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Appl. Sci., Volume 10, Issue 12 (June-2 2020) – 360 articles

Cover Story (view full-size image): The nutritional and medicinal benefits of kiwifruit and persimmon have been known for years. However, the bioactive constituents that mediate the interaction to achieve those benefits are not well documented. Thus, the present study aimed to demarcate the mechanism of actions through in vitro and in silico analyses. Results from the study evidently suggested that the presence of few unique components from different cultivars of the same fruit variety could significantly alter the binding properties with human serum albumin (HSA) and thereby regulate different traits in humans. These findings augur well for applications in functional foods and phar-macological studies. View this paper.
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18 pages, 5799 KiB  
Article
NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency
by Cheng Li, Baolong Guo, Geng Wang, Yan Zheng, Yang Liu and Wangpeng He
Appl. Sci. 2020, 10(12), 4415; https://doi.org/10.3390/app10124415 - 26 Jun 2020
Cited by 10 | Viewed by 4017
Abstract
Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important [...] Read more.
Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better. Full article
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ)
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Figure 1

Figure 1
<p>Dynamic segmentation procedure of simple non-iterative clustering (SNIC). (<b>a</b>) Input image; (<b>b</b>–<b>g</b>) execution of SNIC; (<b>h</b>) segmentation result. Among (<b>b</b>) to (<b>g</b>), each left column indicates the priority queue <math display="inline"><semantics> <mi>Q</mi> </semantics></math>, in which the upmost element has the highest priority. The number in each pixel grid depicts the global order when it is inspected as a 4-neighbor element. Pixels with filled colors in label maps indicate that they have acquired labels corresponding to their nearest cluster centers. Note that the state of <math display="inline"><semantics> <mi>Q</mi> </semantics></math> is one step earlier than the corresponding label map with pixel inspection and label assignment.</p> Full article ">Figure 2
<p>Local inspecting processes of SNIC in detail. Three representative pixels are shown as examples of inspection redundancy when they are labeled for the first time. (<b>a</b>) Boundary pixel 53rd; (<b>b</b>) corner pixel 62nd; (<b>c</b>) internal pixel 8th. Solid arrows stand for popping and labeling in corresponding label map. Notice that there are more than one identical elements with the top-most element in each priority queue.</p> Full article ">Figure 3
<p>Elimination of Inspection Redundancy (EIR)-based inspecting and labeling processes of SNIC. (<b>a</b>) Inspection in clockwise rotation; (<b>b</b>) inspection in cross; (<b>c</b>) local inspecting process in a homogeneous region by EIR. Elements with the yellow box indicates that they are similar in color and satisfy Equation (4).</p> Full article ">Figure 4
<p>Detailed local inspecting and labeling processes from 48th to 50th sequentially of SNIC. Solid arrows stand for popping and labeling in corresponding label maps, while dotted arrows represent inspecting and pushing for the next step.</p> Full article ">Figure 5
<p>Heap structure updating processes of the priority queue from 48th to 50th sequentially in SNIC. (<b>a</b>) Popping 48th from <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>b</b>) pushing 49th on <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>c</b>) a temporary state after sorting 49th in <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>d</b>) Popping 49th from <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>e</b>) pushing 50th on <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>f</b>) a temporary state after sorting 50th in <math display="inline"><semantics> <mi>Q</mi> </semantics></math>. Solid elements indicate that they are newly inspected and pushed in the current step, and their storage location is redirected by solid arrows. Other hollow nodes represent the elements that are inspected before but not labeled. Note that 13th is the last leaf node which recursively executes a sift-up or sift-down operation to modify the structure redirected by dotted arrows, as well as some of hollow nodes with dotted outlines.</p> Full article ">Figure 6
<p>Array structure updating processes in the priority queue from 48th to 50th sequentially in SNIC. (<b>a</b>) Popping 43rd from <math display="inline"><semantics> <mi>Q</mi> </semantics></math> before pushing 25th and 48th; (<b>b</b>) pushing 49th on <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>c</b>) pushing 25th and 49th on <math display="inline"><semantics> <mi>Q</mi> </semantics></math>; (<b>d</b>) popping 48th to 50th from <math display="inline"><semantics> <mi>Q</mi> </semantics></math> before pushing 8th and 51th. The sequence of elements is corresponding to the minimum heap in <a href="#applsci-10-04415-f005" class="html-fig">Figure 5</a>. Solid elements indicate that they are newly inspected and pushed in the current and next step. A last-in-first-out (LIFO) stack with dotted arrows means that the elements are popped in the batch. Notice that there is more than one 25th in each array that is inspected by different pixels.</p> Full article ">Figure 7
<p>Visual comparison of superpixels with <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> on BSDS500. (<b>a</b>) SNIC; (<b>b</b>) Accelerated Implementation based on Recursion (AIR)-based Non-Iterative Clustering (ANIC); (<b>c</b>) EIR-based Non-Iterative Clustering (ENIC); (<b>d</b>) non-iterative clustering with efficiency (NICE). Alternating rows show each segmented image followed by local details of each image.</p> Full article ">Figure 8
<p>Comparison of runtime in milliseconds. (<b>a</b>) Time required for superpixels of increasing number in BSDS500; (<b>b</b>) time required for size-fixed superpixels of increasing size in multiple resolution image sets; (<b>c</b>) time required for number-fixed superpixels of increasing size in multiple resolution image sets. Green boxes show the local details of curves in (<b>b</b>,<b>c</b>).</p> Full article ">Figure 9
<p>Visual comparison of superpixels with <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> on NYUv2. (<b>a</b>) SNIC; (<b>b</b>) FLIC; (<b>c</b>) SNIC; (<b>d</b>) NICE. Alternating columns show each segmented image followed by local details of each image.</p> Full article ">Figure 10
<p>Performance comparison of four algorithms in terms of three quantitative metrics on NYUv2. (<b>a</b>) BR; (<b>b</b>) UE; (<b>c</b>) ASA.</p> Full article ">
25 pages, 6211 KiB  
Article
The Influence of Surfactants, Dynamic and Thermal Factors on Liquid Convection after a Droplet Fall on Another Drop
by Sergey Y. Misyura, Vladimir S. Morozov and Oleg A. Gobyzov
Appl. Sci. 2020, 10(12), 4414; https://doi.org/10.3390/app10124414 - 26 Jun 2020
Cited by 5 | Viewed by 2935
Abstract
The regularities of the processes and characteristics of convection in a sessile drop on a hot wall after the second drop fall are investigated experimentally. The movement of a particle on a drop surface under the action of capillary force and liquid convection [...] Read more.
The regularities of the processes and characteristics of convection in a sessile drop on a hot wall after the second drop fall are investigated experimentally. The movement of a particle on a drop surface under the action of capillary force and liquid convection is considered. The particle motion is realized by a complex curvilinear trajectory. The fall of droplet with and without surfactant additives is considered. Estimates of the influence of the thermal factor (thermocapillary forces) and the dynamic factor (inertia forces) on convection are given. The scientific novelty of the work is the investigation of the simultaneous influence of several factors that is carried out for the first time. It is shown that in the presence of a temperature jump for the time of about 0.01–0.1 s thermocapillary convection leads to a 7–8 times increase in the mass transfer rate in drop. The relative influence of inertial forces is found to be no more than 5%. The fall of drops with surfactant additives (water + surfactant) reduces the velocity jump inside the sessile drop 2–4 times, compared with the water drop without surfactant. Thermocapillary convection leads to the formation of a stable vortex in the drop. The dynamic factor and surfactant additive lead to the vortex breakdown into many small vortices, which results in the suppression of convection. The obtained results are of great scientific and practical importance for heat transfer enhancement and for the control of heating and evaporation rates. Full article
(This article belongs to the Special Issue Heat and Mass Transfer in Intense Liquid Evaporation)
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Figure 1

Figure 1
<p>(<b>a</b>) Scheme of measuring <span class="html-italic">T</span><span class="html-italic"><sub>s</sub></span> and the instantaneous velocity field inside drop 2 using PIV. PIV—Particle Image Velocimetry; TI—Thermal Imager. (<b>b</b>) The measurement scheme of drop 2 static contact angle: (1) the video camera; (2) the source of plane-parallel light; (3) the camera Nikon D750 (with micro lens); (4) drop 2; (5) a thermocouple for measuring the wall temperature (<span class="html-italic">T<sub>w</sub></span>).</p> Full article ">Figure 2
<p>(<b>a</b>) Change of characteristic velocities <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> in the horizontal section of the water drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T</span><sub>w</sub> = 80 °C); (<b>b</b>) change of characteristic velocities <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> in the horizontal section of sessile drop 2 at droplet 1 falling (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">T<sub>W</sub></span> = 80 °C).</p> Full article ">Figure 3
<p>Changes of average temperature for the entire surface of drop 2 over time.</p> Full article ">Figure 4
<p>Changes in average temperature difference Δ<span class="html-italic">T<sub>s</sub></span> for the entire surface of drop 2 over time.</p> Full article ">Figure 5
<p>(<b>a</b>) Thermal image of water drop 2 interface (<span class="html-italic">Ma<sub>T</sub></span>—the thermal Marangoni number, <span class="html-italic">t</span> = 5 s); (<b>b</b>) the direction of characteristic velocities for the two limiting cases of interaction of drops (the joint influence of dynamic and thermal factors); (<b>c</b>) velocities in the horizontal section of sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">T</span><sub>w</sub> = 20 °C).</p> Full article ">Figure 6
<p>(<b>a</b>) Instantaneous velocity fields at the interaction of two drops (PIV measurement, starting point <span class="html-italic">t</span> = 0 s corresponds to droplet 1 falling time, <span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">T</span><sub>w</sub> = 20 °C); (<b>b</b>) image of drop 2 before (left) and after (right) image lightening in the image editor (the red line indicates the contour of drop 2).</p> Full article ">Figure 7
<p>Thermal imager measurements of thermal field on the surface of drop 2 (water) after the fall of droplet 1 (water).</p> Full article ">Figure 8
<p>(<b>a</b>) The behavior of <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> at falling of droplet 1 (<span class="html-italic">V</span><sub>01</sub> = 2.5 µl; water + surfactant AF 9-12; 4% mass) on sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; water); (<b>b</b>) the behavior of <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> at falling of droplet 1 (<span class="html-italic">V</span><sub>01</sub> = 2.5 µl; water + surfactant OP-10; 1% mass) on sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; water); (<b>c</b>) the behavior of <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> at falling of droplet 1 (<span class="html-italic">V</span><sub>01</sub> = 2.5 µl; water + surfactant Sodium DS; 0.1% mass) on sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; water).</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) The behavior of <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> at falling of droplet 1 (<span class="html-italic">V</span><sub>01</sub> = 2.5 µl; water + surfactant AF 9-12; 4% mass) on sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; water); (<b>b</b>) the behavior of <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> at falling of droplet 1 (<span class="html-italic">V</span><sub>01</sub> = 2.5 µl; water + surfactant OP-10; 1% mass) on sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; water); (<b>c</b>) the behavior of <span class="html-italic">U</span><sub>max</sub>, <span class="html-italic">U</span><sub>max(20)</sub>, and <span class="html-italic">U</span><sub>aver</sub> at falling of droplet 1 (<span class="html-italic">V</span><sub>01</sub> = 2.5 µl; water + surfactant Sodium DS; 0.1% mass) on sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; water).</p> Full article ">Figure 9
<p>Velocity field in horizontal section of drop 2; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; <span class="html-italic">T</span><sub>01</sub> = 20 °C; water (droplet 1) + water (drop 2).</p> Full article ">Figure 10
<p>Velocity field in horizontal section of drop 2; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; <span class="html-italic">T</span><sub>01</sub> = 20 °C; water (drop 2), water + surfactant OP-10) (droplet 1).</p> Full article ">Figure 11
<p>Velocity field in horizontal section of drop 2; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; <span class="html-italic">T</span><sub>01</sub> = 20 °C; water (drop 2), water + surfactant AF 9-12 (NEONOL AF 9-12: oxyethylated monoalkyl phenol) (droplet 1).</p> Full article ">Figure 12
<p>Velocity field in horizontal section of drop 2; <span class="html-italic">V</span><sub>01</sub> = 2.5 µl; <span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C; <span class="html-italic">T</span><sub>01</sub> = 20 °C; water (drop 2), water + surfactant SDS (sodium dodecyl sulfate) (droplet 1).</p> Full article ">Figure 13
<p>(<b>a</b>,<b>b</b>) Trajectory of the particle on the drop surface: 1, 2—particle trajectory; 3—the area of the toroid center; (<b>c</b>) particle velocity on the drop surface: 1, 2—experiment; 3—modeling by (4); (<b>d</b>) forces acting on the particle.</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>,<b>b</b>) Trajectory of the particle on the drop surface: 1, 2—particle trajectory; 3—the area of the toroid center; (<b>c</b>) particle velocity on the drop surface: 1, 2—experiment; 3—modeling by (4); (<b>d</b>) forces acting on the particle.</p> Full article ">Figure 14
<p>Velocity <span class="html-italic">U</span><sub>aver</sub> in the horizontal section of sessile drop 2 (<span class="html-italic">V</span><sub>02</sub> = 40 µl; <span class="html-italic">T<sub>w</sub></span> = 80 °C); (1 and 2)—without the fall of small droplet 1; (3–8)—in 3 s after the fall of small droplet 1 with temperature of 20 °C (sessile drop 2 consists of water). Composition of drops: 1—sessile drop 2 (water); 2—sessile drop 2 (water with graphite particles); 3—droplet 1 (surfactant AF 9-12 (4%), <span class="html-italic">V</span><sub>01</sub> = 5 μL); 4—droplet 1 (surfactant AF 9-12 (4%), <span class="html-italic">V</span><sub>01</sub> = 2.5 μL); 5—droplet 1 (surfactant OP-10 (1%), <span class="html-italic">V</span><sub>01</sub> = 2.5 μL); 6—droplet 1 (surfactant OP-10 (1%), <span class="html-italic">V</span><sub>01</sub> = 5 μL); 7—droplet 1 (surfactant SDS (0.1%), <span class="html-italic">V</span><sub>01</sub> = 5 μL); 8—droplet 1 (surfactant SDS (0.1%), <span class="html-italic">V</span><sub>01</sub> = 2.5 μL); I is the interval of measurement errors and I is the interval of measurement errors relative to the dotted horizontal line (average velocity value over four repeated experiments).</p> Full article ">
21 pages, 8622 KiB  
Article
Harmony Search Optimization of Nozzle Movement for Additive Manufacturing of Concrete Structures and Concrete Elements
by Yusuf Cengiz Toklu, Gebrail Bekdaş and Zong Woo Geem
Appl. Sci. 2020, 10(12), 4413; https://doi.org/10.3390/app10124413 - 26 Jun 2020
Cited by 11 | Viewed by 2796
Abstract
There are several ways of using three-dimensional printing techniques in the construction industry. One method that seems quite feasible is the concreting of walls and structural components starting at the bottom and progressing up in layers according to the principles of additive manufacturing. [...] Read more.
There are several ways of using three-dimensional printing techniques in the construction industry. One method that seems quite feasible is the concreting of walls and structural components starting at the bottom and progressing up in layers according to the principles of additive manufacturing. The goal of this study is to optimize the movements of a nozzle at one level that will result in this operation. This study considers that the movements of the nozzle can be of two types: rectangular only (i.e., only in x and y directions) or more freely, including moving in diagonal directions. Applications are performed on four hypothetical flats (with 7, 8, 14, and 31 walls, respectively) and a structural component with 17 members. It is shown that as the number of walls and members increase, the problem of optimizing the movements of the nozzle becomes increasingly difficult due to exponentially increasing path combinations. A comparison is presented in terms of the ratio of movements of the nozzle without concreting to total distances traveled. The optimization process is conducted using the Harmony Search algorithm with a special coding and encoding system. Full article
(This article belongs to the Special Issue Harmony Search Algorithm - Theoretical Background and Practical Applications)
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Figure 1

Figure 1
<p>Five-wall, five-joint example. (<b>a</b>) Numberings of walls and nodes; (<b>b</b>,<b>c</b>,<b>d</b>) nozzle movements for Candidates 1, 2, and 3, respectively.</p> Full article ">Figure 1 Cont.
