform (used to monitor the flow of agricultural pruning from farms to end users and associated information flow). The tests were performed following a product usability testing approach, considering both qualitative and quantitative parameters. The detailed performance evaluation included the following: systematic analysis of 41 recordable parameters (stored in a spreadsheet database), analysis of feedback and problems encountered during the tests, and overall quality analysis applying the product quality model adapted from ISO/IEC FDIS 9126-1 standard. The data recording and storage and the capability to support product traceability and supply chain management were found to be very satisfactory, while assembly of smart box components (mainly the associated cables), data transferring intervals, and manageability could be improved. From the data retrieved during test activities, in more than 95% of the parameters within 41 columns, the expected values were displayed correctly. Some errors were observed, which might have been caused mainly by barriers that could hinder proper data recording and transfer from the smart box to the central database. These problems can be counteracted and the performance of the SLS can be improved so that it can be upgraded to be a marketable tool that can promote sustainable biomass-to-energy value chains. Full article
(This article belongs to the Special Issue Sustainable Energy Systems Planning, Integration and Management)
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<p>Typical pruning biomass logistics chain.</p>
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<p>Different stages of logistics chain in the process of biomass-to-energy conversion.</p>
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<p>Major components of the smart logistics system. Reproduced from permission of [<a href="#B2-applsci-08-01987" class="html-bibr">2</a>] (MDPI, 2018).</p>
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<p>Product quality model for internal and external quality assessment (adapted with modification from ISO/IEC FDIS 9126-1).</p>
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<p>(<b>a</b>) Smart Box assembly: (1) smart box protected with metal case; (2) GPRS/GSM antenna; (3) Global Positioning System (GPS) antenna; (4) temperature and humidity measuring sensor probe; (5) power cable. (<b>b</b>) Power<sup>TM</sup> 9500 scanner with its components.</p>
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<p>Visualization of smart box reading link indicating its Cargolog serial number.</p>
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<p>Visualization of measured parameter values (temperature and humidity as recorded on 21 December 2015, from 08:29:14 to 11:32:02 and displayed as a table).</p>
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<p>Delivery route based on GPS coordinates recorded by the smart box.</p>
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<p>Example of smart box reading visualization: graphic presentation of measured parameters (along with displayed values of temperature, humidity, and GPS coordinates as recorded on 21 December 2015, from 08:29:14 to 11:32:02).</p>
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19 pages, 5168 KiB  
Article
Detectability of Delamination in Concrete Structure Using Active Infrared Thermography in Terms of Signal-to-Noise Ratio
by Jungwon Huh, Van Ha Mac, Quang Huy Tran, Ki-Yeol Lee, Jong-In Lee and Choonghyun Kang
Appl. Sci. 2018, 8(10), 1986; https://doi.org/10.3390/app8101986 - 19 Oct 2018
Cited by 22 | Viewed by 6856
Abstract
Detecting subsurface delamination is a difficult and vital task to maintain the durability and serviceability of concrete structure for its whole life cycle. The aim of this work was to obtain better knowledge of the effect of depth, heating time, and rebar on [...] Read more.
