Electronics

This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show that the proposed enhanced PSO algorithm has better convergence speed and better performance in the execution of workflow tasks. Full article

WESPs (Wet Electrostatic precipitators) are mainly installed in industries and factories where PM (particulate matter) is primarily generated. Such a wet type WESPs exhibits very excellent performance by showing a PM collection efficiency of 97 to 99%, but the PM collection efficiency may decrease rapidly due to a situation in which the dust collector and the discharge electrode is corroded by water. Thus, developing technology to predict efficient PM collection in the design and operation of WESPs is critical. Previous studies have mainly developed machine learning-based models to predict atmospheric PM concentrations using data measured by meteorological agencies. However, the analysis of models for predicting the dust collection efficiency of WESPs installed in factories and industrial facilities is insufficient. In this study, a WESPs was installed, and PM collection experiments were conducted. Nonlinear data such as operating conditions and PM measurements were collected, and ensemble PM collection efficiency prediction models were developed. According to the research results, the random forest model yielded excellent performance, with the best results achieved when the target was PM 7: R2, MAE, and MSE scores of 0.956, 0.747, and 1.748, respectively. Full article

Radiofrequency (RF) surface coils are extensively used as receivers in magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) systems thanks to their high signal-to-noise ratio (SNR). For specific magnetic resonance applications, the design of dedicated RF surface coils with a transverse (to the coil’s plane) RF magnetic field pattern can be necessary. Such transverse-field RF coils are constituted by several central linear (parallel or crossing) conductor elements connected by return current paths. Typically, the outer shape of such RF coils is circular or squared, although other geometries can be used. This paper describes the implementation and validation of a transverse-field RF surface coil simulator based on magnetostatic analysis, which permits the design and optimization of square butterfly and figure-of-eight RF coils with adjustable size and mutual distance between the central linear current elements. The simulation results, compared with the ones provided by a standard square loop RF coil, were validated with 100 MHz workbench measurements performed on three home-built prototypes. Finally, two novel quadrature RF coil structures designed by overlapping two orthogonal square butterfly and figure-of-eight RF coils were simulated and theoretically characterized. The RF coils described here should be suitable for a wide range of MRI/MRS preclinical/clinical applications, mainly at fields below 3 T. Full article

Electrostatic discharge (ESD) is a physical phenomenon that may destroy electronic components due to its high discharge current that may reach a few amperes in just a few ns. However, another major aspect of ESD is the related high-frequency electromagnetic (E/M) fields radiated by the ESD event. The electronic equipment that is affected by the ESD phenomenon is additionally affected by the induced voltages caused by these E/M fields. This is the reason that the current version of the IEC 61000-4-2 on ESD has a special reference to these fields and the measurement setup. Starting with the classical formulation of these fields, this paper reviews the most popular techniques for calculating the ESD electromagnetic fields while also emphasizing the best methods for minimizing computational effort. There is also a separate section for the measurement techniques that have been applied in research works, whose outcomes could be implemented in the next revision of the IEC 61000-4-2. It is extremely important for the next revision to include these measurement setups and the E/M field sensors because the ESD generators should comply with certain values related to the E/M fields they produce. Full article