<p>Five-wall, five-joint example. (<b>a</b>) Numberings of walls and nodes; (<b>b</b>,<b>c</b>,<b>d</b>) nozzle movements for Candidates 1, 2, and 3, respectively.</p> Full article ">Figure 2
<p>Determination of the parameter harmony memory considering rate (HMCR), where the accepted value is 0.95.</p> Full article ">Figure 3
<p>Determination of the parameter pitch adjusting rate (PAR), where the accepted value is 0.34.</p> Full article ">Figure 4
<p>Determination of the parameter PARrange, where the accepted value is 0.60.</p> Full article ">Figure 5
<p>Determination of the parameter PARsign, where the accepted value is 0.16.</p> Full article ">Figure 6
<p>Number of path possibilities with respect to the number of walls.</p> Full article ">Figure 7
<p>Example 1: seven-wall flat. The first R and D solutions are with T = 25.</p> Full article ">Figure 8
<p>Typical run for Example 1 with seven walls: rectangular movements.</p> Full article ">Figure 9
<p>Example 2: eight-wall flat. The first R and D solutions are with T = 35.</p> Full article ">Figure 10
<p>Fourteen-wall flat. The two solutions are with T = 6. (<b>a</b>) First solution with rectangular movements. (<b>b</b>) First solution with diagonal movements permitted.</p> Full article ">Figure 11
<p>Example 4: 31-wall flat. (<b>a</b>) Best solution obtained for rectangular movements with T = 19.5. (<b>b</b>) Best solution obtained for diagonal movements permitted with T = 18.27491.</p> Full article ">Figure 12
<p>Example 5: 17-wall structural component. The first of the best solutions (D1) when diagonal idle movements permitted with T = 12.</p> Full article ">Figure 13
<p>Comparison of idle to total distances.</p> Full article ">
14 pages, 642 KiB  
Article
Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
by Ammar Mohsin Butt, Muhammad Haroon Yousaf, Fiza Murtaza, Saima Nazir, Serestina Viriri and Sergio A. Velastin
Appl. Sci. 2020, 10(12), 4412; https://doi.org/10.3390/app10124412 - 26 Jun 2020
Cited by 5 | Viewed by 2989
Abstract
Human action recognition has gathered significant attention in recent years due to its high demand in various application domains. In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos. The proposed scheme develops a discriminative [...] Read more.
Human action recognition has gathered significant attention in recent years due to its high demand in various application domains. In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos. The proposed scheme develops a discriminative codebook and a hybrid feature vector by encoding the features extracted from CNNs (convolutional neural networks). We explore different CNN architectures for extracting spatio-temporal features. We employ an agglomerative clustering approach for codebook generation, which intends to combine the advantages of global and class-specific codebooks. We propose a Residual Vector of Locally Aggregated Descriptors (R-VLAD) and fuse it with locality-based coding to form a hybrid feature vector. It provides a compact representation along with high order statistics. We evaluated our work on two publicly available standard benchmark datasets HMDB-51 and UCF-101. The proposed method achieves 72.6% and 96.2% on HMDB51 and UCF101, respectively. We conclude that the proposed scheme is able to boost recognition accuracy for human action recognition. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Figure 1
<p>Framework of proposed methodology.</p> Full article ">Figure 2
<p>Agglomerative clustering for codebook generation.</p> Full article ">Figure 3
<p>HMDB51 dataset from left to right Push-up, Chew, Cartwheel, Pour, Sword-Exercise.</p> Full article ">Figure 4
<p>Sample frames from the UCF101 dataset.</p> Full article ">Figure 5
<p>Impact on accuracy by varying the codebook size.</p> Full article ">Figure 6
<p>Comparison of different normalization methods on accuracy.</p> Full article ">
22 pages, 8843 KiB  
Article
Approaches for Detailed Investigations on Transient Flow and Spray Characteristics during High Pressure Fuel Injection
by Noritsune Kawaharada, Lennart Thimm, Toni Dageförde, Karsten Gröger, Hauke Hansen and Friedrich Dinkelacker
Appl. Sci. 2020, 10(12), 4410; https://doi.org/10.3390/app10124410 - 26 Jun 2020
Cited by 5 | Viewed by 3680
Abstract
High pressure injection systems have essential roles in realizing highly controllable fuel injections in internal combustion engines. The primary atomization processes in the near field of the spray, and even inside the injector, determine the subsequent spray development with a considerable impact on [...] Read more.
High pressure injection systems have essential roles in realizing highly controllable fuel injections in internal combustion engines. The primary atomization processes in the near field of the spray, and even inside the injector, determine the subsequent spray development with a considerable impact on the combustion and pollutant formation. Therefore, the processes should be understood as much as possible; for instance, to develop mathematical and numerical models. However, the experimental difficulties are extremely high, especially near the injector nozzle or inside the nozzle, due to the very small geometrical scales, the highly concentrated optical dense spray processes and the high speed and drastic transient nature of the spray. In this study, several unique and partly recently developed techniques are applied for detailed measurements on the flow inside the nozzle and the spray development very near the nozzle. As far as possible, the same three-hole injector for high pressure diesel injection is used to utilize and compare different measurement approaches. In a comprehensive section, the approach is taken to discuss the measurement results in comparison. It is possible to combine the observations within and outside the injector and to discuss the entire spray development processes for high pressure diesel sprays. This allows one to confirm theories and to provide detailed and, in parts, even quantitative data for the validation of numerical models. Full article
(This article belongs to the Special Issue Progress in Spray Science and Technology)
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Figure 1

Figure 1
<p>Overview of spray development [<a href="#B3-applsci-10-04410" class="html-bibr">3</a>].</p> Full article ">Figure 2
<p>Neutron transmission image of diesel injection nozzle [<a href="#B7-applsci-10-04410" class="html-bibr">7</a>].</p> Full article ">Figure 3
<p>X-ray transmission image of a diesel injection nozzle with three injection holes. Two on the left side are overlapping; the right one provides clear visibility. The injection needle is closed.</p> Full article ">Figure 4
<p>Time resolved needle movement.</p> Full article ">Figure 5
<p>Three-dimensional model of the nozzle tip.</p> Full article ">Figure 6
<p>Three-dimensional numerical simulation of the flow inside the nozzle (shown is a 120-degree segment): (<b>a</b>) Velocity amount (left) and streamline (right); (<b>b</b>) Volume fraction of liquid.</p> Full article ">Figure 7
<p>Transparent nozzle and the nozzle holder.</p> Full article ">Figure 8
<p>Sketch of transparent nozzles: (<b>a</b>) 3D cut model; (<b>b</b>) Wireframe with a center plane.</p> Full article ">Figure 9
<p>Actual geometries of transparent nozzles: (<b>a</b>) Nozzle A, Basic geometry; (<b>b</b>) Nozzle B, Basic geometry with hydro-erosive grinding (short step); (<b>c</b>) Nozzle C, Basic geometry with hydro-erosive grinding (long step).</p> Full article ">Figure 10
<p>Comparisons of the cavitation area between experiment (shadowgraphy, left half side) and numerical simulation (right half side) for the different shaped nozzles at 60 MPa injection pressure: (<b>a</b>) Nozzle A, basic geometry; (<b>b</b>) Nozzle B, basic geometry with hydro-erosive grinding (short step); (<b>c</b>) Nozzle C, basic geometry with hydro-erosive grinding (long step).</p> Full article ">Figure 11
<p>Principle and results from the Remote Optical Connectivity Method (ROCM): (<b>a</b>) Principle of ROCM; (<b>b</b>) Illuminated dense core signals for three conditions during start of injection (0.4 ms ASOE) and in the steady injection phase (1.2 ms ASOE) for different injection pressure (single shot examples, filtered signal).</p> Full article ">Figure 12
<p>Comparison of Optical Connectivity Method (OCM) and X-ray phase contrast imaging (PCI) of the near field of a spray (from top to bottom 0.4/1.0/1.4 ms ASOE). Three-hole injector with 115 µm diameter. Injection pressure 100 MPa, gas pressure 0.1 MPa. Both techniques show the non-perturbed length very near to the spray injection, being in the range of 100 to 150 µm. Reproduced with permission from [<a href="#B30-applsci-10-04410" class="html-bibr">30</a>], Elsevier, 2019.</p> Full article ">Figure 13
<p>Length scales of the primary breakup with the non-perturbed length (L<sub>p</sub>) and the breakup length (intact core length, L<sub>b</sub>). Determination of both quantities for different injection pressures for the steady injection state with two methods. Three-hole diesel injector with a nozzle diameter of 115 µm, gas pressure 0.1 MPa. Reproduced with permission from [<a href="#B30-applsci-10-04410" class="html-bibr">30</a>], Elsevier, 2019.</p> Full article ">Figure 14
<p>Double shots shadow graph imaging: (<b>a</b>) Entire spray; (<b>b</b>) Close-up views. Injection with 50 MPa injection pressure.</p> Full article ">Figure 15
<p>Working principle of the Structural Image Velocimetry (SIV). Adapted with permission from [<a href="#B28-applsci-10-04410" class="html-bibr">28</a>], Esytec, 2013.</p> Full article ">Figure 16
<p>Results from high speed SIV at 150 MPa injection pressure: (<b>a</b>) Velocity field; (<b>b</b>) Velocity distribution in radial direction of spray (red) at two downstream positions of 5 and 10 mm, together with measured standard deviation (black); (<b>c</b>) Velocity on spray axis (red) with measured standard deviation (black).</p> Full article ">Figure 17
<p>Schematic diagram and measurement principle of L2F: (<b>a</b>) Measurement prove of L2F; (<b>b</b>) Diagram of the time counter of L2F.</p> Full article ">Figure 18
<p>Time resolved velocity calculated from mass flow rate and velocity measured by L2F.</p> Full article ">Figure 19
<p>Droplet velocity and size results from L2F. Radial profiles at three downstream positions; (<b>a</b>) Droplet velocity; (<b>b</b>) Droplet size.</p> Full article ">Figure 20
<p>Measurement range for different measurement techniques for high pressure diesel spray injection (OCM = Optical Connectivity Method, X-Ray = X-ray phase contrast imaging and X-ray velocimetry, LCV = Laser Correlation Velocimetry, SIV = Structural Image Velocimetry, L2F = Laser 2-Focus Velocimetry, LDA = Laser Doppler Anemometry).</p> Full article ">
22 pages, 3106 KiB  
Article
Handover Management for D2D Communication in 5G Networks
by Wei Kuang Lai, Chin-Shiuh Shieh, Fu-Sheng Chou, Chia-Yu Hsu and Meng-Han Shen
Appl. Sci. 2020, 10(12), 4409; https://doi.org/10.3390/app10124409 - 26 Jun 2020
Cited by 13 | Viewed by 5129
Abstract
This study addresses the handover management issue for Device-to-Device communication in fifth-generation (5G) networks. The Third Generation Partnership Project (3GPP) drafted a standard for proximity services (ProSe), also named device-to-device (D2D) communication, which is a promising technology in offering higher throughput and lower [...] Read more.
This study addresses the handover management issue for Device-to-Device communication in fifth-generation (5G) networks. The Third Generation Partnership Project (3GPP) drafted a standard for proximity services (ProSe), also named device-to-device (D2D) communication, which is a promising technology in offering higher throughput and lower latency services to end users. Handover is an essential issue in wireless mobile networks due to the mobility of user equipment (UE). Specifically, we need to transfer an ongoing connection from an old E-UTRAN Node B (eNB) to a new one, so that the UE can retain its connectivity. In the data plane, both parties of a D2D pair can communicate directly with each other without the involvement of the base station. However, in the control plane, devices must be connected to the eNB for tasks such as power control and resource allocation. In the current standard of handover scheme, the number of unnecessary handovers would be increased by the effect of shadowing fading on two devices. More important, the handover mechanism for D2D pairs is not standardized yet. LTE-A only considers the handover procedure of a single user. Therefore, when a D2D pair moves across cell boundaries, the control channels of the two UEs may connect to different base stations and result in increased latency due to the exchange of D2D related control messages. Hence, we propose a handover management scheme for D2D communication to let both parties of a D2D pair handover to the same destination eNB at the same time. By doing so, the number of unnecessary handovers, as well as the handover latency, can be reduced. In the proposed method, we predict the destination eNB of D2D users based on their movements and the received signal characteristics. Subsequently, we make a handover decision for each D2D pair by jointly factoring in the signal quality and connection stability. Expected improvement can be attained, as revealed in the simulation. Unnecessary handover can be avoided. Consequently, both UEs of a D2D pair reside in the same cell and, therefore, result in increased throughput and decreased delay. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Network environment under consideration.</p> Full article ">Figure 2
<p>An example of a user’s moving process.</p> Full article ">Figure 3
<p>Network topology considered in the simulation.</p> Full article ">Figure 4
<p>Total number of handover, utility, and satisfied ratio versus the number of device-to-device (D2D) pairs under different mobility models. The charts on the left and the charts on the right correspond to the fixed distance (FD) model and reference point group mobility (RPGM) model, respectively. (<b>a</b>) Total number of handovers; (<b>b</b>) utility; (<b>c</b>) satisfied ratio.</p> Full article ">Figure 4 Cont.
<p>Total number of handover, utility, and satisfied ratio versus the number of device-to-device (D2D) pairs under different mobility models. The charts on the left and the charts on the right correspond to the fixed distance (FD) model and reference point group mobility (RPGM) model, respectively. (<b>a</b>) Total number of handovers; (<b>b</b>) utility; (<b>c</b>) satisfied ratio.</p> Full article ">Figure 5
<p>Total number of handovers, utility, and satisfied ratio versus the moving speed of each D2D user under different mobility models. The charts on the left and the charts on the right correspond to the FD model and RPGM model, respectively. (<b>a</b>) Total number of handovers; (<b>b</b>) utility; (<b>c</b>) satisfied ratio.</p> Full article ">Figure 6
<p>Average handover time and average throughput versus the distance between two device-to-device user equipment (DUEs) of the same pair, and the cumulative distribution function (CDF) analysis of transmission delay and average throughput of the system. (<b>a</b>) Performance of average handover time and average throughput. (<b>b</b>) CDF of transmission delay and average throughput.</p> Full article ">Figure 6 Cont.
<p>Average handover time and average throughput versus the distance between two device-to-device user equipment (DUEs) of the same pair, and the cumulative distribution function (CDF) analysis of transmission delay and average throughput of the system. (<b>a</b>) Performance of average handover time and average throughput. (<b>b</b>) CDF of transmission delay and average throughput.</p> Full article ">Figure 7
<p>Ratio of D2D pairs having parties connected to the same eNB.</p> Full article ">Figure 8
<p>System throughput versus user equipment (UE) transmission power.</p> Full article ">Figure 9
<p>Total number of handovers, utility, and average satisfied ratio for the first D2D pair in the system versus its moving speed. (<b>a</b>) Total number of handovers for the first D2D pair. (<b>b</b>) Utility for the first D2D pair. (<b>c</b>) Average satisfied ratio for the first D2D pair.</p> Full article ">Figure 9 Cont.
<p>Total number of handovers, utility, and average satisfied ratio for the first D2D pair in the system versus its moving speed. (<b>a</b>) Total number of handovers for the first D2D pair. (<b>b</b>) Utility for the first D2D pair. (<b>c</b>) Average satisfied ratio for the first D2D pair.</p> Full article ">
20 pages, 6997 KiB  
Article
Hybrid Framework for Simulating Building Collapse and Ruin Scenarios Using Finite Element Method and Physics Engine
by Zhe Zheng, Yuan Tian, Zhebiao Yang and Xinzheng Lu
Appl. Sci. 2020, 10(12), 4408; https://doi.org/10.3390/app10124408 - 26 Jun 2020
Cited by 23 | Viewed by 6245
Abstract
Reliable and high-fidelity virtual ruin scenarios for collapsed buildings are essential for post-earthquake emergency search and rescue training. However, the existing research on the distribution of ruins caused by building collapse is insufficient for supporting post-earthquake rescue training. Therefore, this paper proposes a [...] Read more.