Detecting subsurface delamination is a difficult and vital task to maintain the durability and serviceability of concrete structure for its whole life cycle. The aim of this work was to obtain better knowledge of the effect of depth, heating time, and rebar on the detectability capacity of delamination. Experimental tests were carried out on a concrete specimen in the laboratory using Long Pulsed Thermography (LPT). Six halogen lamps and a long wavelength infrared camera with a focal plane array of 640 × 480 pixels were used as the heat source and infrared detector, respectively. The study focused on the embedded imitation delaminations with the size of 10 cm × 10 cm × 1 cm, located at depths varying from 1 to 8 cm. The signal-to-noise ratio (SNR) was applied as a criterion to assess the detectability of delamination. The results of this study indicate that as the provided heating time climbed, the SNR increased, and the defect could be identified more clearly. On the other hand, when using the same heating regime, a shallow delamination displayed a higher SNR than a deeper one. The moderate fall of the SNR in the case of imitating defect located below reinforced steel was also observed. The absolute contrast was monitored to determine the observation time, and the nondimensional prefactor k was empirically proposed to predict the depth of delamination. The mean absolute percentage error (MAPE) was used to quantitatively evaluate the difference between forecasted and real depth, which evaluation confirmed the high reliability of the estimated value of the prefactor k. Full article
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<p>Location of delamination in concrete structures.</p>
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<p>The principle of infrared thermography.</p>
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<p>Arrangement of artificial delaminations inside the concrete specimen.</p>
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<p>System of infrared thermography and equipment: (<b>a</b>) System of infrared thermography; (<b>b</b>) FLIR SC660 IR Camera (FLIR, Wilsonville, OR, USA); and (<b>c</b>) Kestrel 3000 (Nielsen-Kellerman, Chester, PA, USA).</p>
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<p>Selection of <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mo>−</mo> </msub> <mi>a</mi> <mi>r</mi> <mi>e</mi> <mi>a</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>N</mi> <mo>−</mo> </msub> <mi>a</mi> <mi>r</mi> <mi>e</mi> <mi>a</mi> </mrow> </semantics> </math> for signal-to-noise ratio (SNR) calculation.</p>
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<p>The heat transfer mechanism using halogen lamps.</p>
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<p>Absolute contrast curve in the case of 20 min heating for delamination at 2 cm depth.</p>
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<p>The test results at 1000 s after turning off the heat source in the case of 30-min heating: (<b>a</b>) thermal image and (<b>b</b>) the surface temperature profile of the line 1–1.</p>
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<p>Partition of the undetectability and detectability of delaminations in the tests.</p>
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<p>SNR for delaminations with different depths.</p>
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<p>SNR during cooling time for the depth from (1 to 4) cm in the case of 30-min heating.</p>
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<p>The SNR for delaminations under different heating times.</p>
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<p>The SNR during both cooling time and heating time for 2 cm of depth under 2 to 15 min heating.</p>
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<p>The relationship between the depth and observation time.</p>
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<p>The relationship between <math display="inline"> <semantics> <mrow> <msqrt> <mrow> <mi>α</mi> <msub> <mi>T</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </msqrt> </mrow> </semantics> </math> and the depth (Z) in the case of <span class="html-italic">α</span> = 7.5 × 10<sup>−7</sup> m<sup>2</sup> s<sup>−1</sup>.</p>
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<p>Comparison between the real and predicted depths of delaminations in the case of <span class="html-italic">α</span> = 7.5 × 10<sup>−7</sup> m<sup>2</sup> s<sup>−1</sup>.</p>
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<p>Comparison of the SNR of cases without and with steel bars.</p>
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26 pages, 7619 KiB  
Article
Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures
by Yoshihiro Maeda, Norishige Fukushima and Hiroshi Matsuo
Appl. Sci. 2018, 8(10), 1985; https://doi.org/10.3390/app8101985 - 19 Oct 2018
Cited by 20 | Viewed by 6135
Abstract
In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving [...] Read more.
In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing. Full article
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<p>Occurrence status of denormalized numbers: (<b>a</b>) original image; (<b>b</b>) bilateral filter; (<b>c</b>) non-local means filter; (<b>d</b>) Gaussian range filter; (<b>e</b>) bilateral non-local means filter. (<b>b</b>–<b>e</b>) present heat maps of the occurrence frequency of denormalized numbers in each kernel. The filtering parameters are as follows: <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <msqrt> <mn>2</mn> </msqrt> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>. The template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>, and the search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mi>r</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mi>r</mi> <mo>+</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The image size is 768 × 512. In (<b>b</b>–<b>e</b>), the ratios of denormalized numbers in all weight calculations are 2.11%, 3.26%, 1.97% and 3.32%, respectively.</p>
