Search Results (27)

Compared to base aluminum alloys, the surface composites of aluminum alloys are more widely used in the automotive, aerospace, and other industries. The ability to yield enhanced physical properties and a smoother microstructure has made friction stir processing (FSP) the method of choice for developing aluminum-based surface composites in recent times. In this work, the Goal Programming (GP) approach is adopted for the Multi-Objective Optimization of FSP processes with three Artificial Intelligence (AI)-based metaheuristics, viz., Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO). Three parameters, copper percentage (Cu%), graphite percentage (Gr%), and number of passes, are considered, and multi-factor non-linear regression prediction models are developed for the three responses, Tool Vibrations, Power Consumption, and Cutting Force. The TLBO algorithm outperformed the ABC and PSO algorithms in terms of solution quality and robustness, yielding significant improvements in tool life. The results with TLBO were improved by 20% and 14% compared to the PSO and ABC algorithms, respectively. This proves that the TLBO algorithm performed better compared with the ABC and PSO algorithms. However, the computation time required for the TLBO algorithm is higher compared to the ABC and PSO algorithms. This work has opened new avenues towards applying the GP approach for the Multi-Objective Optimization of FSP tools with composite parameters. This is a significant step towards toll life improvement for the FSP of composite alloys, contributing to sustainable manufacturing. Full article

In the typical vibration signal of a reciprocating air compressor, multi-source nonlinear characteristics are exhibited and are often drowned out in background noise, which leads to a lack of robustness in traditional feature analysis methods and difficulty in effective extraction. To address this issue, an algorithm based on ABC-SGMD is proposed in this paper. The Symplectic Geometry Mode Decomposition (SGMD), which is optimized with the Artificial Bee Colony algorithm (ABC), is utilized to decompose the signal, and a multi-feature fusion model is constructed for fault feature extraction. The extracted features are then input into the Self-Adaptive Evolutionary Extreme Learning Machine (SaDE-ELM), and a fault diagnosis model based on ABC-SGMD and SaDE-ELM is established. Ultimately, the signals of reciprocating air compressors and experimental data are used to demonstrate the applicability of the method. The results manifest that this framework has superiority in handling nonlinear and non-stationary signals. Full article

The Internet of Things (IoT) is a system that enables wirelessly linked devices to be tracked and managed remotely. It uses Ethernet protocols and the principles behind wireless sensor networks. Soil moisture monitoring, hydraulic pressure monitoring, soil testing, preventing trespassing through motion detection, and conserving energy are only some of the agricultural and irrigational operations that are the subject of this research. The implementation shown in this work breaks down larger systems into several smaller ones. A subsystem incorporates a vibration warning sensor, pump, and the ability to monitor soil moisture and hydraulic pressure to detect movement in and around the associated field. The second method will be utilized to deter intruders by picking up on their presence when they move within range of the necessary field barrier. Sensors for measuring current and voltage will be included for energy management regulation. It will be utilized for controlling the system. The main system will receive data through ZigBee from the first and second subsystems, monitor them, and then transfer them to the network router via ZigBee, where the necessary data will be shown on a website home page alongside the appropriate Ethernet protocols and current operating data. Full article

Sensor technologies have sufficiently advanced to provide low-cost devices that can quantify carbon dioxide levels in honeybee hives with high temporal resolution and in a small enough package for hive deployment. Recent publications have shown that summer carbon dioxide levels vary throughout the day and night over ranges that typically exceed 5000 ppm. Such dramatic changes in a measurable parameter associated with bee physiology are likely to convey information about the colony health. In this work, we present data from four UK-based hives collected through the winter of 2022/2023, with a focus on seeing if carbon dioxide can indicate when colonies are at risk of failure. These hives have been fitted with two Sensirion SCD41 photoacoustic non-dispersive infrared (NDIR) carbon dioxide sensors, one in the queen excluder, at the top of the brood box, and one in the crown board, at the top of the hive. Hive scales have been used to monitor the hive mass, and internal and external temperature sensors have been included. Embedded accelerometers in the central frame of the brood box have been used to measure vibrations. Data showed that the high daily variation in carbon dioxide continued throughout the coldest days of winter, and the vibrational data suggested that daily fanning may be responsible for restoring lower carbon dioxide levels. The process of fanning will draw in colder air to the hive at a time when the bees should be using their energy to maintain the colony temperature. Monitoring carbon dioxide may provide feedback, prompting human intervention when the colony is close to collapse, and a better understanding may contribute to discussions on future hive design. Full article