Reliable and high-fidelity virtual ruin scenarios for collapsed buildings are essential for post-earthquake emergency search and rescue training. However, the existing research on the distribution of ruins caused by building collapse is insufficient for supporting post-earthquake rescue training. Therefore, this paper proposes a hybrid framework for simulating building collapse and ruin scenarios, using a finite element (FE) model and a physics engine. Based on this framework, the following methods are proposed: (1) geometric model conversion from the FE model to the physics engine; (2) determination of the initial moment of collapse; and (3) data mapping of the FE simulation results. In addition, a corresponding program, Finite Element Method to Rigid Body Dynamics (FEM2RBD), is developed for the hybrid framework. The proposed framework simulates the entire process of building collapse and the distribution of ruins. The accuracy of the framework is validated using a shaking table test of a three-story reinforced concrete frame. The collapse process and ruin scenario of a real-world library building is simulated as a case study. The results show that the proposed framework combines the advantages of the FE model during the small-deformation stage with the advantages of physics engines during the large-deformation stage. The proposed framework can be valuable in simulating building collapse and ruin scenarios for post-earthquake rescue training. Full article
(This article belongs to the Special Issue Structural Reliability of RC Frame Buildings)
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Figure 1
<p>Hybrid framework based on an FE model and a physics engine.</p> Full article ">Figure 2
<p>Solid model establishment method in Blender.</p> Full article ">Figure 3
<p>Rotation method for inclined elements.</p> Full article ">Figure 4
<p>Flowchart of the displacement mapping method.</p> Full article ">Figure 5
<p>Virtual displacement vector and velocity mapping method.</p> Full article ">Figure 6
<p>Flowchart of the velocity mapping method.</p> Full article ">Figure 7
<p>Dimensions and reinforcements of the model structure (units: mm). [<a href="#B20-applsci-10-04408" class="html-bibr">20</a>] (Reproduced with permission from Huang, Q., from <span class="html-italic">Study on spatial collapse responses of reinforced concrete frame structures under earthquake</span>; published by Tongji Univerity, 2006.).</p> Full article ">Figure 8
<p>Pseudo-acceleration spectra of Load Case 5.</p> Full article ">Figure 9
<p>Maximum horizontal displacement in the X-direction: FE method versus test.</p> Full article ">Figure 10
<p>Collapse process of the reinforced concrete frame subjected to the El-Centro ground motion record: (<b>a</b>) <span class="html-italic">t</span> = 0.00 s; (<b>b</b>) <span class="html-italic">t</span> = 3.88 s; (<b>c</b>) <span class="html-italic">t</span> = 4.16 s.</p> Full article ">Figure 11
<p>Distribution of ruins: (<b>a</b>) test; [<a href="#B20-applsci-10-04408" class="html-bibr">20</a>] (Reproduced with permission from Huang, Q., from <span class="html-italic">Study on spatial collapse responses of reinforced concrete frame structures under earthquake</span>; published by Tongji Univerity, 2006.) (<b>b</b>) the proposed FE and BCB hybrid method; (<b>c</b>) BCB.</p> Full article ">Figure 12
<p>FE model and simulation results of the library building: (<b>a</b>) FE model of the library building; (<b>b</b>) simulation results (<span class="html-italic">t</span> = 1.76 s).</p> Full article ">Figure 13
<p>Collapse process of the library building: (<b>a</b>) <span class="html-italic">t</span> = 2.01 s; (<b>b</b>) <span class="html-italic">t</span> = 3.01 s; (<b>c</b>) <span class="html-italic">t</span> = 3.64 s; (<b>d</b>) <span class="html-italic">t</span> = 4.26 s; (<b>e</b>) <span class="html-italic">t</span> = 5.51 s; (<b>f</b>) <span class="html-italic">t</span> = 6.97 s; (<b>g</b>) <span class="html-italic">t</span> = 14.26 s; (<b>h</b>) <span class="html-italic">t</span> = 18.43 s.</p> Full article ">Figure 13 Cont.
<p>Collapse process of the library building: (<b>a</b>) <span class="html-italic">t</span> = 2.01 s; (<b>b</b>) <span class="html-italic">t</span> = 3.01 s; (<b>c</b>) <span class="html-italic">t</span> = 3.64 s; (<b>d</b>) <span class="html-italic">t</span> = 4.26 s; (<b>e</b>) <span class="html-italic">t</span> = 5.51 s; (<b>f</b>) <span class="html-italic">t</span> = 6.97 s; (<b>g</b>) <span class="html-italic">t</span> = 14.26 s; (<b>h</b>) <span class="html-italic">t</span> = 18.43 s.</p> Full article ">Figure 14
<p>Distribution of the library building ruins.</p> Full article ">
16 pages, 3976 KiB  
Article
Analysis on Steering Performance of Active Steering Bogie According to Steering Angle Control on Curved Section
by Hyunmoo Hur, Yujeong Shin and Dahoon Ahn
Appl. Sci. 2020, 10(12), 4407; https://doi.org/10.3390/app10124407 - 26 Jun 2020
Cited by 8 | Viewed by 3766
Abstract
In this paper, prior to the commercialization of a developed active steering bogie, we want to analyze steering performance experimentally according to steering angle level with the aim of obtaining steering performance data to derive practical design specifications for a steering system. In [...] Read more.
In this paper, prior to the commercialization of a developed active steering bogie, we want to analyze steering performance experimentally according to steering angle level with the aim of obtaining steering performance data to derive practical design specifications for a steering system. In other words, the maximum steering performance can be obtained by controlling the steering angle at the 100% level of the target steering angle, but it is necessary to establish the practical control range in consideration of the steering system cost increase, size increase, and consumer steering performance requirements and commercialize. The steering control test using the active steering bogie was conducted in the section of the steep curve with a radius of curvature of R300, and steering performance such as bogie angle, wheel lateral force, and derailment coefficient were analyzed according to the steering angle level. As the steering angle level increased, the bogie indicated that it was aligned with the radial steering position, and steering performance such as wheel lateral force and derailment coefficient was improved. The steering control at 100% level of the target steering angle can achieve the highest performance of 83.6% reduction in wheel lateral force, but it can be reduced to about one-half of the conventional bogie at 25% level control and about one-third at 50% level. Considering cost rise by adopting the active steering system, this result can be used as a very important design indicator to compromise steering performance and cost rise issues in the design stage of the steering system from a viewpoint of commercialization. Therefore, it is expected that the results of the steering performance experiment according to the steering angle level in this paper will be used as very useful data for commercialization. Full article
(This article belongs to the Section Acoustics and Vibrations)
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<p>Wheelset alignment of a conventional railway vehicle when running on curved section.</p> Full article ">Figure 2
<p>Wheelset alignment of a railway vehicle with active steering technology when running on curved section.</p> Full article ">Figure 3
<p>Radial steering position.</p> Full article ">Figure 4
<p>Configuration of the active steering bogie.</p> Full article ">Figure 5
<p>Prototype of the active steering bogie.</p> Full article ">Figure 6
<p>Block diagram of the active steering control by estimating the radius of the curve.</p> Full article ">Figure 7
<p>The steering angle produced between the front and rear wheelsets of the bogie.</p> Full article ">Figure 8
<p>Curvature of the test curve section.</p> Full article ">Figure 9
<p>Steering angle sensor.</p> Full article ">Figure 10
<p>Wheel force measuring wheelset.</p> Full article ">Figure 11
<p>Measured steering angle according to steering control condition.</p> Full article ">Figure 12
<p>Measured bogie angle of the front bogie.</p> Full article ">Figure 13
<p>Measured bogie angle of the rear bogie.</p> Full article ">Figure 14
<p>Bogie angle difference between front and rear bogies according to steering control conditions.</p> Full article ">Figure 15
<p>Wheel lateral force test data according to steering control test conditions.</p> Full article ">Figure 16
<p>Analysis results for wheel lateral force according to steering control test conditions.</p> Full article ">Figure 17
<p>Derailment coefficient test data according to steering control test conditions.</p> Full article ">Figure 18
<p>Analysis results for derailment coefficient according to steering control test conditions.</p> Full article ">
19 pages, 6734 KiB  
Article
Response Characteristics of Cross Tunnel Lining under Dynamic Train Load
by Ang Wang, Chenghua Shi, Chenyang Zhao, E Deng, Weichao Yang and Hong He
Appl. Sci. 2020, 10(12), 4406; https://doi.org/10.3390/app10124406 - 26 Jun 2020
Cited by 10 | Viewed by 3196
Abstract
The crossing area is a vulnerable component of the interchange high-speed railway tunnel because of the high-static stress level and the long-term dynamic train load in the operation period. Although attention has been paid to this problem, the response characteristics of high-speed railway [...] Read more.
The crossing area is a vulnerable component of the interchange high-speed railway tunnel because of the high-static stress level and the long-term dynamic train load in the operation period. Although attention has been paid to this problem, the response characteristics of high-speed railway tunnel lining at the cross position under the dynamic train load may still need further research as very little investigation is available on this issue at present. In this paper, the initial stress state and dynamic response characteristics of tunnel lining were studied using the three-dimensional finite element method. Furthermore, the damage evolutionary characteristics of the tunnel inverted arch under dynamic and initial static loads were researched using a set of self-developed indoor fatigue test devices. The size of the test box is 400 × 300 × 250 mm (length × width × height). Numerical simulation results indicate that the displacement and stress levels of tunnel lining are very high at the cross position. The stress increment of tunnel lining due to the dynamic train load is more likely to induce a break in the tunnel lining at this position. The indoor fatigue tests reveal that the change of structural strain increment amplitude and strain ratio is obvious when the dynamic load stress level is higher. It is better for dynamic stress levels not to exceed 0.6 times of structural tensile strength to avoid the tunnel lining being damaged in the long-time service period. The initial static load has an influence on the tunnel inverted arch, and the static stress level should be lower than 0.65 times of structural tensile strength to ensure the tunnel has long-time serviceability. This paper provides a reference for the future design of new cross tunnels and the operation safety evaluation and disease regulation of existing high-speed railway tunnels. Full article
(This article belongs to the Special Issue Dynamics of Building Structures)
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<p>Stress diagram of the tunnel inverted arch.</p> Full article ">Figure 2
<p>Schematic diagram of cross tunnels.</p> Full article ">Figure 3
<p>Comparison between field tests and corresponding numerical models: (<b>a</b>) Field measuremen; (<b>b</b>) Numerical model; (<b>c</b>) Local model grid; (<b>d</b>) Schematic diagram of measuring point layout.</p> Full article ">Figure 4
<p>Comparison of vibration acceleration between numerical simulation and field measurement: (<b>a</b>) Transverse acceleration; (<b>b</b>) Vertical acceleration.</p> Full article ">Figure 5
<p>Test system: (<b>a</b>) Model diagram of test system: 1, lateral pressure spring; 2, bottom spring plate; 3, hydraulic golden roof; 4, MTS system; 5, contact steel plate; 6, gearbox. (<b>b</b>) Test loading diagram.</p> Full article ">Figure 6
<p>Monitoring and data collection system. (<b>a</b>) IMC dynamic strain collecting instrument, (<b>b</b>) PV80A impedance analyzer, (<b>c</b>) Layout of the monitoring point- 1, upper strain gauge; 2, lower strain gauge; 3, piezoelectric ceramic patch.</p> Full article ">Figure 7
<p>Initial stress distribution: (<b>a</b>) Maximum tensile stress, σ1; (<b>b</b>) maximum compressive stress, σ3.</p> Full article ">Figure 8
<p>Initial displacement of tunnel lining.</p> Full article ">Figure 9
<p>Vertical displacement of measuring points under different rock thickness: (<b>a</b>) arch of upper tunnel, (<b>b</b>) arch of lower tunnel, (<b>c</b>) left side wall of upper tunnel, (<b>d</b>) left side wall of lower tunnel, (<b>e</b>) invert of upper tunnel, (<b>f</b>) invert of lower tunnel.</p> Full article ">Figure 9 Cont.
<p>Vertical displacement of measuring points under different rock thickness: (<b>a</b>) arch of upper tunnel, (<b>b</b>) arch of lower tunnel, (<b>c</b>) left side wall of upper tunnel, (<b>d</b>) left side wall of lower tunnel, (<b>e</b>) invert of upper tunnel, (<b>f</b>) invert of lower tunnel.</p> Full article ">Figure 10
<p>The maximum tensile stress, σ1; distribution of secondary lining: (<b>a</b>) vault of upper tunnel, (<b>b</b>) vault of lower tunnel, (<b>c</b>) left side wall of upper tunnel, (<b>d</b>) left side wall of lower tunnel, (<b>e</b>) inverted arch of upper tunnel, (<b>f</b>) inverted arch of lower tunnel.</p> Full article ">Figure 11
<p>The maximum compressive stress, σ3; distribution of secondary lining: (<b>a</b>) vault of upper tunnel, (<b>b</b>) vault of lower tunnel, (<b>c</b>) left side wall of upper tunnel, (<b>d</b>) left side wall of lower tunnel, (<b>e</b>) inverted arch of upper tunnel, (<b>f</b>) inverted arch of lower tunnel.</p> Full article ">Figure 11 Cont.
<p>The maximum compressive stress, σ3; distribution of secondary lining: (<b>a</b>) vault of upper tunnel, (<b>b</b>) vault of lower tunnel, (<b>c</b>) left side wall of upper tunnel, (<b>d</b>) left side wall of lower tunnel, (<b>e</b>) inverted arch of upper tunnel, (<b>f</b>) inverted arch of lower tunnel.</p> Full article ">Figure 12
<p>Strain evolutionary characteristics under different dynamic stress levels: (<b>a</b>) strain increment; (<b>b</b>) strain ratio.</p> Full article ">Figure 13
<p>Process of strain evolution.</p> Full article ">Figure 14
<p>Least-square fitting of S-N curve fitting.</p> Full article ">Figure 15
<p>Structural dynamic response with different static loads: (<b>a</b>) compressive strain curves; (<b>b</b>) tensile strain curves.</p> Full article ">Figure 16
<p>Strain and damage curves: (<b>a</b>) damage curve; (<b>b</b>) changing ratio curve.</p> Full article ">Figure 17
<p>Strain evolutionary characteristic: (<b>a</b>) Strain increment; (<b>b</b>) Strain ratio.</p> Full article ">
4 pages, 205 KiB  
Editorial
Sustainable Energy Systems: Optimization and Efficiency
by João Carlos de Oliveira Matias, Radu Godina and Edris Pouresmaeil
Appl. Sci. 2020, 10(12), 4405; https://doi.org/10.3390/app10124405 - 26 Jun 2020
Cited by 6 | Viewed by 2589
Abstract
The world population is growing at a very high rate, which also entails a massive increase in energy consumption, and also, therefore, in its production, which is gradually and steadily increasing. Energy and the environment are essential to achieving sustainable development, and constitute [...] Read more.
The world population is growing at a very high rate, which also entails a massive increase in energy consumption, and also, therefore, in its production, which is gradually and steadily increasing. Energy and the environment are essential to achieving sustainable development, and constitute a fundamental part of human activity. If we consider energy efficiency as the use of an appliance, process or installation for which we try to produce more energy, but with less energy consumption than the average for these appliances, processes or installations, then achieving a higher energy efficiency is imperative. Energy efficiency is a cornerstone policy on the road to stopping climate change and to achieving sustainable societies, along with the development of renewable energy and an environmentally friendly transport policy. In this Special Issue, 11 selected and peer-reviewed articles have been contributed, on a wide range of topics under the umbrella of sustainable energy systems. The published articles encompass distinct areas of interest. One area addresses distributed generation, which addresses such topics as the optimal planning of distributed generation, protection of blind areas in distribution networks, multi-objective optimization in distributed generation, energy management of virtual power plants in distributed generation, and the impact of demand-response programs on a home microgrid, as well as concentrating solar power into a highly renewable, penetrated power system. The second section of the Special Issue addresses a wide range of topics, from parametric studies of 2 MW gas engines or data centers, to combustion characteristics of a non-premixed oxy-flame, to new techniques of PV Tracking, to applications of nanofluids in the thermal performance enhancement of parabolic trough solar collectors. Full article
(This article belongs to the Special Issue Sustainable Energy Systems: Optimization and Efficiency)
11 pages, 3124 KiB  
Article
Temperature and Pressure Dependence of the Infrared Spectrum of 1-Ethyl-3-Methylimidazolium Trifluoromethanesulfonate Ionic Liquid
by Francesco Trequattrini, Anna Celeste, Francesco Capitani, Oriele Palumbo, Adriano Cimini and Annalisa Paolone
Appl. Sci. 2020, 10(12), 4404; https://doi.org/10.3390/app10124404 - 26 Jun 2020
Cited by 1 | Viewed by 3084
Abstract
The infrared absorption spectrum of 1-ethyl-3-methylimidazolium trifluoromethanesulfonate (EMI–TfO) was investigated at ambient pressure and variable temperatures between 120 and 330 K, or at room temperature and variable pressures up to 10 GPa. Upon cooling, the ionic liquid crystallizes; on the contrary, upon compression [...] Read more.