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<p>Set and gather instructions: (<b>a</b>) set instruction; (<b>b</b>) gather instruction.</p>
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<p>Computational time of the bilateral filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The computational times are shown in terms of single precision (32F) and double precision (64F) floating point numbers. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Speedup ratio of bilateral filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The speedup ratio is shown regarding single precision (32F) and double precision (64F) floating point numbers. If the ratio exceeds one, all implementation of the method are faster than the straightforward implementation (none). <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Computational time of Gaussian range filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The computational times are shown in terms of single precision (32F) and double precision (64F) floating point numbers. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Speedup ratio of Gaussian range filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The speedup ratio is shown in single precision (32F) and double precision (64F) floating point numbers. If the ratio exceeds one, all implementation of the method are faster than the straightforward implementation (none). <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Computational time of non-local means filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The computational times are shown in terms of single precision (32F) and double precision (64F) floating point numbers. <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>, template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>, and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>37</mn> <mo>,</mo> <mn>37</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Speedup ratio of non-local means filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The speedup ratio is shown regarding single precision (32F) and double precision (64F) floating point numbers. If the ratio exceeds one, all implementation of the method are faster than the straightforward implementation (none). <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>; template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>, and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>37</mn> <mo>,</mo> <mn>37</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Computational time of bilateral non-local means filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The computational times are shown in terms of single precision (32F) and double precision (64F) floating point numbers. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>; template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>, and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo>×</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>×</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>+</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Speedup ratio of bilateral non-local means filter on Intel Core i9 7980XE: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX512. The speedup ratio is shown regarding single precision (32F) and double precision (64F) floating point numbers. If the ratio exceeds one, all implementation of the method are faster than the straightforward implementation (none). <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>, template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>, and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo>×</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>×</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>+</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>PSNRs of the bilateral filter, Gaussian range filter, non-local means filter and bilateral non-local means filter: (<b>a</b>) bilateral filter; (<b>b</b>) Gaussian range filter; (<b>c</b>) non-local means filter; (<b>d</b>) bilateral non-local means filter. Note that the maximal value in (<b>a</b>–<b>d</b>) is infinity.</p>
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<p>Computational time of the bilateral filter in various CPU microarchitectures: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX/AVX2 with FMA3; (<b>d</b>) AVX512; (<b>e</b>) AVX512 with FMA3. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Computational time of non-local means filter in various CPU microarchitectures: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX/AVX2 with FMA3; (<b>d</b>) AVX512; (<b>e</b>) AVX512 with FMA3. <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>; template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>; and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>37</mn> <mo>,</mo> <mn>37</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Speedup ratio of bilateral filter in various CPU microarchitectures: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX/AVX2 with FMA3; (<b>d</b>) AVX512; (<b>e</b>) AVX512 with FMA3. If the ratio exceeds one, the implementation is faster than a scalar implementation for all CPU microarchitectures. Note that the scalar implementation is parallelized using multi-core. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Speedup ratio of non-local means filter in various CPU microarchitectures: (<b>a</b>) SSE; (<b>b</b>) AVX/AVX2; (<b>c</b>) AVX/AVX2 with FMA3; (<b>d</b>) AVX512; (<b>e</b>) AVX512 with FMA3. If the ratio exceeds one, the implementation is faster than a scalar implementation for each CPU microarchitecture. Note that the scalar implementation is parallelized using multi-core. <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>; template window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>; and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>37</mn> <mo>,</mo> <mn>37</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>PSNR vs. computational time in quantization range/merged LUT; (<b>a</b>) bilateral filter; (<b>b</b>) non-local mean filter (template window size <math display="inline"><semantics> <mrow> <mo>=</mo> <mo stretchy="false">(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>); (<b>c</b>) non-local mean filter (template window size <math display="inline"><semantics> <mrow> <mo>=</mo> <mo stretchy="false">(</mo> <mn>5</mn> <mo>,</mo> <mn>5</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>). In sqrt and div, the quantization function is square root and division, respectively. These results were obtained on an Intel Core i9 7980XE. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>4</mn> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>; and search window size is <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mn>37</mn> <mo>,</mo> <mn>37</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Image size is 768 × 512.</p>
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<p>Computational time when using unsigned char (8U) and single precision floating point number (32F) with respect to kernel radius: (<b>a</b>) bilateral filter; (<b>b</b>) non-local means filter. Note that the computational time is plotted on the logarithmic scale. These results were obtained on an Intel Core i9 7980XE. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>; and image size is 768 × 512.</p>
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<p>Speedup ratios of various proposed implementation approaches and that of scalar implementation: (<b>a</b>) bilateral filter; (<b>b</b>) non-local means filter. The types of implementation considered herein are parallelized using multi-cores. These results were obtained on an Intel Core i9 7980XE. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> </mrow> </semantics></math>; and image size is 768 × 512.</p>
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<p>Computational time and speedup ratio of fastest implementation and OpenCV implementation in bilateral filter. These results were calculated using an Intel Core i9 7980XE. Note that the computational time is plotted on the logarithmic scale. If the speedup ratio exceeds one, the fastest implementation is faster than the OpenCV implementation. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> <msub> <mi>σ</mi> <mi>s</mi> </msub> </mrow> </semantics></math>; and image size is 768 × 512. For <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, the PSNRs of the proposed method and OpenCV are 84.63 dB and 44.08 dB, respectively. For <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, they are 85.45 dB and 43.55 dB, respectively. For <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>, they are 84.41 dB and 43.19 dB, respectively.</p>
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10 pages, 4454 KiB  
Article
Thermoresponsive Behavior of Magnetic Nanoparticle Complexed pNIPAm-co-AAc Microgels
by Su-Kyoung Lee, Yongdoo Park and Jongseong Kim
Appl. Sci. 2018, 8(10), 1984; https://doi.org/10.3390/app8101984 - 19 Oct 2018
Cited by 9 | Viewed by 6106
Abstract
Characterization of responsive hydrogels and their enhancement with novel moieties have improved our understanding of functional materials. Hydrogels coupled with inorganic nanoparticles have been sought for novel types of responsive materials, but the efficient routes for the formation and the responsivity of complexed [...] Read more.
Characterization of responsive hydrogels and their enhancement with novel moieties have improved our understanding of functional materials. Hydrogels coupled with inorganic nanoparticles have been sought for novel types of responsive materials, but the efficient routes for the formation and the responsivity of complexed materials remain for further investigation. Here, we report that responsive poly(N-isopropylacrylamide-co-acrylic acid) (pNIPAm-co-AAc) hydrogel microparticles (microgels) are tunable by varying composition of co-monomer and crosslinker as well as by their complexation with magnetic nanoparticles in aqueous dispersions. Our results show that the hydrodynamic diameter and thermoresponsivity of microgels are closely related with the composition of anionic co-monomer, AAc and crosslinker, N,N′-Methylenebisacrylamide (BIS). As a composition of hydrogels, the higher AAc increases the swelling size of the microgels and the volume phase transition temperature (VPTT), but the higher BIS decreases the size with no apparent effect on the VPTT. When the anionic microgels are complexed with amine-modified magnetic nanoparticles (aMNP) via electrostatic interaction, the microgels decrease in diameter at 25 °C and shift the volume phase transition temperature (VPTT) to a higher temperature. Hysteresis on the thermoresponsive behavior of microgels is also measured to validate the utility of aMNP-microgel complexation. These results suggest a simple, yet valuable route for development of advanced responsive microgels, which hints at the formation of soft nanomaterials enhanced by inorganic nanoparticles. Full article