The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method that uses the optimal mode component as the input feature. The vibration signal is first decomposed by variational mode decomposition (VMD) based on the optimal parameters searched by the artificial bee colony (ABC). Moreover, the key components are screened using an evaluation function that is a fusion of the arrangement entropy, the signal-to-noise ratio, and the power spectral density weighting. The Stockwell transform is then used to convert the filtered modal components into time–frequency images. Finally, the MBConv quantity and activation function of the EfficientNet network are optimized, and the time–frequency pictures are imported into the optimized network model for fault diagnosis. The comparative experiments show that the proposed method accurately extracts the optimal modal component and has a fault classification accuracy greater than 98%. Full article

This paper presents the design and implementation of a versatile IoT testbed utilizing the openHAB platform, along with various wireless interfaces, including Z-Wave, ZigBee, Wi-Fi, 4G-LTE (Long-Term Evolution), and IR (Infrared Radiation), and an array of sensors for motion, temperature, luminance, humidity, vibration, UV (ultraviolet), and energy consumption. First, the testbed architecture, setup, basic testing, and collected data results are described. Then, by showcasing a typical day in the laboratory, we illustrate the testbed’s potential through the collection and analysis of data from multiple sensors. The study also explores the capabilities of the openHAB platform, including its robust persistence layer, event management, real-time monitoring, and customization. The significance of the testbed in enhancing data collection methodologies for energy assets and unlocking new possibilities in the realm of IoT technologies is particularly highlighted. Full article

An optimization design of the bending-vibration resistance of magnetorheological elastomer carbon fibre reinforced polymer sandwich sheets (MECFRPSSs) was studied in this paper. Initially, by adopting the classical laminate theory, the Reddy’s high-order shear deformation theory, the Rayleigh-Ritz method, etc., an analytical model of the MECFRPSSs was established to predict both bending and vibration parameters, with the three-point bending forces and a pulse load being considered separately. After the validation of the model was completed, the optimization design work of the MECFRPSSs was conducted based on an optimization model developed, in which the thickness, modulus, and density ratios of magnetorheological elastomer core to carbon fibre reinforced polymer were taken as design variables, and static bending stiffness, the averaged damping, and dynamic stiffness parameters were chosen as objective functions. Subsequently, an artificial bee colony algorithm was adopted to execute single-objective, dual-objective, and multi-objective optimizations to obtain the optimal design parameters of such structures, with the convergence effectiveness being examined in a validation example. It was found that it was hard to improve the bending, damping, and dynamic stiffness behaviours of the structure simultaneously as the values of design variables increased. Some compromised results of design parameters need to be determined, which are based on Pareto-optimal solutions. In further engineering application of the MECFRPSSs, it is suggested to use the corresponding design parameters related to a turning point to better exert their bending-vibration resistance. Full article

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers. Full article

In underground mines, various mining activities may generate dust or vibrations, affecting workers’ health and safety. Therefore, for worker safety, we must monitor the environment and identify possible risks. However, it is difficult to install multiple sensors and acquire data simultaneously because of the difficulties of connecting to an external network in underground mines. This study developed a digital device to share acquired data by combining ZigBee communication technology with an accelerometer and dust sensor. In total, 29 vibration modules, 14 dust modules, and 2 coordinator modules were installed at Taeyoung EMC’s Samdo Mine in Samcheok, Republic of Korea. Because of its application, we could detect changes in vibration and dust before and after blasting. The dust density of the devices close to the blasting point increases rapidly up to about 230 µg/m³ and then decreases to about 180 µg/m³, and the dust density of the devices further increases over time. The dust density was usually maintained at a value of about 100 to 150 µg/m³ before blasting. The spatial distribution of the dust density of multiple devices was visualized using ArcGIS Pro. Although the wireless sensor network is well-established, some modules were temporarily disconnected from the network. In order to solve the problem of unstable network connection in some modules, change of network settings and line of sight analysis are required. Improvements in the technology developed in this study may help prevent potential hazards in underground mines. Full article

To improve the seeding qualification rate and stability of the air-suction vibrating disc type seed meter on the rice seedling raising line, in this paper, an improved wireless sensor network node layout optimization algorithm was proposed, and the operation monitoring system of the seed meter was designed using the Internet of Things and configuration software. In the system, the upper computer software adopted the Kingview software, the lower computer took the STM32F429IGT and CC2530 as the core controllers, and ZigBee was selected for data transmission to build the wireless sensor network. The acquisition of field status information and the sending of control instructions were realized through the sensor nodes constructed by the CC2530 core controller. The data was sent to the coordinator node in real-time through the wireless sensor network. The coordinator node realized the bidirectional transmission of data with Kingview and the upper computer control instructions forwarding using the ASCII protocol The host computer monitoring and management software was developed based on configuration software to realize real-time data monitoring, access database storage, fault alarm, control command sending and other functions. The experimental results showed that the detection accuracy of the system for the seeding amount and missed seeding amount was 94.3% and 95.6%, respectively, which could realize the monitoring of the primary working status of the seed meter. The system realized effective data transmission and data remote wireless transmission function, which provided sufficient theoretical and data support for the performance optimization of the seed meter, and laid a good foundation for the visualization and intelligence of information data. Full article