The infrared absorption spectrum of 1-ethyl-3-methylimidazolium trifluoromethanesulfonate (EMI–TfO) was investigated at ambient pressure and variable temperatures between 120 and 330 K, or at room temperature and variable pressures up to 10 GPa. Upon cooling, the ionic liquid crystallizes; on the contrary, upon compression no evidence of crystallization can be obtained from the infrared spectra. Moreover, Density Functional Theory (DFT) calculations were applied to gain a better description of the ionic couple. The ωB97X-D functional, including not only the empirical dispersion corrections but also the presence of a polar solvent, gives a good agreement with the infrared spectrum and suggests that TfO resides above the plane of the imidazolium, with the shorter distance between the O atom of the anion and the C2 atom of the imidazolium ring equal to 2.23 Å. Full article
(This article belongs to the Special Issue Ionic Liquids: Properties and Applications)
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<p>Scheme of the cation and anion composing the investigated ionic liquid.</p> Full article ">Figure 2
<p>Absorbance of 1-Ethyl-3-methylimidazolium trifluoromethanesulfonate (EMI–TfO) in the mid infrared range, measured on cooling (<b>left panel</b>) and subsequent heating (<b>right panel</b>).</p> Full article ">Figure 3
<p>Absorbance of EMI–TfO in the mid infrared range measured at room temperature as a function of pressure. For comparison, two spectra recorded as a function of the temperature on heating in the same frequency range are reported in the lower part of the figure.</p> Full article ">Figure 4
<p>Dependence of the frequency of the vibration mode centered at 1031 cm<sup>−1</sup> at 0.1 GPa from the applied pressure.</p> Full article ">Figure 5
<p>Lowest energy configuration of the EMI–TfO ionic couple, obtained by DFT calculations employing the ωB97X-D functional (including empirical dispersion corrections) and the 6-31G** basis set. The numbers are the distances of selected atoms, expressed in Å.</p> Full article ">Figure 6
<p>Comparison of the experimental absorbance spectra of EMI–TfO at 110 and 330 K (top part of the panels) with the calculations (bottom part of the panels). For the isolated TfO ion, a frequency scaling factor of 0.97 was used. The panel (<b>a</b>,<b>b</b>) reports the far- (mid-) infrared range.</p> Full article ">
18 pages, 3876 KiB  
Article
Quasi-Static Characteristics and Vibration Responses Analysis of Helical Geared Rotor System with Random Cumulative Pitch Deviations
by Bing Yuan, Geng Liu and Lan Liu
Appl. Sci. 2020, 10(12), 4403; https://doi.org/10.3390/app10124403 - 26 Jun 2020
Cited by 8 | Viewed by 3347
Abstract
As one of the long period gear errors, the effects of random cumulative pitch deviations on mesh excitations and vibration responses of a helical geared rotor system (HGRS) are investigated. The long-period mesh stiffness (LPMS), static transmission error (STE), as well as composite [...] Read more.
As one of the long period gear errors, the effects of random cumulative pitch deviations on mesh excitations and vibration responses of a helical geared rotor system (HGRS) are investigated. The long-period mesh stiffness (LPMS), static transmission error (STE), as well as composite mesh error (CMS), and load distributions of helical gears are calculated using an enhanced loaded tooth contact analysis (LTCA) model. A dynamic model with multi degrees of freedom (DOF) is employed to predict the vibration responses of HGRS. Mesh excitations and vibration responses analysis of unmodified HGRS are conducted in consideration of random cumulative pitch deviations. The results indicate that random cumulative pitch deviations have significant effects on mesh excitations and vibration responses of HGRS. The curve shapes of STE and CMS become irregular when the random characteristic of cumulative pitch deviations is considered, and the appearance of partial contact loss in some mesh cycles leads to decreased LPMS when load torque is relatively low. Vibration modulation phenomenon can be observed in dynamic responses of HGRS. In relatively light load conditions, the amplitudes of sideband frequencies become larger than that of mesh frequency and its harmonics (MFIHs) because of relatively high contact ratio. The influences of random cumulative pitch deviations on the vibration responses of modified HGRS are also discussed. Full article
(This article belongs to the Section Mechanical Engineering)
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<p>Flow chart of the research framework and analysis process.</p> Full article ">Figure 2
<p>End-side view and mating state of helical gear pair with cumulative pitch deviations.</p> Full article ">Figure 3
<p>Enhanced loaded tooth contact analysis (LTCA) model of a helical gear pair during one mesh cycle.</p> Full article ">Figure 4
<p>Initial contact condition before load in consideration of cumulative pitch deviations.</p> Full article ">Figure 5
<p>Schematic diagram of HGRS.</p> Full article ">Figure 6
<p>Dynamic model of gear mesh element.</p> Full article ">Figure 7
<p>Dynamic model of HGRS.</p> Full article ">Figure 8
<p>Definition of random cumulative pitch deviations and pitch deviations.</p> Full article ">Figure 9
<p>Long-period mesh stiffness (LPMS), static transmission error (STE), and composite mesh (CMS) at different load torques considering random cumulative pitch deviations.</p> Full article ">Figure 10
<p>Load distributions in the twelfth mesh cycle considering random cumulative pitch deviations.</p> Full article ">Figure 11
<p>DTEs at different load torques considering random cumulative pitch deviations.</p> Full article ">Figure 12
<p>Bearing vibration acceleration at different load torques considering random cumulative pitch deviations.</p> Full article ">Figure 13
<p>Schematic diagram of profile modification for helical gears.</p> Full article ">Figure 14
<p>Brute global optimization of profile modification (unit: μm).</p> Full article ">Figure 15
<p>DTEs of modified and unmodified helical gear pair considering random cumulative pitch deviations.</p> Full article ">Figure 16
<p>Bearing vibration acceleration along y direction considering random cumulative pitch deviations.</p> Full article ">Figure 17
<p>DTEs versus input speeds under different random cumulative pitch deviations conditions.</p> Full article ">
22 pages, 4042 KiB  
Article
A New Semi-Analytical Method for Elasto-Plastic Analysis of a Deep Circular Tunnel Reinforced by Fully Grouted Passive Bolts
by Mingnian Wang, Xiao Zhang, Jianjun Tong, Wenhao Yi, Zhilong Wang and Dagang Liu
Appl. Sci. 2020, 10(12), 4402; https://doi.org/10.3390/app10124402 - 26 Jun 2020
Cited by 5 | Viewed by 2316
Abstract
The use of fully grouted passive bolts as a reinforcement technique has been widely applied to improve the stability of tunnels. To analyze the behaviors of passive bolts and rock mass in a deep circular tunnel, a new semi-analytical solution is presented in [...] Read more.
The use of fully grouted passive bolts as a reinforcement technique has been widely applied to improve the stability of tunnels. To analyze the behaviors of passive bolts and rock mass in a deep circular tunnel, a new semi-analytical solution is presented in this work based on the finite difference method. The rock mass was assumed to experience elastic–brittle–plastic behavior, and the linear Mohr–Coulomb criterion and the nonlinear generalized Hoek–Brown criterion were employed to govern the yielding of the rock mass. The interaction and decoupling between the rock mass and bolts were considered by using the spring–slider model. To simplify the analysis process, a bolted tunnel was divided into a bolted region and an unbolted region, while the contact stress at the bolted–unbolted interface and the rigid displacement of the bolts were obtained using two boundary conditions in combination with the bisection method. Comparisons show that the results obtained using the proposed solution agree well with those from the commercial numerical software and the in situ test. Finally, parametric analyses were performed to examine the effects of various reinforcement parameters on the tunnel’s stability. The proposed solution provided a fast but accurate estimation of the behavior of a reinforced deep circular tunnel for preliminary design purposes. Full article
(This article belongs to the Section Civil Engineering)
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<p>A deep circular tunnel reinforced with passive bolts: (<b>a</b>) the cross-section and (<b>b</b>) the longitudinal section.</p> Full article ">Figure 2
<p>Static equilibrium condition for an infinitesimal element of the bolted rock mass: (<b>a</b>) stresses in the rock mass and bolt tension and (<b>b</b>) equivalent stresses.</p> Full article ">Figure 3
<p>The spring–slider model of the bolt–rock interface.</p> Full article ">Figure 4
<p>The effective diameter of the bolt.</p> Full article ">Figure 5
<p>Schematic representation of the interaction between the rock mass and the end plate: (<b>a</b>) undeformed rock mass and (<b>b</b>) deformed rock mass.</p> Full article ">Figure 6
<p>A bolted circular tunnel with a finite number of annuluses.</p> Full article ">Figure 7
<p>Schematic representation of the numerical model.</p> Full article ">Figure 8
<p>Comparison of the results calculated using the proposed model and numerical model (Mohr–Coulomb (M–C) rock mass): (<b>a</b>) <span class="html-italic">σ<sub>r</sub></span> and <span class="html-italic">σ<sub>θ</sub></span>, (<b>b</b>) <span class="html-italic">u<sub>r</sub></span>, (<b>c</b>) <span class="html-italic">F<sub>n</sub></span>, and (<b>d</b>) <span class="html-italic">τ<sub>s</sub></span>.</p> Full article ">Figure 9
<p>Comparison of the results calculated using the proposed model and numerical model (Hoek–Brown (H–B) rock mass): (<b>a</b>) <span class="html-italic">σ<sub>r</sub></span> and <span class="html-italic">σ<sub>θ</sub></span>, (<b>b</b>) <span class="html-italic">u<sub>r</sub></span>, (<b>c</b>) <span class="html-italic">F<sub>n</sub></span>, and (<b>d</b>) <span class="html-italic">τ<sub>s</sub></span>.</p> Full article ">Figure 10
<p>Comparison of the calculation results and the measured data: (<b>a</b>) <span class="html-italic">u<sub>r</sub></span> and (<b>b</b>) <span class="html-italic">F<sub>n</sub></span>.</p> Full article ">Figure 11
<p>The results obtained from the models that considered or neglected decoupling: (<b>a</b>) <span class="html-italic">u<sub>r(R)</sub></span>/<math display="inline"><semantics> <msubsup> <mi>u</mi> <mrow> <mi>r</mi> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> <mrow> <mi>u</mi> <mi>b</mi> </mrow> </msubsup> </semantics></math>, (<b>b</b>) <span class="html-italic">τ<sub>s</sub></span>, and (<b>c</b>) <span class="html-italic">F<sub>n</sub></span> (<span class="html-italic">E<sub>b</sub></span> = 210 GPa, <span class="html-italic">d<sub>s</sub></span> = 25 mm, <span class="html-italic">A<sub>b</sub></span> = 491 mm<sup>2</sup>, <span class="html-italic">l<sub>z</sub></span> = 1 m, <span class="html-italic">l<sub>b</sub></span> = 3 m, <span class="html-italic">ω</span> = 10°, <span class="html-italic">K<sub>ep</sub></span> = 0 MN/m, <span class="html-italic">β</span> = 0.3, <span class="html-italic">p</span> = 0, <span class="html-italic">c<sub>g</sub></span> = 1 MPa, <span class="html-italic">φ<sub>g</sub></span> = 40°).</p> Full article ">Figure 12
<p>The calculation results with different shear stiffnesses and with or without an end plate: (<b>a</b>) <span class="html-italic">u<sub>r(R)</sub></span>/<math display="inline"><semantics> <msubsup> <mi>u</mi> <mrow> <mi>r</mi> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> <mrow> <mi>u</mi> <mi>b</mi> </mrow> </msubsup> </semantics></math>, (<b>b</b>) <span class="html-italic">τ<sub>s</sub></span>, and (<b>c</b>) <span class="html-italic">F<sub>n</sub></span> (<span class="html-italic">E<sub>b</sub></span> = 210 GPa, <span class="html-italic">d<sub>s</sub></span> = 25 mm, <span class="html-italic">A<sub>b</sub></span> = 491 mm<sup>2</sup>, <span class="html-italic">l<sub>z</sub></span> = 1 m, <span class="html-italic">l<sub>b</sub></span> = 3 m, <span class="html-italic">ω</span> = 10°, <span class="html-italic">β</span> = 0.3, <span class="html-italic">p</span> = 0, <span class="html-italic">c<sub>g</sub></span> = 1 MPa, <span class="html-italic">φ<sub>g</sub></span> = 40°).</p> Full article ">Figure 13
<p>The calculation results with different bolt lengths and densities: (<b>a</b>) <span class="html-italic">l<sub>bn</sub></span>, (<b>b</b>) <span class="html-italic">τ<sub>s</sub></span>, and (<b>c</b>) <span class="html-italic">F<sub>n</sub></span> (<span class="html-italic">E<sub>b</sub></span> = 210 GPa, <span class="html-italic">d<sub>s</sub></span> = 25 mm, <span class="html-italic">A<sub>b</sub></span> = 491 mm<sup>2</sup>, <span class="html-italic">l<sub>z</sub></span> = 1 m, <span class="html-italic">l<sub>b</sub></span> = 3 m, <span class="html-italic">ω</span> = 10°, <span class="html-italic">K<sub>s</sub></span> = 50 MPa, <span class="html-italic">K<sub>ep</sub></span> = 20 MN/m, <span class="html-italic">β</span> = 0.3, <span class="html-italic">p</span> = 0, <span class="html-italic">c<sub>g</sub></span> = 1 MPa, <span class="html-italic">φ<sub>g</sub></span> = 40°).</p> Full article ">Figure A1
<p>An unbolted circular tunnel with a finite number of annuluses.</p> Full article ">Figure A2
<p>Calculation procedure of the proposed solution.</p> Full article ">
26 pages, 6896 KiB  
Article
The Elastic Wave Propagation in Rectangular Waveguide Structure: Determination of Dispersion Curves and Their Application in Nondestructive Techniques
by Eduardo Becker Groth, Thomas Gabriel Rosauro Clarke, Guilherme Schumacher da Silva, Ignacio Iturrioz and Giuseppe Lacidogna
Appl. Sci. 2020, 10(12), 4401; https://doi.org/10.3390/app10124401 - 26 Jun 2020
Cited by 15 | Viewed by 5059
Abstract
The use of mechanic waves for assessing structural integrity is a well-known non-destructive technique (NDT). The ultrasound applied in the guided wave in particular requires significant effort in order to understand the complexities of the propagation so as to develop new methods in [...] Read more.