(This article belongs to the Special Issue Nanocomposite Hydrogels for Biomedical Applications)
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Graphical abstract

Graphical abstract
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<p>TEM image of Zn<sub>0.4</sub>Fe<sub>2.6</sub>O<sub>4</sub> nanoparticles (<b>left panel</b>) and silica coated Zn<sub>0.4</sub>Fe<sub>2.6</sub>O<sub>4</sub> nanoparticles (<b>right panel</b>). The average sizes of the nanoparticles are 13 nm ± 1.1 nm and 21 nm ± 1.5 nm, respectively.</p>
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<p>pNIPAm-<span class="html-italic">co</span>-AAc microgel (<b>a</b>) and its thermoresponsive behavior (<b>b</b>). Amine-functionalized magnetic nanoparticle (aMNP) enhanced microgel via electrostatic coupling (<b>c</b>) and its temperature dependent volume change (<b>d</b>).</p>
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<p>TEM images of aMNP complexed pNIPAm-<span class="html-italic">co</span>-AAc microgel. Low magnification (<b>left</b>) and high magnification (<b>right</b>). Note that dark circles represent aMNP incorporated to microgels.</p>
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<p>The zeta potential value and the hydrodynamic size for 10% AAc (acrylic acid), 2% BIS (<span class="html-italic">N</span>,<span class="html-italic">N</span>′-Methylenebisacrylamide) microgels upon 0.6, 1.2, 1.8, and 2.4 μg of aMNP additions. Note that zeta potential values of aMNP and microgels were measured as 19 mV and −18 mV, respectively.</p>
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<p>Hydrodynamic diameter of 5% (□), 10% (○), and 20% (△) AAc (<b>a</b>) or 0% (□), 2% (○), and 5% (△) BIS microgels (<b>b</b>) upon the addition of 0.6 μg of aMNP aliquots. The hydrodynamic diameter of microgels measured by dynamic light scattering (DLS) at 25 °C.</p>
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<p>The hydrodynamic diameter (nm) of 5% (□), 10% (○), and 20% (△) AAc microgels without (<b>a</b>) and with (<b>b</b>) aMNP complexation and 0% (□), 2% (○), and 5% (△) BIS microgels without (<b>c</b>) and with (<b>d</b>) aMNP complexation at 25 °C and 50 °C in cooling and heating cycles, respectively. Note that each measurement was performed after 15 min of equilibration time at the cycles.</p>
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<p>The hydrodynamic diameter (nm) of 5% (□), 10% (○), and 20% (△) AAc microgels without (<b>a</b>) and with (<b>b</b>) aMNP complexation and 0% (□), 2% (○), and 5% (△) BIS microgels without (<b>c</b>) and with (<b>d</b>) aMNP complexation in temperature ramp from 25 to 55 °C at one degree intervals.</p>
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16 pages, 2406 KiB  
Article
Modeling Power Generation and Energy Efficiencies in Air-Cathode Microbial Fuel Cells Based on Freter Equations
by Hongjian Lin, Sarah (Xiao) Wu and Jun Zhu
Appl. Sci. 2018, 8(10), 1983; https://doi.org/10.3390/app8101983 - 19 Oct 2018
Cited by 13 | Viewed by 3497
Abstract
The model proposed in this study was based on the assumption that the biomass attached to the anode served as biocatalysts for microbial fuel cell (MFC) exoelectrogenesis, and this catalytic effect was quantified by the exchange current density of anode. By modifying the [...] Read more.
The model proposed in this study was based on the assumption that the biomass attached to the anode served as biocatalysts for microbial fuel cell (MFC) exoelectrogenesis, and this catalytic effect was quantified by the exchange current density of anode. By modifying the Freter model and combining it with the Butler–Volmer equation, this model could adequately describe the processes of electricity generation, substrate utilization, and the suspended and attached biomass concentrations, at both batch and continuous operating modes. MFC performance is affected by the operating variables such as initial substrate concentration, external resistor, influent substrate concentration, and dilution rate, and these variables were revealed to have complex interactions by data simulation. The external power generation and energy efficiency were considered as indices for MFC performance. The simulated results explained that an intermediate initial substrate concentration (about 100 mg/L under this reactor configuration) needed to be chosen to achieve maximum overall energy efficiency from substrate in the batch mode. An external resistor with the value approximately that of the internal resistance, boosted the power generation, and a resistor with several times of that of the internal resistance achieved better overall energy efficiency. At continuous mode, dilution rate significantly impacted the steady-state substrate concentration level (thus substrate removal efficiency and rate), and attached biomass could be fully developed when the influent substrate concentration was equal to or higher than 100 mg/L at any dilution rate of the tested range. Overall, this relatively simple model provided a convenient way for evaluating and optimizing the performance of MFC reactors by regulating operating parameters. Full article
(This article belongs to the Special Issue Microbial Fuel Cells)
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Figure 1
<p>A schematic of organic substrate and energy flow in a microbial fuel cell.</p>