Bumblebee pollination is crucial to the production of tomato in protected cultivation. Both tomato yield and flavor play important roles in attracting attentions from growers and consumers. Compared with yield, much less work has been conducted to investigate whether and how pollination methods affect tomato flavor. In this study, the effects of bumblebee pollination, vibrator treatment, and plant growth regulator (PGR) treatment on tomato yield and flavor were tested in Gobi Desert greenhouses. Compared with vibrator or PGR treatments, bumblebee pollinated tomato had higher and more stable fruit set, heavier fruit weight, and more seed. We also found that the seed quantity positively correlated with fruit weight in both bumblebee pollinated, and vibrator treated tomato, but not in PGR treated tomato. Besides enhancing yield, bumblebee pollination improved tomato flavor. Bumblebee pollinated tomato fruits contained more fructose and glucose, but less sucrose, citric acid, and malic acid. Furthermore, the volatile organic compounds of bumblebee pollinated tomato were distinctive with vibrator or PGR treated tomato, and more consumer liking related compounds were identified in bumblebee pollinated tomato. Our findings provide new insights into the contributions of bee pollinator towards improving crop yield and quality, emphasizing the importance of bumblebee for tomato pollination. Full article

Piezoelectric actuators (PEAs), as a smart material with excellent characteristics, are increasingly used in high-precision and high-speed nano-positioning systems. Different from the usual positioning control or fixed frequency tracking control, the more accurate rate-dependent PEA nonlinear model is needed in random signal dynamic tracking control systems such as active vibration control. In response to this problem, this paper proposes a Hammerstein model based on fractional order rate correlation. The improved Bouc-Wen model is used to describe the asymmetric hysteresis characteristics of PEA, and the fractional order model is used to describe the dynamic characteristics of PEA. The nonlinear rate-dependent hysteresis model can be used to accurately describe the dynamic characteristics of PEA. Compared with the integer order model or linear autoregressive model to describe the dynamic characteristics of the PEA Hammerstein model, the modeling accuracy is higher. Moreover, an artificial bee colony algorithm (DE-ABC) based on differential evolution was proposed to identify model parameters. By adding the mutation strategy and chaos search of the genetic algorithm into the previous ABC, the convergence speed of the algorithm is faster and the identification accuracy is higher, and the simultaneous identification of order and coefficient of the fractional model is realized. Finally, by comparing the simulation and experimental data of multiple sets of sinusoidal excitation with different frequencies, the effectiveness of the proposed modeling method and the accuracy and rapidity of the identification algorithm are verified. The results show that, in the wide frequency range of 1–100 Hz, the proposed method can obtain more accurate rate-correlation models than the Bouc-Wen model, the Hammerstein model based on integer order or the linear autoregressive model to describe dynamic characteristics. The maximum error (Max error) is 0.0915 μm, and the maximum mean square error (RMSE) is 0.0244. Full article

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model. Full article

Buzz pollination is a specialized pollination syndrome that requires vibrational energy to extract concealed pollen grains from poricidal anthers. Although a large body of work has examined the ecology of buzz pollination, whether acoustic properties of buzz pollinators affect pollen extraction is less understood, especially in weeds and invasive species. We examined the pollination biology of Silverleaf nightshade (Solanum elaeagnifolium), a worldwide invasive weed, in its native range in the Lower Rio Grande Valley (LRGV) in south Texas. Over two years, we documented the floral visitors on S. elaeagnifolium, their acoustic parameters (buzzing amplitude, frequency, and duration of buzzing) and estimated the effects of the latter two factors on pollen extraction. We found five major bee genera: Exomalopsis, Halictus, Megachile, Bombus, and Xylocopa, as the most common floral visitors on S. elaeagnifolium in the LRGV. Bee genera varied in their duration of total buzzing time, duration of each visit, and mass. While we did not find any significant differences in buzzing frequency among different genera, an artificial pollen collection experiment using an electric toothbrush showed that the amount of pollen extracted is significantly affected by the duration of buzzing. We conclude that regardless of buzzing frequency, buzzing duration is the most critical factor in pollen removal in this species. Full article

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe. From the observed results, it can be seen that the unsupervised method does not require previous calibration of target dimensions, histogram, and distances involved in positioning. The algorithm showed less sensitivity in the extraction of sharpness characteristics regarding the number of edges and greater sensitivity to the gradient, allowing unforeseen operation scenarios, even in small sharpness variations, and robust response to variance local, temporal, and geophysical of the magnetic declination, not needing luminosity after scanning, with the two freedom of degrees of the rotation. Full article