The use of mechanic waves for assessing structural integrity is a well-known non-destructive technique (NDT). The ultrasound applied in the guided wave in particular requires significant effort in order to understand the complexities of the propagation so as to develop new methods in damage detection, in this case, knowing the interaction between the wave propagation and the geometry of the waveguides is mandatory. In the present work, the wave propagation in rectangular steel rod is presented. In this study, the section dimensions were fixed as 5 × 15 [mm], a typical element of the flexible riser structural amour commonly used in the offshore oil industry. The studies here presented were restricted to [0, 100 KHz] frequencies. This frequency interval is in the range of commercial waveguide equipment commonly applied in ducts in NDT applications. The computation of the dispersion curves is performed by using three different methodologies: (i) analytical solutions, (ii) a method that combines analytical approaches with finite element methods (SAFE), and (iii) experimental method that extracted information from the rod using laser vibrometers and piezoelectric actuators. Finally, two applications based on the dispersion curves determined in the rectangular waveguide are presented to illustrate the possibilities of the curve dispersion knowledge related to the specific geometry in the development and application linked to NDT. The first application consists on showing the possibilities of the techniques that use a fiber grating Bragg cell (FGB) to measure the wave displacement and the second application involves the simulation of pre-fissured prismatic waveguide aimed at searching to induce three characteristic acoustic events. The model was built combining the finite element method and a version of the discrete element method. Full article
(This article belongs to the Special Issue Nondestructive Testing (NDT): Volume II)
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<p>(<b>a</b>) The different layers of a typical riser, (<b>b</b>) the region of the riser that could be monitored using techniques based on guided wave propagation.</p> Full article ">Figure 2
<p>Modes <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">n</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">A</mi> <mrow> <mrow> <mi mathvariant="normal">n</mi> <mtext> </mtext> </mrow> </mrow> </msub> </mrow> </semantics></math> of a rectangular waveguide, with section 15 × 5 [mm], in the frequency range [0, 1] MHz. Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 3
<p>The dispersion curves computed by [<a href="#B11-applsci-10-04401" class="html-bibr">11</a>], for a waveguide of rectangular section 15 × 5 [mm]. Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 4
<p>Dispersion curves of the rectangular stem computed from the two plates overlapping as presented [<a href="#B13-applsci-10-04401" class="html-bibr">13</a>], one considering a thickness of 15 [mm] and the other one a thickness of 5 [mm]. The axis orientation is presented in <a href="#applsci-10-04401-f005" class="html-fig">Figure 5</a>g. Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 5
<p>(<b>a</b>) Dispersion curves computed using the Pedroi et al. method presented in [<a href="#B28-applsci-10-04401" class="html-bibr">28</a>]. Displacement field of (<b>b</b>) second torsional, (<b>c</b>) longitudinal, (<b>d</b>) torsional, (<b>e</b>) flexural (around Axis (2)), (<b>f</b>) flexural wave mode (around Axis (3)). (<b>g</b>) The waveguide scheme with the reference axes indicated. Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 6
<p>Scheme showing the portion of the waveguide modeled using 3D finite element analysis (FEM) for the Sorohan et al. [<a href="#B25-applsci-10-04401" class="html-bibr">25</a>] in the semi-analytical finite element method (SAFE) implementation.</p> Full article ">Figure 7
<p>Results using the Sorohan et al. [<a href="#B25-applsci-10-04401" class="html-bibr">25</a>] SAFE method, the dispersion curves and the displacement field of each mode. Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 8
<p>Frequency response of the piezoelectric transducers used during the tests.</p> Full article ">Figure 9
<p>Details of the experimental setup: (<b>a</b>) laser vibrometer position to obtain displacements u<sub>1</sub> and u<sub>2</sub>. Using piezoelectric ceramics (PZT) with longitudinal polarization to induce longitudinal and flexural around Axis 3 wave modes. (<b>b</b>) Laser vibrometer position to obtain displacements u<sub>2</sub> and u<sub>3</sub>. Using PZT with transversal polarization to induce the flexural around Axis 2 and torsional wave modes. The black line indicates the scanning region, and the green arrow shows the polarization of the PZT transducer. (<b>c</b>) Detail of the PZT transducer glued to the beam tip. (<b>d</b>) The platform where the vibrometers are fixed (the red arrows indicate the degrees of freedom of the platform). (<b>e</b>) Diagram of the experimental setup where the main dimensions were indicated.</p> Full article ">Figure 10
<p>A tone burst with the main frequency of 80 KHz in longitudinal direction. Experimental results: (<b>a</b>) displacements measured in a point over the scan line where T is the period of each wave propagation mode. (<b>b</b>) Displacements over the scan line at a specific time. Numerical results: (<b>c</b>) amplitude displacement in the frequency vs. wave number dominium (with a black line indicating the dispersion curves). (<b>d</b>) Bar analyzed with the map of displacement on it, at a specific time. (<b>e</b>) Transversal section with the spatial distribution of two wave modes. (<b>f</b>) Tridimensional mode views.</p> Full article ">Figure 11
<p>A tone burst with the main frequency of 80 KHz in transversal direction. Experimental results: (<b>a</b>) Displacements measured in a point over the scan line where T is the period of each wave propagation mode. (<b>b</b>) displacement over the scan line at a specific time. Numerical results: (<b>c</b>) amplitude displacement in the frequency vs. wave number dominium (with a black line indicating the dispersion curves). (<b>d</b>) Bar analyzed with the map of displacement on it, in a specific time. (<b>e</b>) Transversal section with the spatial distribution of two wave modes. (<b>f</b>) Tridimensional mode views.</p> Full article ">Figure 12
<p>Comparison made between experimental results (dots) and theoretical dispersion curves obtained using SAFE [<a href="#B28-applsci-10-04401" class="html-bibr">28</a>] implemented in the COMSOL [<a href="#B29-applsci-10-04401" class="html-bibr">29</a>] environment (lines). Moreover, <span class="html-italic">l</span> = longitudinal mode, f<sub>2</sub> = flexion around 2, t = torsion, f<sub>3</sub> = flexion around Axis 3. Notice that, in this plot the wave number is defined as <span class="html-italic">k</span> = (1/λ)</p> Full article ">Figure 13
<p>Comparison made between the analytical beam model [<a href="#B11-applsci-10-04401" class="html-bibr">11</a>] (color lines) and SAFE [<a href="#B28-applsci-10-04401" class="html-bibr">28</a>] solution (grey lines). (l: longitudinal, t: torsional, f2: flexion around Axis 2, f<sub>3</sub>: flexion around Axis 3. Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 14
<p>Comparison between SAFE method [<a href="#B28-applsci-10-04401" class="html-bibr">28</a>] (grey lines) and the superposition of the plate solutions [<a href="#B13-applsci-10-04401" class="html-bibr">13</a>] (color lines). Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 15
<p>Scattering curves of three different sections height × width. (The blue dots in the graph indicate no fundamental modes). Notice that in this graph the wave number is defined as k = 2π/λ [m<sup>−1</sup>].</p> Full article ">Figure 16
<p>Experimental setup used in the assessment made using optical fiber sensors to capture the propagation of mechanical waves in a rectangular rod.</p> Full article ">Figure 17
<p>Collected Signal with the fiber Bragg grating with a tone burst excitation (57 KHz and 7 cycles).</p> Full article ">Figure 18
<p>The lattice discrete element method (LDEM); (<b>a</b>) basic cubic module, (<b>b</b>) generation of a prismatic body, and (<b>c</b>) bilinear constitutive model where the damage is taken into account.</p> Full article ">Figure 19
<p>The pre-fissured prismatic metallic waveguide modeled with LDEM + FEM. The boundary conditions are also depicted.</p> Full article ">Figure 20
<p>Results in the Frequency vs. wave-number domain. (<b>a</b>) in Mode I, (<b>b</b>) in Mode II, (<b>c</b>) in Mode III. (<b>d</b>) The spatial distribution of the three modes of fissure crack propagation, AE emitted from the pre-fissure solicited.</p> Full article ">Figure 21
<p>Information about the acoustic generic emission events simulated: when a typical event happened: (<b>a</b>) dissipated energy increments Ed and its Ed evolution during the process simulated. (<b>b</b>) Kinetic energy Ek and the evolution of its increment Ek during the process simulated. (<b>c</b>) The typical acoustic emission event, A shows by means of the acceleration captured by a sensor (gray line), the Ek and Ed evolution during the process simulated. (<b>d</b>) Detail of a typical fissure propagation simulated with the numerical model used (figure edited from [<a href="#B59-applsci-10-04401" class="html-bibr">59</a>]).</p> Full article ">
27 pages, 9059 KiB  
Article
End-to-End Automated Guided Modular Vehicle
by Luis A. Curiel-Ramirez, Ricardo A. Ramirez-Mendoza, Rolando Bautista-Montesano, M. Rogelio Bustamante-Bello, Hugo G. Gonzalez-Hernandez, Jorge A. Reyes-Avedaño and Edgar Cortes Gallardo-Medina
Appl. Sci. 2020, 10(12), 4400; https://doi.org/10.3390/app10124400 - 26 Jun 2020
Cited by 18 | Viewed by 4351
Abstract
Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial [...] Read more.
Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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<p>Deep Reinforcement Learning (DRL) diagram.</p> Full article ">Figure 2
<p>Research and development methodology.</p> Full article ">Figure 3
<p>Modular platform built by Campus Puebla.</p> Full article ">Figure 4
<p>Instrument connection diagram.</p> Full article ">Figure 5
<p>Final CNN architecture.</p> Full article ">Figure 6
<p>Test track satellite view.</p> Full article ">Figure 7
<p>Robot Operating System (ROS) integration diagram.</p> Full article ">Figure 8
<p>Image acquired and area of interest.</p> Full article ">Figure 9
<p>Histogram of the raw data acquisition.</p> Full article ">Figure 10
<p>Histogram of the data after the normalization and augmentation process.</p> Full article ">Figure 11
<p>Diagram for the data acquisition and training of the neural network.</p> Full article ">Figure 12
<p>Stage 2 of a test, prediction of the model with unseen video.</p> Full article ">Figure 13
<p>Diagram for the prediction of the steering wheel angle of the vehicle.</p> Full article ">Figure 14
<p>Loss plot of model 13 during training.</p> Full article ">Figure 15
<p>Prediction of the steering angle in a sample dataset.</p> Full article ">Figure 16
<p>Examples of shadows on the day of the final presentation.</p> Full article ">
12 pages, 3212 KiB  
Article
A Strain-Transfer Model of Surface-Bonded Sapphire-Derived Fiber Bragg Grating Sensors
by Penghao Zhang, Li Zhang, Zhongyu Wang, Shuang Chen and Zhendong Shang
Appl. Sci. 2020, 10(12), 4399; https://doi.org/10.3390/app10124399 - 26 Jun 2020
Cited by 5 | Viewed by 2125
Abstract
An improved strain-transfer model was developed for surface-bonded sapphire-derived fiber Bragg grating sensors. In the model, the core and cladding of the fiber are separated into individual layers, unlike in conventional treatment that regards the fiber as a unitive structure. The separation is [...] Read more.
An improved strain-transfer model was developed for surface-bonded sapphire-derived fiber Bragg grating sensors. In the model, the core and cladding of the fiber are separated into individual layers, unlike in conventional treatment that regards the fiber as a unitive structure. The separation is because large shear deformation occurs in the cladding when the core of the sapphire-derived fiber is heavily doped with alumina, a material with a high Young’s modulus. Thus, the model was established to have four layers, namely, a core, a cladding, an adhesive, and a host material. A three-layer model could also be obtained from the regressed four-layer model when the core’s radius increased to that of the cladding, which treated the fiber as if it were still homogeneous material. The accuracy of both the four- and three-layer models was verified using a finite-element model and a tensile-strain experiment. Experiment results indicated that a larger core diameter and a higher alumina content resulted in a lower average strain-transfer rate. Error percentages were less than 1.8% when the four- and three-layer models were used to predict the transfer rates of sensors with high and low alumina content, respectively. Full article
(This article belongs to the Section Optics and Lasers)
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<p>Four-layer model of surface-bonded sapphire-derived fiber Bragg grating (SDFBG) test system.</p> Full article ">Figure 2
<p>Three-layer model regressed from four-layer model.</p> Full article ">Figure 3
<p>Stress state in four-layer model.</p> Full article ">Figure 4
<p>Influence of half-bond length and adhesive-bottom thickness on average strain-transfer rate (ASTR) calculated from three-layer model.</p> Full article ">Figure 5
<p>Finite-element (FE) mesh of strain transfer.</p> Full article ">Figure 6
<p>Influence of core radius and alumina content on average strain-transfer rate (ASTR) calculated from four-layer model and finite-element (FE) model.</p> Full article ">Figure 7
<p>Errors of four- and three-layer models from finite-element (FE) model.</p> Full article ">Figure 8
<p>Schematic diagram for strain calibration.</p> Full article ">Figure 9
<p>Experiment setup for strain calibration.</p> Full article ">
16 pages, 870 KiB  
Article
Use of Biofuel Industry Wastes as Alternative Nutrient Sources for DHA-Yielding Schizochytrium limacinum Production
by Sofoklis Bouras, Nikolaos Katsoulas, Dimitrios Antoniadis and Ioannis T. Karapanagiotidis
Appl. Sci. 2020, 10(12), 4398; https://doi.org/10.3390/app10124398 - 26 Jun 2020
Cited by 9 | Viewed by 3745
Abstract
The simultaneous use of crude glycerol and effluent from anaerobic digestate, both wastes derived from the biofuel industry, were tested in the frame of circular economy concept, as potential low-cost nutrient sources for the cultivation of rich in docosahexaenoic acid (DHA) oil microalgae [...] Read more.
The simultaneous use of crude glycerol and effluent from anaerobic digestate, both wastes derived from the biofuel industry, were tested in the frame of circular economy concept, as potential low-cost nutrient sources for the cultivation of rich in docosahexaenoic acid (DHA) oil microalgae strain Schizochytrium limacinum SR21. Initially, the optimal carbon and nitrogen concentration levels for high S. limacinum biomass and lipids production were determined, in a culture media containing conventional, high cost, organic nitrogen sources (yeast extract and peptone), micronutrients and crude glycerol at varying concentrations. Then, the effect of a culture media composed of crude glycerol (as carbon source) and effluent digestate at varying proportions on biomass productivity, lipid accumulation, proximate composition, carbon assimilation and fatty acid content were determined. It was shown that the biomass and total lipid content increased considerably with varying effluent concentrations reaching 49.2 g L−1 at 48% (v/v) of effluent concentration, while the lipid yield at the same effluent concentration reached 10.15 g L−1, compared to 17.0 g L−1 dry biomass and 10.2 g L−1 lipid yield when yeast extract and peptone medium with micronutrients was used. Compared to the control treatment, the above production was obtained with 48% less inorganic salts, which are needed for the preparation of the artificial sea water. It was shown that Schizochytrium limacinum SR21 was able to remediate 40% of the total organic carbon content of the biofuel wastes, while DHA productivity remained at low levels with saturated fatty acids comprising the main fraction of total fatty acid content. The results of the present study suggest that the simultaneous use of two waste streams from the biofuel industry can serve as potential nutrient sources for the growth of Schizochytrium limacinum SR21, replacing the high cost organic nutrients and up to one half the required artificial sea water salts, but upregulation of DHA productivity through optimization of the abiotic environment is necessary for industrial application, including aqua feed production. Full article
(This article belongs to the Special Issue Algal Biomass, Biofuels and Bioproducts)
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<p>Comparison of biomass productivity of <span class="html-italic">S. limacinum</span> in growth media containing the same carbon (120 g L<sup>−1</sup> crude glycerol) concentration and (<b>a</b>) yeast and peptone (Y+P) and micronutrients or (<b>b</b>) varying volumes of effluent digestate (0–48%) combined with NH<sub>4</sub>Cl that resulted in the same nitrogen (3.3 g L<sup>−1</sup>) concentrations, without additional micronutrients. Values are represented as mean ± standard deviation of triplicates, whereas asterisks indicate statistical differences analyzed at a level of <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Comparison of lipid productivity of <span class="html-italic">S. limacinum</span> in growth media containing the same carbon (120 g L<sup>−1</sup> crude glycerol) concentration and (<b>a</b>) yeast and peptone (Y+P) and micronutrients or (<b>b</b>) varying volumes of effluent digestate (0–48%) combined with NH<sub>4</sub>Cl that resulted in the same nitrogen (3.3 g L<sup>−1</sup>) concentrations, without additional micronutrients. Values are represented as mean ± standard deviation of triplicates, whereas asterisks indicate statistical differences analyzed at a level of <span class="html-italic">p</span> &lt; 0.05</p> Full article ">Figure 3
<p>Effect of effluent digestate at varying concentrations (0–48%) with the same carbon and nitrogen concentration of the medium without the addition of micronutrients on total organic carbon assimilation of <span class="html-italic">S. limacinum</span>. Values are represented as mean ± standard deviation of triplicates, whereas asterisks indicate statistical differences analyzed at a level of <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>DHA yield of <span class="html-italic">S. limacinum</span> grown on medium containing effluent digestate at varying concentrations (0–48%) with the same carbon and nitrogen concentration without the addition of micronutrients. Values are represented as mean ± standard deviation of triplicates, whereas asterisks indicate statistical differences analyzed at a level of <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">
18 pages, 3228 KiB  
Article
Precise Channel Estimation Approach for a mmWave MIMO System
by Prateek Saurabh Srivastav, Lan Chen and Arfan Haider Wahla
Appl. Sci. 2020, 10(12), 4397; https://doi.org/10.3390/app10124397 - 26 Jun 2020
Cited by 7 | Viewed by 2966
Abstract
Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. [...] Read more.
Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm’s convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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<p>Block structure of a typical mmWave Multiple Input Multiple Output (MIMO) system.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>c</b>) ASE for different transmit SNRs for a 64 × 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>–<b>c</b>) ASE for different transmit SNRs for a 64 × 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>c</b>) NMSE for different transmit SNRs for a 64 × 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>–<b>c</b>) NMSE for different transmit SNRs for a 64 × 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>c</b>) NMSE at a 30-db transmit SNR for a 64 × 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200, with respect to algorithmic iteration for different <math display="inline"><semantics> <mi mathvariant="sans-serif">γ</mi> </semantics></math> values. (<b>d</b>) The effect of NMSE for multiple paths <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">N</mi> <mrow> <mrow> <mi mathvariant="normal">p</mi> <mtext> </mtext> </mrow> </mrow> </msub> <mrow> <mi>at</mi> <mtext> </mtext> <mi mathvariant="normal">T</mi> </mrow> <mo>=</mo> <mn>2000</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>–<b>c</b>) NMSE at a 30-db transmit SNR for a 64 × 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200, with respect to algorithmic iteration for different <math display="inline"><semantics> <mi mathvariant="sans-serif">γ</mi> </semantics></math> values. (<b>d</b>) The effect of NMSE for multiple paths <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">N</mi> <mrow> <mrow> <mi mathvariant="normal">p</mi> <mtext> </mtext> </mrow> </mrow> </msub> <mrow> <mi>at</mi> <mtext> </mtext> <mi mathvariant="normal">T</mi> </mrow> <mo>=</mo> <mn>2000</mn> </mrow> </semantics></math>.</p> Full article ">
10 pages, 4116 KiB  
Article
Efficient Multifocal Structured Illumination Microscopy Utilizing a Spatial Light Modulator
by Liang Feng, Xiaolei Wang, Xinlei Sun, Sende Wang, Lie Lin, Olga Kosareva and Weiwei Liu
Appl. Sci. 2020, 10(12), 4396; https://doi.org/10.3390/app10124396 - 26 Jun 2020
Cited by 2 | Viewed by 3345
Abstract
We demonstrated an efficient system for multifocal structured illumination microscopy (MSIM) utilizing a spatial light modulator (SLM). Nine phase profiles of chessboard phase plates loaded on the SLM in sequence are used to generate nine multifocal arrays on the focal plane. Subsequently, nine [...] Read more.