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<p>Electrochemical parameters estimation and validation; (<b>A</b>) the electrode potential during polarization experiment; (<b>B</b>,<b>C</b>) model validation for experimental data from another identical microbial fuel cell (MFC) reactor (<b>B</b> for <span class="html-italic">U<sub>ext</sub></span>, and <b>C</b> for <span class="html-italic">P<sub>ext</sub></span>); and (<b>D</b>) comparison of the simulated and experimental <span class="html-italic">U<sub>ext</sub></span> during inoculation stage.</p>
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<p>Simulated time-course profiles of process variables at different initial substrate concentration. (<b>A</b>) the substrate concentration; (<b>B</b>) suspended biomass concentration; (<b>C</b>) attached biomass concentration, and (<b>D</b>) the external voltage <span class="html-italic">U<sub>ext</sub></span>.</p>
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<p>Simulated polarization curves at different moments of inoculation. Attached bacteria concentration reached a plateau value after about 2 d, and the polarization curves did not change much thereafter.</p>
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<p>Simulated batch performance of MFC with 100 mg L<sup>−1</sup> initial substrate at different external resistors. (<b>A</b>) substrate concentration; (<b>B</b>) suspended biomass concentration; (<b>C</b>) external voltage; and (<b>D</b>) power generation.</p>
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<p>Process variables and MFC performance predicted at steady state under different dilution rates and influent substrate concentrations. (<b>A</b>) steady state substrate concentration; (<b>B</b>) suspended biomass concentration; (<b>C</b>) attached biomass concentration, and (<b>D</b>) the overall energy efficiency <span class="html-italic">η<sub>ext</sub></span>.</p>
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<p>Simulated MFC performance at steady state under different electrical currents (or external resistors), dilution rates and influent substrate concentrations. (<b>A</b>) steady state power density; (<b>B</b>) MFC cell energy efficiency; (<b>C</b>) overall energy efficiency at <span class="html-italic">S<sub>in</sub></span> = 100 mg L<sup>−1</sup>; and (<b>D</b>) <span class="html-italic">S<sub>in</sub></span> = 2400 mg L<sup>−1</sup>; (<b>E</b>) effluent substrate concentration at <span class="html-italic">S<sub>in</sub></span> = 100 mg L<sup>−1</sup>; and (<b>F</b>) <span class="html-italic">S<sub>in</sub></span> = 2400 mg L<sup>−1</sup>.</p>
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26 pages, 10071 KiB  
Article
Aerodynamic Force and Comprehensive Mechanical Performance of a Large Wind Turbine during a Typhoon Based on WRF/CFD Nesting
by Shitang Ke, Wenlin Yu, Jiufa Cao and Tongguang Wang
Appl. Sci. 2018, 8(10), 1982; https://doi.org/10.3390/app8101982 - 19 Oct 2018
Cited by 16 | Viewed by 4281
Abstract
Compared with normal wind, typhoons may change the flow field surrounding wind turbines, thus influencing their wind-induced responses and stability. The existing typhoon theoretical model in the civil engineering field is too simplified. To address this problem, the WRF (Weather Research Forecasting) model [...] Read more.
Compared with normal wind, typhoons may change the flow field surrounding wind turbines, thus influencing their wind-induced responses and stability. The existing typhoon theoretical model in the civil engineering field is too simplified. To address this problem, the WRF (Weather Research Forecasting) model was introduced for high-resolution simulation of the Typhoon “Nuri” firstly. Secondly, the typhoon field was analyzed, and the wind speed profile of the boundary layer was fitted. Meanwhile, the normal wind speed profile with the same wind speed of the typhoon speed profile at the gradient height of class B landform in the code was set. These two wind speed profiles were integrated into the UDF (User Defined Function). On this basis, a five-MW wind turbine in Shenzhen was chosen as the research object. The action mechanism of speed was streamlined and turbulence energy surrounding the wind turbine was disclosed by microscale CFD (Computational Fluid Dynamics) simulation. The influencing laws of a typhoon and normal wind on wind pressure distribution were compared. Finally, key attention was paid to analyzing the structural response, buckling stability, and ultimate bearing capacity of the wind turbine system. The research results demonstrated that typhoons increased the aerodynamic force and structural responses, and decreased the overall buckling stability and ultimate bearing capacity of the wind turbine. Full article
(This article belongs to the Special Issue Wind Turbine Aerodynamics)
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Figure 1

Figure 1
<p>Meshing of the Weather Research Forecasting (WRF) model. (<b>a</b>) Horizontal grids (space between coarse and thin grids is 3:1); (<b>b</b>) Vertical grids.</p>
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<p>Simulation domain of the Typhoon “Nuri”.</p>
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<p>Typhoon path and minimum sea level pressure throughout the simulation. (<b>a</b>) Typhoon path; (<b>b</b>) Minimum sea level pressure.</p>
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<p>Simulation results for landing of typhoon. (<b>a</b>) Air pressure nephogram; (<b>b</b>) Wind speed nephogram; (<b>c</b>) Temperature nephogram; (<b>d</b>) Rainfall nephogram.</p>
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<p>Measured results for landing of typhoon. (<b>a</b>) Air pressure nephogram; (<b>b</b>) Wind speed nephogram; (<b>c</b>) Temperature nephogram; (<b>d</b>) Rainfall nephogram.</p>
Full article ">Figure 5 Cont.
<p>Measured results for landing of typhoon. (<b>a</b>) Air pressure nephogram; (<b>b</b>) Wind speed nephogram; (<b>c</b>) Temperature nephogram; (<b>d</b>) Rainfall nephogram.</p>
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<p>Wind speed streamlines before, at, and after landing of the typhoon. (<b>a</b>) Before landing (14:00 on 22 August); (<b>b</b>) At landing (20:00 on 22 August); (<b>c</b>) After landing (02:00 on 23 August).</p>
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<p>Wind speed streamlines before, at, and after landing of the typhoon. (<b>a</b>) Before landing (14:00 on 22 August); (<b>b</b>) At landing (20:00 on 22 August); (<b>c</b>) After landing (02:00 on 23 August).</p>
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<p>Wind speed profile close to the typhoon center and near-ground wind speed and fitting curves in the center of the simulation region at different moments. (<b>a</b>) Wind speed profile close to the typhoon center at different moments; (<b>b</b>) Near-ground wind speed and fitting curves in the center of the simulation region.</p>
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<p>Computational domain and meshing schemes. (<b>a</b>) Meshing of the overall computational domain; (<b>b</b>) Meshing of local computational domain; (<b>c</b>) Meshing of the <span class="html-italic">x</span>-<span class="html-italic">y</span> plane; (<b>d</b>) Meshing of the encrypted <span class="html-italic">y</span>-<span class="html-italic">z</span> plane.</p>
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<p>Boundary conditions of the computational domain.</p>
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<p>Procedures of WRF-CFD (Computational Fluid Dynamics) computation and nesting.</p>
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<p>Numerical simulation results under normal wind conditions versus value specified by Chinese code.</p>
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<p>Wind speed streamlines at a typical section of the tower body under normal wind and typhoon loads. (<b>a</b>) Normal wind (0.3H); (<b>b</b>) Typhoon (0.3H); (<b>c</b>) Normal wind (0.8H); (<b>d</b>) Typhoon (0.8H).</p>
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<p>Turbulence energy distribution at a typical section of the tower body under normal wind and typhoon loads. (<b>a</b>) Normal wind (0.3H); (<b>b</b>) Typhoon (0.3H); (<b>c</b>) Normal wind (0.8H); (<b>d</b>) Typhoon (0.8H).</p>
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<p>Distribution curves of wind pressure coefficient on tower body under normal wind and typhoon loads. (<b>a</b>) Normal wind; (<b>b</b>) Typhoon.</p>
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<p>Wind pressure coefficient nephograms on the windward and leeside of blades as well as the mean along the blade under normal wind and typhoon loads. (<b>a</b>) Wind pressure coefficient nephogram on the windward side; (<b>b</b>) Wind pressure coefficient curves on the windward side; (<b>c</b>) Wind pressure coefficient nephogram on the leeside; (<b>d</b>) Wind pressure coefficient curves on the leeside.</p>
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<p>Distribution curves of overall wind pressure coefficient on blades under normal wind and typhoon loads. (<b>a</b>) Normal wind; (<b>b</b>) Typhoon.</p>
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<p>Distribution curves of lift coefficient and drag coefficient of the tower body under normal wind and typhoon loads. (<b>a</b>) Lift coefficient. (<b>b</b>) Drag coefficient.</p>
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<p>Finite element model of wind turbine and the first 100 orders of inherent frequency. (<b>a</b>) Finite element model; (<b>b</b>) First 100 orders of inherent frequency.</p>
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<p>Mode of vibration of the tower–blade coupling model at different orders. (<b>a</b>) First order; (<b>b</b>) Fifth order; (<b>c</b>) 10th order; (<b>d</b>) 30th order; (<b>e</b>) 50th order; (<b>f</b>) 100th order.</p>
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<p>3D radial displacement of tower body under normal wind and typhoon loads. (<b>a</b>) Normal wind; (<b>b</b>) Typhoon.</p>
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<p>Internal stress responses at the tower bottom of the wind turbine system under normal wind and typhoon loads. (<b>a</b>) Meridian axial force Ty/(N); (<b>b</b>) Shearing force Txy/(N); (<b>c</b>) Circumferential bending moment Mx/(N·m); (<b>d</b>) Meridian bending moment My/(N·m).</p>