We demonstrated an efficient system for multifocal structured illumination microscopy (MSIM) utilizing a spatial light modulator (SLM). Nine phase profiles of chessboard phase plates loaded on the SLM in sequence are used to generate nine multifocal arrays on the focal plane. Subsequently, nine raw multifocal images are acquired. Finally, by extracting the parameters of the illumination patterns from the raw images precisely, a final super-resolved image is reconstructed by performing the standard reconstruction procedure of structured illumination microscopy (SIM). Our MSIM system realized nearly a 1.5-fold enhancement in spatial resolution compared with wide-field (WF) microscopy. The feasibility of the present system is validated on experiments and the results show its great performances along with good compatibility. Full article
(This article belongs to the Section Optics and Lasers)
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<p>Schematic diagram of the multifocal structured illumination microscope. Abbreviations: MO1–MO2: microscope objective lens; L1–L3, lens from Union Optic with focal lengths of 125, 150 and 150 mm; RM, reflective mirror; HPW, half wave plate; LP: linear polarizer; SLM: spatial light modulator; SF: spatial filter; DM, dichroic mirror; LPF, long-pass filter.</p> Full article ">Figure 2
<p>Schematic of the generation of the multifocal arrays. (<b>a</b>) The four remaining laser beams are focused by the MO2. (<b>b</b>) The multifocal array generated by the interference and overlap of the four laser beams.</p> Full article ">Figure 3
<p>Framework of the reconstruction procedure for the multifocal structured illumination microscopy (MSIM) data. (<b>a</b>) The procedure of the reconstruction for the MSIM data, where D<sub>1</sub>–D<sub>9</sub> are nine raw multifocal images arranged in three rows and three columns. A<sub>10</sub>–A<sub>12</sub> are obtained images reconstructed with Column 1–Column 3, while A<sub>13</sub>–A<sub>15</sub> are obtained images reconstructed with Row 1–Row 3, respectively. A<sub>16</sub> is the final super-resolved image obtain by the sum of A<sub>10</sub>–A<sub>16</sub>. (<b>b</b>) Schematic of the reconstruction procedure for the MSIM data.</p> Full article ">Figure 4
<p>The spatial light modulator (SLM) pattern and the multifocal array. (<b>a</b>) The subareas of the load on the SLM. (<b>b</b>) The multifocal at the focal plane, where the inset is the spatial distribution of the remaining two sets of ±1 order diffraction beams.</p> Full article ">Figure 5
<p>Image results of three separate microspheres by wide-field (WF) microscopy (<b>a</b>) and MSIM (<b>b</b>). (<b>c</b>) Cross-section through one of the microsphere images in (<b>a</b>,<b>b</b>), respectively. Scale bars: 4 µm.</p> Full article ">Figure 6
<p>Nine raw multifocal images (<b>a</b>–<b>i</b>) of the fixed sample of Melosira. Scale bars: 40 μm.</p> Full article ">Figure 7
<p>Comparison of WF (<b>a</b>) and MSIM imaging (<b>c</b>) of the fixed sample of Melosira. (<b>b</b>) The sum result of nine raw multifocal images. (<b>d</b>–<b>f</b>) The magnifications of the colored boxed regions in (<b>a</b>–<b>c</b>), respectively. (<b>g</b>) Normalized intensity profiles along the respectively colored lines in (<b>a</b>–<b>c</b>), (<b>h</b>) the inset is the same areas in (<b>d</b>–<b>f</b>) observed by an objective with a higher numerical aperture (0.65) and the plot is the normalized intensity profile along the dashed line in the inset. Scale bars in (<b>a</b>–<b>c</b>): 40 μm, (<b>d</b>–<b>e</b>) and inset in (<b>h</b>): 4 µm.</p> Full article ">Figure 8
<p>Results of the fixed partial transverse section of earthworm (dehydration structure of body cavity fluid) by WF microscopy (<b>a</b>) and MSIM (<b>b</b>). (<b>c</b>) Intensity profiles along the colored dash lines in (<b>a</b>,<b>b</b>), respectively. Scale bars: 40 μm.</p> Full article ">Figure 9
<p>Results of the fixed transverse section of esophagus (esophageal fibrous membrane) by WF microscopy (<b>a</b>) and MSIM (<b>b</b>). (<b>c</b>) Intensity profiles along the colored dash lines in (<b>a</b>,<b>b</b>), respectively. Scale bars: 40 μm.</p> Full article ">
18 pages, 29350 KiB  
Article
Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
by Jongsu Yoon and Yoonsik Choe
Appl. Sci. 2020, 10(12), 4395; https://doi.org/10.3390/app10124395 - 26 Jun 2020
Cited by 8 | Viewed by 3394
Abstract
Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms [...] Read more.
Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of an object. The main aim of this paper is to study image enhancement using convolution sparse coding and sparse representations of the reflectance component in the Retinex model over a learned dictionary. To realize this, we use the convolutional sparse coding model to represent the reflectance component in detail. In addition, we propose that the reflectance component can be reconstructed using a trained general dictionary by using convolutional sparse coding from a large dataset. We use singular value decomposition in limited memory to construct a best reflectance dictionary. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in the experimental results. Consequently, we can reduce the difference in perception between humans and machines through the proposed Retinex-based image enhancement. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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<p>Single image dictionary: (<b>a</b>) Training image; (<b>b</b>) Sparse coding dictionary in single image [<a href="#B17-applsci-10-04395" class="html-bibr">17</a>]; (<b>c</b>) CSC dictionary in single image.</p> Full article ">Figure 2
<p>The result of applying the Retinex algorithm from the single image dictionary in <a href="#applsci-10-04395-f001" class="html-fig">Figure 1</a>: (<b>a</b>) SCR [<a href="#B17-applsci-10-04395" class="html-bibr">17</a>] illumination; (<b>b</b>) SCR reflectance; (<b>c</b>) Image enhancement through SCR; (<b>d</b>) CSC Retinex [<a href="#B18-applsci-10-04395" class="html-bibr">18</a>] illumination; (<b>e</b>) CSC Retinex reflectance; (<b>f</b>) Image enhancement through CSC Retinex.</p> Full article ">Figure 3
<p>Proposed Retinex algorithm dictionary; (<b>a</b>) Our CSC dictionary in large datasets, (<b>b</b>) Our SVD-CSC dictionary in large datasets.</p> Full article ">Figure 4
<p>Results of using different images for training and test in a single image: (<b>a</b>) Training image; (<b>b</b>) Reflectance using CSC dictionary; (<b>c</b>) Image enhancement using CSC dictionary; (<b>d</b>) Original Test image; (<b>e</b>) Reflectance using sparse coding dictionary; (<b>f</b>) Image enhancement using sparse coding dictionary.</p> Full article ">Figure 5
<p>Comparison of single image dictionary and large datasets general dictionary in enhancement image: (<b>a</b>) Image enhancement using SCR same as <a href="#applsci-10-04395-f002" class="html-fig">Figure 2</a>c; (<b>b</b>) Image enhancement using CSC Retinex, same as <a href="#applsci-10-04395-f002" class="html-fig">Figure 2</a>f; (<b>c</b>) Image enhancement using CSC general dictionary; (<b>d</b>) Image enhancement using SVD-CSC single dictionary; (<b>e</b>) Image enhancement using SVD-CSC general dictionary.</p> Full article ">Figure 6
<p>Comparison of SVD-CSC and CSC methods without SVD: (<b>a</b>) Original test image; (<b>b</b>) Reflectance using CSC general dictionary; (<b>c</b>) Image enhancement using CSC general dictionary; (<b>d</b>) Reflectance using SVD-CSC general dictionary; (<b>e</b>) Image enhancement using SVD-CSC general dictionary.</p> Full article ">Figure 7
<p>Comparison of SVD-CSC and CSC methods without SVD: (<b>a</b>) Original test image; (<b>b</b>) Reflectance using CSC general dictionary; (<b>c</b>) Image enhancement using CSC general dictionary; (<b>d</b>) Reflectance using SVD-CSC general dictionary; (<b>e</b>) Image enhancement using SVD-CSC general dictionary.</p> Full article ">Figure 8
<p>Comparison of SVD-CSC and CSC methods without SVD: (<b>a</b>) Original test image; (<b>b</b>) Reflectance using CSC general dictionary; (<b>c</b>) Image enhancement image using CSC general dictionary; (<b>d</b>) Reflectance using SVD-CSC general dictionary; (<b>e</b>) Image enhancement image using SVD-CSC general dictionary.</p> Full article ">Figure 9
<p>Comparison of reconstruction results by filter size; (<b>a</b>) 5 × 5 × 100 filters CSC, (<b>b</b>) 5 × 5 × 100 filters SVD-CSC, (<b>c</b>) 15 × 15 × 100 filters CSC, (<b>d</b>) 15 × 15 × 100 filters SVD-CSC.</p> Full article ">Figure 10
<p>Comparison of high contrast image reconstruction: (<b>a</b>) Original test image; (<b>b</b>) Reflectance using [<a href="#B16-applsci-10-04395" class="html-bibr">16</a>] Retinex model; (<b>c</b>) Image enhancement image using [<a href="#B16-applsci-10-04395" class="html-bibr">16</a>] Retinex model; (<b>d</b>) Reflectance using SVD-CSC general dictionary; (<b>e</b>) Image enhancement image using SVD-CSC general dictionary.</p> Full article ">Figure 11
<p>Comparison of various methods using S-CIELAB color metric: (<b>a</b>) Original image; (<b>b</b>) Shadow version of the original image; (<b>c</b>) Image enhancement by sparse coding; (<b>d</b>) Image enhancement by single image CSC; (<b>e</b>) Image enhancement by single image SVD-CSC; (<b>f</b>) Image enhancement by large data CSC; (<b>g</b>) Image enhancement by large data SVD-CSC.</p> Full article ">Figure 12
<p>S-CIELAB Spatial distribution of errors and error histogram: (<b>a</b>) Spatial distribution of errors between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the sparse coding results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>c that are 30 units or higher, marked in green; (<b>b</b>) S-CIELAB histogram distribution between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the sparse coding results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>c; (<b>c</b>) Spatial distribution of errors between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the single image CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>d that are 30 units or higher, marked in green; (<b>d</b>) S-CIELAB histogram distribution between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the single image CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>d; (<b>e</b>) Spatial distribution of errors between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the single image SVD-CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>e that are 30 units or higher, marked in green; (<b>f</b>) S-CIELAB histogram distribution between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the single image SVD-CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>e; (<b>g</b>) Spatial distribution of errors between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the large data CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>f that are 30 units or higher, marked in green; (<b>h</b>) S-CIELAB histogram distribution between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the large data CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>f; (<b>i</b>) Spatial distribution of errors between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the large data SVD-CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>g that are 30 units or higher, marked in green; (<b>j</b>) S-CIELAB histogram distribution between <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>a and the large data SVD-CSC results in <a href="#applsci-10-04395-f011" class="html-fig">Figure 11</a>g.</p> Full article ">
29 pages, 13341 KiB  
Article
Appraisal of the Spatial Resolution of 2D Electrical Resistivity Tomography for Geotechnical Investigation
by Yin Chun Hung, Ho Shu Chou and Chih Ping Lin
Appl. Sci. 2020, 10(12), 4394; https://doi.org/10.3390/app10124394 - 26 Jun 2020
Cited by 18 | Viewed by 3281
Abstract
In the past decade, the 2D electrical resistivity tomography (ERT) has been extensively used in the investigation and monitoring of geotechnical engineering and environment engineering, but there are many uncertainties hidden behind its vivid color earth-resistivity profiles. In order to use the 2D [...] Read more.
In the past decade, the 2D electrical resistivity tomography (ERT) has been extensively used in the investigation and monitoring of geotechnical engineering and environment engineering, but there are many uncertainties hidden behind its vivid color earth-resistivity profiles. In order to use the 2D ERT in the scale of geotechnical engineering effectively, the accuracy and spatial resolution capability of measurements must be enhanced, or at least these uncertainties should be mastered to avoid overreading the measurement results. There were seven common geological models built in this study to discuss the variance in spatial analysis capability of 2D electrical resistivity profiles under different geologic conditions. The findings show that the resolution capability of 2D electrical resistivity profiles was influenced by depth, and in different strata, it may be influenced by the resistivity ratio, layer depth, covering depth, interlayer thickness, tilt angle, medium size, and noise intensity. Generally speaking, the relatively low resistance stratum had better resolution capability; if the relatively high resistance stratum was located under the relatively low resistance stratum, its resolution capability declined. In different strata, the resolution capability may be degraded under the effect of different factors. In addition, any noise in the course of measurement resulted in a random jump of the electrical resistivity profile, which worsened as the noise increased. These circumstances should be paid special attention to avoid misrecognition of electrical resistivity profile images. Full article
(This article belongs to the Section Civil Engineering)
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<p>Method of 2D ERT: (<b>a</b>) Schematic diagram of the arrangement of current electrodes and potential electrodes and (<b>b</b>) 2D ERT measurement method and pseudo-section.</p> Full article ">Figure 2
<p>Schematic diagram of the single horizontal layer stratum model.</p> Full article ">Figure 3
<p>Schematic diagram of the horizontal interlayer stratum model.</p> Full article ">Figure 4
<p>Schematic diagram of the single vertical layer stratum model.</p> Full article ">Figure 5
<p>Schematic diagram of the vertical interlayer stratum model.</p> Full article ">Figure 6
<p>Schematic diagram of the composite stratum model.</p> Full article ">Figure 7
<p>Schematic diagram of the tilted layer stratum model.</p> Full article ">Figure 8
<p>Schematic diagram of the debris mixed stratum model.</p> Full article ">Figure 9
<p>Schematic diagram of the boundary effect model.</p> Full article ">Figure 10
<p>Schematic diagram of the 3D effect model.</p> Full article ">Figure 11
<p>Results of Single Horizontal Layer Stratum: (<b>a</b>) different resistivities, (<b>b</b>) layers at different heights, and (<b>c</b>) different noises.</p> Full article ">Figure 11 Cont.
<p>Results of Single Horizontal Layer Stratum: (<b>a</b>) different resistivities, (<b>b</b>) layers at different heights, and (<b>c</b>) different noises.</p> Full article ">Figure 12
<p>Results of horizontal interlayer stratum: (<b>a</b>) different resistivities, (<b>b</b>) interlayer center at different depths, (<b>c</b>) different interlayer thicknesses, and (<b>d</b>) different noises.</p> Full article ">Figure 12 Cont.
<p>Results of horizontal interlayer stratum: (<b>a</b>) different resistivities, (<b>b</b>) interlayer center at different depths, (<b>c</b>) different interlayer thicknesses, and (<b>d</b>) different noises.</p> Full article ">Figure 13
<p>Results of the single vertical layer stratum: (<b>a</b>) different resistivities and (<b>b</b>) differen noises.</p> Full article ">Figure 14
<p>Results of the vertical interlayer stratum: (<b>a</b>) different resistivities, (<b>b</b>) different interlayer thicknesses, and (<b>c</b>) different noises.</p> Full article ">Figure 14 Cont.
<p>Results of the vertical interlayer stratum: (<b>a</b>) different resistivities, (<b>b</b>) different interlayer thicknesses, and (<b>c</b>) different noises.</p> Full article ">Figure 15
<p>Results of composite stratum: (<b>a</b>) different resistivities, (<b>b</b>) different material center depths, and (<b>c</b>) different noises.</p> Full article ">Figure 15 Cont.
<p>Results of composite stratum: (<b>a</b>) different resistivities, (<b>b</b>) different material center depths, and (<b>c</b>) different noises.</p> Full article ">Figure 16
<p>Results of Tilted Layer Stratum: (<b>a</b>) different resistivities, (<b>b</b>) different tilt angles, and (<b>c</b>) different noises.</p> Full article ">Figure 17
<p>Results of debris mixed stratum: (<b>a</b>) different resistivities, (<b>b</b>) different covering depths, (<b>c</b>) different mesh sizes, (<b>d</b>) different mesh spacings, and (<b>e</b>) different noises.</p> Full article ">Figure 17 Cont.
<p>Results of debris mixed stratum: (<b>a</b>) different resistivities, (<b>b</b>) different covering depths, (<b>c</b>) different mesh sizes, (<b>d</b>) different mesh spacings, and (<b>e</b>) different noises.</p> Full article ">Figure 17 Cont.
<p>Results of debris mixed stratum: (<b>a</b>) different resistivities, (<b>b</b>) different covering depths, (<b>c</b>) different mesh sizes, (<b>d</b>) different mesh spacings, and (<b>e</b>) different noises.</p> Full article ">Figure 18
<p>Influential distances of the boundary effect.</p> Full article ">Figure 19
<p>Influential distances of the boundary effect.</p> Full article ">
15 pages, 2842 KiB  
Article
Experimental Study on the Performance of GFRP–GFRP Slip-Critical Connections with and without Stainless-Steel Cover Plates
by Yang Peng, Wei Chen, Zhe Wu, Jun Zhao and Jun Dong
Appl. Sci. 2020, 10(12), 4393; https://doi.org/10.3390/app10124393 - 26 Jun 2020
Cited by 2 | Viewed by 2852
Abstract
Composite structures have become increasingly popular in civil engineering due to many advantages, such as light weight, excellent corrosion resistance and high productivity. However, they still lack the strength, stiffness, and convenience of constructions of fastener connections in steel structures. The most popular [...] Read more.