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<p>Internal stress responses at the blade roots of the wind turbine under normal wind and typhoon loads. (<b>a</b>) Shearing force (Txy); (<b>b</b>) Circumferential bending moment (Mx); (<b>c</b>) Meridian bending moment (My).</p>
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<p>Distribution curves of downwind displacements of three blades under normal wind and typhoon loads. (<b>a</b>) Normal wind; (<b>b</b>) Typhoon.</p>
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<p>Changes of the buckling displacement of the wind turbine with wind speed under normal wind and typhoon loads.</p>
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20 pages, 2013 KiB  
Article
Convolutional Neural Network-Based Remote Sensing Images Segmentation Method for Extracting Winter Wheat Spatial Distribution
by Chengming Zhang, Shuai Gao, Xiaoxia Yang, Feng Li, Maorui Yue, Yingjuan Han, Hui Zhao, Ya’nan Zhang and Keqi Fan
Appl. Sci. 2018, 8(10), 1981; https://doi.org/10.3390/app8101981 - 19 Oct 2018
Cited by 14 | Viewed by 4257
Abstract
When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for extracting accurate winter wheat spatial distribution [...] Read more.
When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for extracting accurate winter wheat spatial distribution based on CNN is proposed. A hybrid structure convolutional neural network (HSCNN) was first constructed, which consists of two independent sub-networks of different depths. The deeper sub-network was used to extract the pixels present in the interior of the winter wheat field, whereas the shallower sub-network extracts the pixels at the edge of the field. The model was trained by classification-based learning and used in image segmentation for obtaining the distribution of winter wheat. Experiments were performed on 39 GF-2 images of Shandong province captured during 2017–2018, with SegNet and DeepLab as comparison models. As shown by the results, the average accuracy of SegNet, DeepLab, and HSCNN was 0.765, 0.853, and 0.912, respectively. HSCNN was equally as accurate as DeepLab and superior to SegNet for identifying interior pixels, and its identification of the edge pixels was significantly better than the two comparison models, which showed the superiority of HSCNN in the identification of winter wheat spatial distribution. Full article
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Figure 1

Figure 1
<p>Network architecture of the Hybrid Structure Convolutional Neural Network (HSCNN): (<b>a</b>) input; (<b>b</b>) inner-CNN; (<b>c</b>) inner-layers; (<b>d</b>) inner-encoder; (<b>e</b>) inner-classifier; (<b>f</b>) edge-CNN; (<b>g</b>) edge-layers; (<b>h</b>) edge-encoder; (<b>i</b>) edge-classifier; (<b>j</b>) vote function; (<b>k</b>) output.</p>
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<p>Image-label pair example: (<b>a</b>) original image; and (<b>b</b>) labels.</p>
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<p>Segmentation results for Gaofen-2 (GF-2) images: (<b>a</b>) original images; (<b>b</b>) ground truth, (<b>c</b>) results of SegNet corresponding to the images in (<b>a</b>); (<b>d</b>) errors of SegNet; (<b>e</b>) results of DeepLab; (<b>f</b>) errors of DeepLab; (<b>g</b>) results of HSCNN; and (<b>h</b>) errors of HSCNN.</p>
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<p>Segmentation results for Gaofen-2 (GF-2) images: (<b>a</b>) original images; (<b>b</b>) ground truth, (<b>c</b>) results of SegNet corresponding to the images in (<b>a</b>); (<b>d</b>) errors of SegNet; (<b>e</b>) results of DeepLab; (<b>f</b>) errors of DeepLab; (<b>g</b>) results of HSCNN; and (<b>h</b>) errors of HSCNN.</p>
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<p>Distribution of the probability differences for the inner wheat pixels.</p>
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<p>Distribution of the probability differences for the edge wheat pixels.</p>
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<p>Distribution of the probability differences for the inner wheat pixels.</p>
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<p>Distribution of the probability differences for the edge wheat pixels.</p>
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