Composite structures have become increasingly popular in civil engineering due to many advantages, such as light weight, excellent corrosion resistance and high productivity. However, they still lack the strength, stiffness, and convenience of constructions of fastener connections in steel structures. The most popular fastener connections in steel structures are slip-critical connections, and the major factors that influence their strength are the slip factors between faying surfaces and the clamping force due to the prevailing torque. This paper therefore examined the effect that changing the following parameters had on the slip factor: (1) replacing glass fiber reinforced plastic (GFRP) cover plates with stainless-steel cover plates; (2) adopting different surface treatments for GFRP-connecting plates and stainless-steel cover plates, respectively; and (3) applying different prevailing torques to the high-strength bolts. The impact on the long-term effects of the creep property in composite elements under the pressure of high-strength bolts was also studied with pre-tension force relaxation tests. It is shown that a high-efficiency fastener connection can be obtained by using stainless-steel cover plates with a grit-blasting surface treatment, with the maximum slip factor reaching 0.45, while the effects of the creep property are negligible. Full article
(This article belongs to the Special Issue Progress of Fiber-Reinforced Composites: Design and Applications)
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<p>Specimen size (unit:mm).</p> Full article ">Figure 2
<p>Plate surfaces with and without grit-blasting treatment. (<b>a</b>) Untreated stainless-steel surface; (<b>b</b>) stainless-steel surface treated with 60# grit-blasting; (<b>c</b>) stainless-steel surface treated with 24# grit-blasting; (<b>d</b>) untreated GFRP surface; (<b>e</b>) GFRP surface treated with 60# grit-blasting; (<b>f</b>) GFRP surface treated with 24# grit-blasting.</p> Full article ">Figure 3
<p>Three types of slip load curves [<a href="#B10-applsci-10-04393" class="html-bibr">10</a>].</p> Full article ">Figure 4
<p>Loading equipment and testing setup.</p> Full article ">Figure 5
<p>The load–displacement curves of specimens with different cover plates.</p> Full article ">Figure 6
<p>Plate surfaces of G-G connection after shear test. (<b>a</b>) GFRP connecting plate surface after shear test; (<b>b</b>) GFRP cover plate surface after shear test.</p> Full article ">Figure 7
<p>The load–displacement curves of specimens with different grit-blasting surface treatments.</p> Full article ">Figure 8
<p>Plate surfaces of G-S24# connection after shear test. (<b>a</b>) GFRP connecting plate surface after shear test; (<b>b</b>) stainless-steel cover plate surface after shear test.</p> Full article ">Figure 9
<p>Plate surfaces of G60#-S24# connection after shear test. (<b>a</b>) GFRP connecting plate surface after shear test; (<b>b</b>) stainless-steel cover plate surface after shear test.</p> Full article ">Figure 10
<p>The load–displacement curves of connections with different prevailing torques.</p> Full article ">Figure 11
<p>Comparison of the slip load residual rate after the bolt pre-tension relaxation tests.</p> Full article ">
14 pages, 2438 KiB  
Article
A Textile-Based Microfluidic Platform for the Detection of Cytostatic Drug Concentration in Sweat Samples
by Goran M. Stojanović, Maja M. Radetić, Zoran V. Šaponjić, Marija B. Radoičić, Milan R. Radovanović, Željko V. Popović and Saša N. Vukmirović
Appl. Sci. 2020, 10(12), 4392; https://doi.org/10.3390/app10124392 - 26 Jun 2020
Cited by 18 | Viewed by 3385
Abstract
This work presents a new multilayered microfluidic platform, manufactured using a rapid and cost-effective xurography technique, for the detection of drug concentrations in sweat. Textile fabrics made of cotton and polyester were used as a component of the platform, and they were positioned [...] Read more.
This work presents a new multilayered microfluidic platform, manufactured using a rapid and cost-effective xurography technique, for the detection of drug concentrations in sweat. Textile fabrics made of cotton and polyester were used as a component of the platform, and they were positioned in the middle of the microfluidic device. In order to obtain a highly conductive textile, the fabrics were in situ coated with different amounts of polyaniline and titanium dioxide nanocomposite. This portable microfluidic platform comprises at least three layers of optically transparent and flexible PVC foils which were stacked one on top of the other. Electrical contacts were provided from the edge of the textile material when a microfluidic variable resistor was actually created. The platform was tested in plain artificial sweat and in artificial sweat with a dissolved cytostatic test drug, cyclophosphamide, of different concentrations. The proposed microfluidic device decreased in resistance when the sweat was applied. In addition, it could successfully detect different concentrations of cytostatic medication in the sweat, which could make it a very useful tool for simple, reliable, and fast diagnostics. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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<p>(<b>a</b>) Design of the separate layers; (<b>b</b>) the manufactured textile-based microfluidic platform.</p> Full article ">Figure 2
<p>SEM images of control and impregnated CO and PET fibers.</p> Full article ">Figure 3
<p>FTIR spectra of (<b>a</b>) CO and CO-PANI/TiO<sub>2</sub>-1 samples and (<b>b</b>) PET and PET-PANI/TiO<sub>2</sub>-1 samples.</p> Full article ">Figure 4
<p>(<b>a</b>) SEM image of CO fabric, (<b>b</b>) SEM image of PET fabric, and magnified views of the microfluidic platforms with (<b>c</b>) CO-PANI/TiO<sub>2</sub>-1; (<b>d</b>) CO-PANI/TiO<sub>2</sub>-2; (<b>e</b>) PET-PANI/TiO<sub>2</sub>-1; (<b>f</b>) PET-PANI/TiO<sub>2</sub>-2; and (<b>g</b>) PET-PANI/TiO<sub>2</sub>-1 with injected artificial sweat.</p> Full article ">Figure 5
<p>(<b>a</b>) Resistance as a function of frequency for PET-PANI/TiO<sub>2</sub>-1 and for different fluids soaking the fabric; (<b>b</b>) Resistance as a function of cyclophosphamide (CPA) concentration for different fabrics.</p> Full article ">Figure 6
<p>(<b>a</b>) A block diagram of the electronic circuit for data acquisition from the microfluidic platform, processing and displaying measured data (ADC - analog-to-digital converter); (<b>b</b>) The electronic device for displaying the measured concentration of CPA.</p> Full article ">
15 pages, 6896 KiB  
Article
Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging
by Yasir Bashir, Nordiana Mohd Muztaza, Seyed Yaser Moussavi Alashloo, Syed Haroon Ali and Deva Prasad Ghosh
Appl. Sci. 2020, 10(12), 4391; https://doi.org/10.3390/app10124391 - 26 Jun 2020
Cited by 12 | Viewed by 3602
Abstract
Fractured imaging is an important target for oil and gas exploration, as these images are heterogeneous and have contain low-impedance contrast, which indicate the complexity in a geological structure. These small-scale discontinuities, such as fractures and faults, present themselves in seismic data in [...] Read more.
Fractured imaging is an important target for oil and gas exploration, as these images are heterogeneous and have contain low-impedance contrast, which indicate the complexity in a geological structure. These small-scale discontinuities, such as fractures and faults, present themselves in seismic data in the form of diffracted waves. Generally, seismic data contain both reflected and diffracted events because of the physical phenomena in the subsurface and due to the recording system. Seismic diffractions are produced once the acoustic impedance contrast appears, including faults, fractures, channels, rough edges of structures, and karst sections. In this study, a double square root (DSR) equation is used for modeling of the diffraction hyperbola with different velocities and depths of point diffraction to elaborate the diffraction hyperbolic pattern. Further, we study the diffraction separation methods and the effects of the velocity analysis methods (semblance vs. hybrid travel time) for velocity model building for imaging. As a proof of concept, we apply our research work on a steep dipping fault model, which demonstrates the possibility of separating seismic diffractions using dip frequency filtering (DFF) in the frequency–wavenumber (F-K) domain. The imaging is performed using two different velocity models, namely the semblance and hybrid travel time (HTT) analysis methods. The HTT method provides the optimum results for imaging of complex structures and imaging below shadow zones. Full article
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<p>Graphical representation of the diffraction phenomenon with the geometry of the source and receiver functions produced at the edges of the reflectors (D1 and D2). A phase change of 180° is shown on either side of the curves [<a href="#B16-applsci-10-04391" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>Diffraction hyperboloids with constant velocity at: (<b>a</b>) 2000 m/sec, (<b>b</b>) 3500 m/sec, and (<b>c</b>) 5000 m/sec. The curvature of the hyperboloids is spread out with an increase of velocity.</p> Full article ">Figure 3
<p>Diffraction hyperboloids: (<b>a</b>) increasing velocity with depth and (<b>b</b>) decreasing velocity with depth.</p> Full article ">Figure 4
<p>(<b>a</b>) Geographical map of the Malay Basin. Shown are the provinces of Peninsular in the Malay Basin, Sarawak, and Sabah Basin [<a href="#B24-applsci-10-04391" class="html-bibr">24</a>]. The Malay Basin subsurface structure shown with a regional cross-section view. (<b>b</b>) South-west to north-south section. The fractured basement can be seen at a depth of 2–5 km and varies with lateral extension. (<b>c</b>) North-west to south-east section [<a href="#B5-applsci-10-04391" class="html-bibr">5</a>].</p> Full article ">Figure 5
<p>(<b>a</b>) Malay Basin field, which contains extensive faults and fractures in the igneous basement [<a href="#B28-applsci-10-04391" class="html-bibr">28</a>]. (<b>b</b>) Input initial velocity model for seismic analysis [<a href="#B8-applsci-10-04391" class="html-bibr">8</a>].</p> Full article ">Figure 6
<p>(<b>a</b>) Seismic response of the input velocity model; a series of diffractions are generated at the fault location. (<b>b</b>) The frequency–wavenumber (F-K) spectrum, which shows the amplitude distribution with wave cycles per kilometer. (<b>c</b>) F-K spectrum after application of the designed filter; only the diffraction amplitude is displayed, while the reflection amplitude has been removed. (<b>d</b>) After separation using the DFF filtering algorithm, diffractions are preserved and reflections are successfully suppressed.</p> Full article ">Figure 7
<p>(<b>a</b>) Migrated seismic section, The point diffractor shows the location of the discontinuity and the fault indicator. (<b>b</b>) Full-wave seismic migrated section; all components of the fault are missing. (<b>c</b>) Amplitude spectrum (frequency bandwidth) of the migrated data, showing that the diffraction amplitude is biased.</p> Full article ">Figure 8
<p>(<b>a</b>) Marmousi velocity model with a velocity range from 1650 to 4600 m/sec. Three major faults containing unconformity and overburden cause complexity in the velocity model for anticline structures. (<b>b</b>) Synthetic shot gather data are generated using finite difference modeling.</p> Full article ">Figure 9
<p>Seismic gather data (<b>a</b>) before the NMO correction and (<b>b</b>) after the NMO correction. Events are slightly straight at the true reflection points.</p> Full article ">Figure 10
<p>Trajectories of 100 rays emitted by a source point in a heterogeneous medium. Shadow zone and multipathing effects can be seen [<a href="#B30-applsci-10-04391" class="html-bibr">30</a>].</p> Full article ">Figure 11
<p>(<b>a</b>) Common Mid Point (CMP) stacked section after careful processing, including sorting of the gather data from the CMP gather data, amplitude correction, and NMO correction. (<b>b</b>) Diffraction section after application of dip frequency filtering with high amplitude display. Diffracted events are enhanced. (<b>c</b>) Difference or reflection section, showing that consideration of the diffraction is quite important for successful subsurface imaging, especially for faults and fractures.</p> Full article ">Figure 12
<p>(<b>a</b>) Seismic migration using prestack Kirchhoff migration. A velocity model was updated using semblance velocity analysis. Faults and anticlinal structures that were our targets are not imaged. (<b>b</b>) Migrated seismic data using Kirchhoff depth migration, with the hybrid travel time calculated by the eikonal equation and paraxial raytracing. Faults and anticline sections in the deeper section are imaged properly.</p> Full article ">Figure 13
<p>(<b>a</b>) The amplitude spectrum of the migrated data shows a linear distribution of the amplitude energy. (<b>b</b>) Frequency spectrum of input data (purple), diffraction data (green), and migrated data using hybrid travel time (blue).</p> Full article ">
14 pages, 3327 KiB  
Article
Assessment of Angular Spectral Distributions of Laser Accelerated Particles for Simulation of Radiation Dose Map in Target Normal Sheath Acceleration Regime of High Power Laser-Thin Solid Target Interaction—Comparison with Experiments
by Andreea Groza, Alecsandru Chirosca, Elena Stancu, Bogdan Butoi, Mihai Serbanescu, Dragana B. Dreghici and Mihai Ganciu
Appl. Sci. 2020, 10(12), 4390; https://doi.org/10.3390/app10124390 - 26 Jun 2020
Cited by 6 | Viewed by 3377
Abstract
An adequate simulation model has been used for the calculation of angular and energy distributions of electrons, protons, and photons emitted during a high-power laser, 5-µm thick Ag target interaction. Their energy spectra and fluencies have been calculated between 0 and 360 degrees [...] Read more.
An adequate simulation model has been used for the calculation of angular and energy distributions of electrons, protons, and photons emitted during a high-power laser, 5-µm thick Ag target interaction. Their energy spectra and fluencies have been calculated between 0 and 360 degrees around the interaction point with a step angle of five degrees. Thus, the contribution of each ionizing species to the total fluency value has been established. Considering the geometry of the experimental set-up, a map of the radiation dose inside the target vacuum chamber has been simulated, using the Geant4 General Particle Source code, and further compared with the experimental one. Maximum values of the measured dose of the order of tens of mGy per laser shot have been obtained in the direction normal to the target at about 30 cm from the interaction point. Full article
(This article belongs to the Special Issue Laser-Driven Accelerators, Radiations, and Their Applications)
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<p>Geometry mesh of the GEANT4 simulation model: 1a—source of electrons; 1b—target; 1c—source of protons; 2—hole; 3—magnetic spectrometer; 4—target holder; 5—detector holder; 6—aluminum cylinder. The structure of the electron, proton sources and target are presented in the right corner of the image.</p> Full article ">Figure 2
<p>(<b>a</b>) Experimental set-up; (<b>b</b>) Detailed of the experimental set-up.</p> Full article ">Figure 3
<p>(<b>a</b>) Pixel values to optical density Rodbard calibration curve. (<b>b</b>) The dependence of optical density on radiation dose.</p> Full article ">Figure 4
<p>Simulated spectra of laser accelerated: (<b>a</b>) protons; (<b>b</b>) electrons beams and (<b>c</b>) emitted photons at different angles within 0–15 degrees range. 0° is considered in forward direction normal to target.</p> Full article ">Figure 5
<p>Simulated spectra of laser accelerated: (<b>a</b>) electron and (<b>b</b>) photon beams at different angles.</p> Full article ">Figure 6
<p>Angular dependence of particle fluencies/cm<sup>2</sup> at 3 cm from the interaction point on logarithmic scale: (<b>a</b>) electrons (<b>b</b>) protons (<b>c</b>) photons and (<b>d</b>) total fluencies.</p> Full article ">Figure 6 Cont.
<p>Angular dependence of particle fluencies/cm<sup>2</sup> at 3 cm from the interaction point on logarithmic scale: (<b>a</b>) electrons (<b>b</b>) protons (<b>c</b>) photons and (<b>d</b>) total fluencies.</p> Full article ">Figure 7
<p>(<b>a</b>) Map of the measured dose (blue dots) inside the target chamber and of the simulated radiation dose (red dots); (<b>b</b>) The fluctuations in the angular distribution of measured and simulated dose.</p> Full article ">
13 pages, 4145 KiB  
Article
Experimental Characterization of Plasmonic Sensors Based on Lab-Built Tapered Plastic Optical Fibers
by Nunzio Cennamo, Francesco Arcadio, Aldo Minardo, Domenico Montemurro and Luigi Zeni
Appl. Sci. 2020, 10(12), 4389; https://doi.org/10.3390/app10124389 - 26 Jun 2020
Cited by 27 | Viewed by 2829
Abstract
In this work, we have compared several configurations of surface plasmon resonance (SPR) sensors based on D-shaped tapered plastic optical fibers (TPOFs). Particularly, the TPOFs used to obtain the SPR sensors are made by a lab-built system based on two motorized linear positioning [...] Read more.
In this work, we have compared several configurations of surface plasmon resonance (SPR) sensors based on D-shaped tapered plastic optical fibers (TPOFs). Particularly, the TPOFs used to obtain the SPR sensors are made by a lab-built system based on two motorized linear positioning stages and a heating plate. Preliminarily, a comparative analysis has been carried out between two different configurations, one with and one without a thin buffer layer deposited between the core of TPOFs and the gold film. After this preliminary step, we have used the simpler configuration, obtained without the buffer layer, to realize different SPR D-shaped TPOF sensors. This study could be of interest in SPR D-shaped multimode plastic optical fiber (POF) sensors because, without the tapers, the performances decrease when the POF’s diameter decreases, whereas the performances improve in SPR D-shaped tapered POF sensors, where the diameter decreases in the D-shaped sensing area. The performances of the SPR sensors based on different taper ratios have been analyzed and compared. The SPR-TPOF sensors have been tested using water–glycerin mixtures with refractive indices ranging from 1.332 to 1.381 RIU. According to the theory, the experimental results have demonstrated that, as the taper ratio increases, the sensitivity of the SPR sensor increases as well, while on the contrary the signal-to-noise ratio (SNR) decreases. Full article
(This article belongs to the Special Issue World of Biosensing)
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<p>Schematic lab-built system used to realize tapered plastic optical fibers (TPOFs).</p> Full article ">Figure 2
<p>Top view of a TPOF made by lab-built system.</p> Full article ">Figure 3
<p>Surface plasmon resonance (SPR)-TPOF sensors: (<b>a</b>) configuration with buffer layer and (<b>b</b>) configuration without buffer layer.</p> Full article ">Figure 4
<p>Experimental setup used to test SPR-TPOF sensors.</p> Full article ">Figure 5
<p>SPR spectra, normalized to reference spectrum, of (<b>a</b>) SPR-TPOF configuration with buffer layer and (<b>b</b>) without buffer layer. (<b>c</b>) Resonance wavelength variations (Δλ) with respect to the water (1.332) vs. the refractive index for the configurations with and without buffer layer, along with the linear fitting to the data and with the error bars. Linear fitting equation for the configuration with buffer layer: Δλ = 1801.8 n − 2414.5 (R<sup>2</sup> = 0.95). Linear fitting equation for the configuration without buffer layer: Δλ = 1774.2 n − 2336.3 (R<sup>2</sup> = 0.97).</p> Full article ">Figure 5 Cont.
<p>SPR spectra, normalized to reference spectrum, of (<b>a</b>) SPR-TPOF configuration with buffer layer and (<b>b</b>) without buffer layer. (<b>c</b>) Resonance wavelength variations (Δλ) with respect to the water (1.332) vs. the refractive index for the configurations with and without buffer layer, along with the linear fitting to the data and with the error bars. Linear fitting equation for the configuration with buffer layer: Δλ = 1801.8 n − 2414.5 (R<sup>2</sup> = 0.95). Linear fitting equation for the configuration without buffer layer: Δλ = 1774.2 n − 2336.3 (R<sup>2</sup> = 0.97).</p> Full article ">Figure 6
<p>SPR-TPOF sensor images acquired from a digital microscope. (<b>a</b>) Configuration “A”, taper ratio 1.3; (<b>b</b>) configuration “B”, taper ratio 1.4; (<b>c</b>) configuration “C”, taper ratio 1.5.</p> Full article ">Figure 7
<p>SPR spectra, normalized to reference spectrum, of (<b>a</b>) configuration “A”, (<b>b</b>) configuration “B”, and (<b>c</b>) configuration “C”. (<b>d</b>) Plasmon resonance wavelength variation (Δλ) with respect to water (1.332) as a function of the refractive index, along with quadratic fitting to the data, for each configuration.</p> Full article ">Figure 8
<p>Sensitivity as a function of refractive index for all SPR-TPOF sensor configurations.</p> Full article ">Figure 9
<p>(<b>a</b>) Full width at half maximum (FWHM) and (<b>b</b>) signal-to-noise ratio (SNR) as a function of refractive index for all SPR-TPOF sensor configurations.</p> Full article ">
5 pages, 173 KiB  
Editorial
Recent Trends in DC and Hybrid Microgrids: Opportunities from Renewables Sources, Battery Energy Storages and Bi-Directional Converters
by Sergio Saponara, Roberto Saletti and Lucian Mihet-Popa
Appl. Sci. 2020, 10(12), 4388; https://doi.org/10.3390/app10124388 - 26 Jun 2020
Cited by 6 | Viewed by 2819
Abstract
This editorial manuscript reviews the papers accepted for publication in the Special Issue “DC & Hybrid Microgrids” of Applied Sciences. This Special Issue, co-organized by the University of Pisa, Italy and Østfold University College in Norway, has collected nine papers from 25 [...] Read more.
This editorial manuscript reviews the papers accepted for publication in the Special Issue “DC & Hybrid Microgrids” of Applied Sciences. This Special Issue, co-organized by the University of Pisa, Italy and Østfold University College in Norway, has collected nine papers from 25 submitted, with authors from Asia, North America and Europe. The published articles provide an overview of the most recent research advances in direct current (DC) and hybrid microgrids, exploiting the opportunities offered by the use of renewable energy sources, battery energy storage systems, power converters, innovative control and energy management strategies. Full article
(This article belongs to the Special Issue DC & Hybrid Micro-Grids)
28 pages, 5808 KiB  
Article
Design and Implementation of an IoT-Oriented Strain Smart Sensor with Exploratory Capabilities on Energy Harvesting and Magnetorheological Elastomer Transducers
by Jorge de-J. Lozoya-Santos, L. C. Félix-Herrán, Juan C. Tudón-Martínez, Adriana Vargas-Martinez and Ricardo A. Ramirez-Mendoza
Appl. Sci. 2020, 10(12), 4387; https://doi.org/10.3390/app10124387 - 26 Jun 2020
Cited by 9 | Viewed by 3047
Abstract
This work designed and implemented a new low-cost, Internet of Things-oriented, wireless smart sensor prototype to measure mechanical strain. The research effort explores the use of smart materials as transducers, e.g., a magnetorheological elastomer as an electrical-resistance sensor, and a cantilever beam with [...] Read more.
This work designed and implemented a new low-cost, Internet of Things-oriented, wireless smart sensor prototype to measure mechanical strain. The research effort explores the use of smart materials as transducers, e.g., a magnetorheological elastomer as an electrical-resistance sensor, and a cantilever beam with piezoelectric sensors to harvest energy from vibrations. The study includes subsequent and validated results with a magnetorheological elastomer transducer that contained multiwall carbon nanotubes with iron particles, generated voltage tests from an energy-harvesting system that functions with an array of piezoelectric sensors embedded in a rubber-based cantilever beam, wireless communication to send data from the sensor’s central processing unit towards a website that displays and stores the handled data, and an integrated manufactured prototype. Experiments showed that electrical-resistivity variation versus measured strain, and the voltage-generation capability from vibrations have the potential to be employed in smart sensors that could be integrated into commercial solutions to measure strain in automotive and aircraft systems, and civil structures. The reported experiments included cloud-computing capabilities towards a potential Internet of Things application of the smart sensor in the context of monitoring automotive-chassis vibrations and airfoil damage for further analysis and diagnostics, and in general structural-health-monitoring applications. Full article
(This article belongs to the Special Issue New Sensors for Nondestructive Evaluation)
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<p>Schematic of the designed and implemented smart sensor. Red text—separately tested subsystems with preliminary results.</p> Full article ">Figure 2
<p>Computer-aided-design (CAD) magnetorheological-elastomer (MRE) mold with 3 layers: floor, middle, and top.</p> Full article ">Figure 3
<p>CAD images of three implemented MRE patterns. (<b>a</b>) Horizontal array with 4 lines; (<b>b</b>) 3-line array with 90<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> and 45<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> between them; (<b>c</b>) 4-line array with 90<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> between them. Figures taken from [<a href="#B39-applsci-10-04387" class="html-bibr">39</a>].</p> Full article ">Figure 4
<p>Examples of two generated MR elastomers with 90% silicone rubber and 10% catalyst. Depicted patterns are (<b>a</b>) 3-line array with 90<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> and 45<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> between them, and (<b>b</b>) 4-line array with 90<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> between them.</p> Full article ">Figure 5
<p>Manufactured 2-layer MR elastomer with nanoparticles.</p> Full article ">Figure 6
<p>Sequence of experiments during the manufacture of the MRE.</p> Full article ">Figure 7
<p>(<b>a</b>) Exploded isometric view of computer-aided design (CAD). (<b>b</b>) Prototype with all the signal-conditioning elements.</p> Full article ">Figure 8
<p>Central processing unit (CPU) subsystem and communication interfaces. (<b>a</b>) Isometric CAD view; (<b>b</b>) manufactured circuit.</p> Full article ">Figure 9
<p>From measurement reading and local processing to online data visualization; dashboard available in the cloud for users.</p> Full article ">Figure 10
<p>Two implemented clay molds for rubber plates.</p> Full article ">Figure 11
<p>(<b>a</b>) mold filled with rubber-based mixture; (<b>b</b>) MEAS piezovibration sensor inside an empty mold.</p> Full article ">Figure 12
<p>Preliminary circuit to store and deliver harvested energy.</p> Full article ">Figure 13
<p>Improved circuit to store and deliver energy.</p> Full article ">Figure 14
<p>Energy-harvesting subsystem. (<b>a</b>) CAD drawing; (<b>b</b>) manufactured subsystem with protective case.</p> Full article ">Figure 15
<p>Complete manufactured smart-sensor prototype.</p> Full article ">Figure 16
<p>Laboratory setup for MRE tensile tests.</p> Full article ">Figure 17
<p>Laboratory setup for compression tests. Figures modified from [<a href="#B40-applsci-10-04387" class="html-bibr">40</a>].</p> Full article ">Figure 18
<p>Experimental setup for energy harvesting: rubber plate and vibrating testing machine at the lab. Figure taken from [<a href="#B39-applsci-10-04387" class="html-bibr">39</a>].</p> Full article ">Figure 19
<p>Google spreadsheet with 11 readings (stored in the cloud) coming from the smart strain sensor.</p> Full article ">Figure 20
<p>Graph settings for the implemented dashboard. Figure modified from [<a href="#B39-applsci-10-04387" class="html-bibr">39</a>].</p> Full article ">Figure 21
<p>Tensile tests. Electrical resistance vs applied force for three manufactured MRE transducers shown in <a href="#applsci-10-04387-f003" class="html-fig">Figure 3</a>. For each transducer, two tests were carried out (Replicas 1 and 2). (<b>a</b>) Four lines in parallel; (<b>b</b>) four lines in 90°; and (<b>c</b>) three lines in 90°/45°.</p> Full article ">Figure 22
<p>Compression tests. Electrical resistance vs applied force for three manufactured MRE transducers shown in <a href="#applsci-10-04387-f003" class="html-fig">Figure 3</a>. Two tests (Replicas 1 and 2) were carried out for each transducer. (<b>a</b>) Four lines in parallel; (<b>b</b>) four lines in 90°; and (<b>c</b>) three lines in 90°/45°.</p> Full article ">Figure 23
<p>Generated voltage for different cantilever-beam lengths. Three replicas were applied for each test (length and frequency), and 500 measurements were gathered per tested frequency: (<b>a</b>) 6.5 cm; (<b>b</b>) 7.0 cm; (<b>c</b>) 7.5 cm; and (<b>d</b>) 8.0 cm.</p> Full article ">Figure 24
<p>Energy harvesting for Replica 1. The maximum generated voltage was 1.625 V.</p> Full article ">Figure 25
<p>Energy harvesting for Replica 2. The maximum generated voltage was 1.621 V.</p> Full article ">Figure 26
<p>Energy harvesting for Replica 3. The maximum generated voltage was 1.487 V.</p> Full article ">Figure 27
<p>Energy-harvesting tests for the implemented piezoelectric system. (<b>a</b>) Replica 1; (<b>b</b>) Replica 2; and (<b>c</b>) Replica 3.</p> Full article ">Figure 28
<p>Measured output voltage (Vout) for a 7.5 cm length cantilever beam with three piezoelectric sensors, as shown in <a href="#applsci-10-04387-f013" class="html-fig">Figure 13</a>. Three samples were applied for each test (length and frequency). Small black numbers, accumulated voltage measurements.</p> Full article ">Figure 29
<p>Rectangular rosette 0°/45°/90° strain-gauge array; model: SGD-6/350-RYT21; 350 Ω of nominal resistance, 16.3 mm per side with a gauge factor of 2.13.</p> Full article ">
20 pages, 373 KiB  
Article
A Polarity Capturing Sphere for Word to Vector Representation
by Sandra Rizkallah, Amir F. Atiya and Samir Shaheen
Appl. Sci. 2020, 10(12), 4386; https://doi.org/10.3390/app10124386 - 26 Jun 2020
Cited by 8 | Viewed by 3856
Abstract
Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications. Distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing [...] Read more.
Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications. Distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing word embedding methods is that they often fail to distinguish between synonymous, antonymous, and unrelated word pairs. Meanwhile, polarity detection is crucial for applications such as sentiment analysis. In this work we propose an embedding approach that is designed to capture the polarity issue. The approach is based on embedding the word vectors into a sphere, whereby the dot product between any vectors represents the similarity. Vectors corresponding to synonymous words would be close to each other on the sphere, while a word and its antonym would lie at opposite poles of the sphere. The approach used to design the vectors is a simple relaxation algorithm. The proposed word embedding is successful in distinguishing between synonyms, antonyms, and unrelated word pairs. It achieves results that are better than those of some of the state-of-the-art techniques and competes well with the others. Full article
(This article belongs to the Special Issue Natural Language Processing: Emerging Neural Approaches and Applications)
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<p>Semi-supervised illustration.</p> Full article ">Figure 2
<p>Convergence.</p> Full article ">Figure 3
<p>Histogram for the degree of vertices.</p> Full article ">
11 pages, 3451 KiB  
Article
Three-Dimensional Analysis of Root Anatomy and Root Canal Curvature in Mandibular Incisors Using Micro-Computed Tomography with Novel Software
by JongKi Lee, Shin-Hoon Lee, Jong-Rak Hong, Kee-Yeon Kum, Soram Oh, Adel Saeed Al-Ghamdi, Fawzi Ali Al-Ghamdi, Ayman Omar Mandorah, Ji-Hyun Jang and Seok Woo Chang
Appl. Sci. 2020, 10(12), 4385; https://doi.org/10.3390/app10124385 - 26 Jun 2020
Cited by 2 | Viewed by 2924
Abstract
Root canal treatment of mandibular incisor is difficult because of the narrow pulp space and apical curvature. The aim of this study was to measure the anatomical indicators of the mandibular incisors in Koreans using micro-computed tomography (MCT) with novel software (Kappa 2). [...] Read more.
Root canal treatment of mandibular incisor is difficult because of the narrow pulp space and apical curvature. The aim of this study was to measure the anatomical indicators of the mandibular incisors in Koreans using micro-computed tomography (MCT) with novel software (Kappa 2). The MCT-scanned data from 27 mandibular incisors were reconstructed and analyzed. For each canal, 3-dimensional (3D) surface models were re-sliced at 0.1 mm intervals perpendicular to the central axis of the root canal. Root canal width, dentine thickness, and direction and degree of root canal curvatures were measured automatically on each slice. Measurements were analyzed statistically with Bhapkar test, Friedman test, and Wilcoxon signed rank test. Labial and lingual dentine thicknesses were significantly larger than mesial and distal thicknesses (p < 0.001). The thinnest dentine was mainly located on the mesio-lingual side of the canals in the apical third. The mean narrowest and widest canal width in the apical sixth were 0.22 mm and 0.40 mm, respectively. The canal curvature abruptly increased in the apical 0.5-mm portion. MCT with novel software provided useful anatomical information for root canal instrumentation. Full article
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<p>Slice of reconstructed image. (<b>A</b>) When sliced perpendicular to the long axis of the root canal, no distortions are produced. (<b>B</b>) When sliced perpendicular to the long axis of the tooth, distortions are produced in the images of the root canal and surrounding dentin.</p> Full article ">Figure 2
<p>Overall process of the automatic measurement of root and canal dimensions. From the MCT images of mandibular incisors, 3D surface models of root canals were constructed and the central axis was plotted using V works 4.0; Cybermed, Seoul, Korea. Subsequently, surface models were sectioned perpendicular to the canal axis (at 0.1 mm intervals). Pre-defined anatomic parameters were computed in each section.</p> Full article ">Figure 3
<p>Graphical visualization of the measurements. Left image is the representative 3D root model, and the right image is its section; <b>①</b> the central axis (red curve) of the canal; <b>②</b> the root canal; <b>③</b> root outline; <b>④</b> labial dentin thickness; <b>⑤</b> lingual dentin thickness; <b>⑥</b> mesial dentin thickness; <b>⑦</b> distal dentin thickness; <b>⑧</b> the thinnest dentin thickness and its direction.</p> Full article ">Figure 4
<p>Axial level of the root canal</p> Full article ">Figure 5
<p>Measurements of dentin thicknesses. The average dentin thickness was measured on the mesial, distal, labial, and lingual sides, and the thinnest dentin thickness (MinDist), narrowest canal width (Canal width (min)), and the widest canal width (Canal width (max)) were plotted along the entire length of the canals.</p> Full article ">Figure 6
<p>Average dentin thickness (mm) in mesial, distal, labial, and lingual directions.</p> Full article ">Figure 7
<p>The thinnest dentin thickness (mm).</p> Full article ">Figure 8
<p>The relative frequency (%) of the directions with the thinnest dentin. S1: apical third; S2: middle third; S3: coronal third.</p> Full article ">Figure 9
<p>The narrowest canal width (mm).</p> Full article ">Figure 10
<p>The widest canal width (mm).</p> Full article ">Figure 11
<p>Average degree of canal curvatures (mm<sup>−1</sup>).</p> Full article ">
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