VR has shown positive growth in the world in recent years, which is mainly due to projects such as learning, games, entertainment and experiential activities. VR has changed the way of life of users, providing users with more interesting interactions and immersive experiences. This study aims to investigate students’ practical capabilities and learning effectiveness under the instruction strategy of integrating virtual reality into simulation games into the Vehicle Body Electrical System Comprehensive Maintenance and Repair Services Practice curriculum for students of the Dept. of Auto Mechanics in a skills-based senior high school. Two student classes of the Dept. of Auto Mechanics major in Electrical Engineering featuring practical subjects in one skills-based senior high school in central Taiwan were chosen as the participants for this study. By way of pretest–post-test research design and heterogeneous grouping, an 8-week instruction experiment was conducted in which ZPD (zone of proximal development) instruction strategies were used in the experimental group (with 43 persons), while traditional didactic instruction strategies were used in the control group (with 36 persons). ZPD instructional strategies analyze and collect quantitative and qualitative data to investigate the instructional effectiveness and feasibility in developing ZPD as the research material in the practical curriculum for the study area of the Power Machinery in Vehicle Body Electrical System Comprehensive Maintenance and Repair Services practice. According to the research objective, the results are concluded as follows. (1) Students achieved the best learning effectiveness when adopting ZPD (zone of proximal development) strategies in which virtual reality was integrated into the vehicle charging and starting system to investigate students’ automotive diagnostic troubleshooting and fault-clearing capabilities. (2) Students attained the highest acceptance in learning phenomenon when adopting ZPD (zone of proximal development) strategies in which virtual reality was integrated into students’ familiar practice factory environment and the tools and equipment operation process. (3) Students had a higher acceptance of learning effectiveness when using virtual reality simulation games in the disassembly and functional detection of vehicle charging and starting systems. (4) There is a positive effect when integrating virtual reality simulation games into ZPD instruction strategies in the knowledge, skills and attitude on students’ overall inquiry practical capabilities and their learning effectiveness. Full article

With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results. Full article

As an essential medical device, a bag infusion set is often used for intravenous infusion, and an infusion bag is an essential part of the bag infusion set. Due to the unavoidable defects in the production process, quality detection of infusion bags is critical to ensure the use quality of the infusion set. In this paper, we adopt a machine vision system to inspect the assembly quality of the lanyard and dosing interface of liquid bag assembly and conduct in-depth discussion and research from image acquisition, a defect detection strategy, and a defect detection algorithm of a vision system for two defects of lanyard missing and dosing interface missing. The design of the image acquisition auxiliary mechanism is realized to solve the complex problem of image acquisition due to the irregular shape of the liquid bag assembly; based on determining the defect detection strategy, the algorithm study of contour extraction is finally completed through comparison experiments to extract a precise contour of the liquid bag piping area; finally, the virtual straight line method is proposed and combined with the ROI selected according to the position feature of the outer rectangle of the contour in this paper, the count of the number of contours is completed, and the defect detection goal is finally achieved. The pipeline defect detection rate of the method proposed in this paper reaches 100%, which can perfectly replace the existing manual visual inspection and reduce the employment cost of enterprises. Full article

The objective of this work is to propose the development of a virtual electronics laboratory with an aeronautical radar simulator using immersive technologies to help students learn. To verify whether this proposal was viable, the systematic literature review (SLR) methodology was used, whose objective was to verify whether immersive technologies were being used effectively in education and, also, what challenges, opportunities, and benefits they bring to Education 4.0. For this, eight Research Questions (RQs) were formulated to be answered by articles based on the highest SLR scores. The results presented by SLR were as follows: there was an increase in the use of immersive technologies in education, but virtual reality (VR) is still more used in education than AR, despite VR being more expensive than AR; the use of these new technologies brings new challenges, opportunities, and benefits for education; there was an increase in the quality of teaching for complex subjects; and there was an increase in students’ interest in the content presented. Full article

In recent years, significant technological advances have emerged in renewable power generation systems (RPGS), making them more economical and competitive. On the other hand, for the RPGS to achieve the highest level of performance possible, it is important to ensure the healthy operation of their main building blocks. Power electronic converters (PEC), which are one of the main building blocks of RPGS, have some vulnerable components, such as capacitors, which are responsible for more than a quarter of the failures in these converters. Therefore, it is of paramount importance that the design of fault diagnosis techniques (FDT) assess the capacitor’s state of health so that it is possible to implement predictive and preventive maintenance plans in order to reduce unexpected stoppage of these systems. One of the most commonly used capacitors in power converters is the aluminum electrolytic capacitor (AEC) whose aging manifests itself through an increase in its equivalent series resistance (ESR). Several advanced intelligent techniques have been proposed for assessing AEC health status, many of which require the use of a current sensor in the capacitor branch. However, the introduction of a current sensor in the capacitor branch imposes practical restrictions; in addition, it introduces unwanted resistive and inductive effects. This paper presents an FDT based on the random forest classifier (RFC), which triggers an alert mechanism when the DC-link AEC reaches its ESR threshold value. The great advantage of the proposed solution is that it is non-invasive; therefore, it is not necessary to introduce any sensor inside the converter. The validation of the proposed FDT will be carried out using several computer simulations carried out in Matlab/Simulink. Full article

Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-modality transfer learning with a vision transformer to develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors in varying lighting conditions. Importantly, the system requires no model architecture or weight updates to enable this smooth transition. Specifically, the system uses a depth sensor in low light conditions (night time) and both an RGB camera and a depth sensor or RGB camera only in well-lit environments. We evaluate our unified model on the SUN RGB-D dataset and demonstrate that it achieves a similar or better performance in terms of the mAP50 compared to state-of-the-art methods in the SUNRGBD16 category and a comparable performance in point-cloud-only mode. We also introduce a novel inter-modality mixing method that enables our model to achieve significantly better results than previous methods. We provide our code, including training/inference logs and model checkpoints, to facilitate reproducibility and further research. Full article

We propose a new framework for camouflaged object detection (COD) named FLCNet, which comprises three modules: an underlying feature mining module (UFM), a texture-enhanced module (TEM), and a neighborhood feature fusion module (NFFM). Existing models overlook the analysis of underlying features, which results in extracted low-level feature texture information that is not prominent enough and contains more interference due to the slight difference between the foreground and background of the camouflaged object. To address this issue, we created a UFM using convolution with various expansion rates, max-pooling, and avg-pooling to deeply mine the textural information of underlying features and eliminate interference. Motivated by the traits passed down through biological evolution, we created an NFFM, which primarily consists of element multiplication and concatenation followed by an addition operation. To obtain precise prediction maps, our model employs the top-down strategy to gradually combine high-level and low-level information. Using four benchmark COD datasets, our proposed framework outperforms 21 deep-learning-based models in terms of seven frequently used indices, demonstrating the effectiveness of our methodology. Full article

Flood management and media production planning are both tasks that require timely and sound decision making, as well as effective collaboration between professionals in a team split between remote headquarter operators and in situ actors. This paper presents an extended reality (XR) platform that utilizes interactive and immersive technologies and integrates artificial intelligence (AI) algorithms to support the professionals and the public involved in such incidents and events. The developed XR tools address various specialized end-user needs of different target groups and are fueled by modules that intelligently collect, analyze, and link data from heterogeneous sources while considering user-generated content. This platform was tested in a flood-prone area and in a documentary planning scenario, where it was used to create immersive and interactive experiences. The findings demonstrate that it increases situation awareness and improves the overall performance of the professionals involved. The proposed XR system represents an innovative technological approach for tackling the challenges of flood management and media production, one that also has the potential to be applied in other fields. Full article

Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods is weakened due to the lack of texture information and the low signal-to-noise ratio of infrared small-target images. To detect small targets in infrared images with limited information, a depth feature-extraction network based on a residual module is proposed in this paper. First, a global attention guidance enhancement module (GAGEM) is used to enhance the original infrared small target image in a single frame, which considers the global and local features. Second, this paper proposes a depth feature-extraction module (DFEM) for depth feature extraction. Our IRST-Involution adds the attention mechanism to the classic Involution module and combines it with the residual module for the feature extraction of the backbone network. Finally, the feature pyramid with self-learning weight parameters is used for feature fusion. The comparative experiments on three public datasets demonstrate that our proposed infrared small-target detection algorithm exhibits higher detection accuracy and better robustness. Full article

The simultaneous multi-beam working mode of the collocated multiple-input and multiple-output (MIMO) radar enables the radar to track multiple targets simultaneously. A joint power and bandwidth allocation algorithm in a collocated MIMO radar based on the quality of service (QoS) framework is proposed for the multi-target tracking problem with different threat levels. Firstly, a posterior Cramer–Rao lower bound (PCRLB) concerning the power and bandwidth is derived. In addition, the optimal objective functions of power and bandwidth are designed based on the QoS framework, and the problem is solved using the convex relaxation technique and the cyclical minimization algorithm. The numerical results show that the proposed algorithm has better tracking accuracy and achieves more reasonable resource allocation compared to strategies such as average allocation. Full article

The Si and Mn contents in steel alloys are important characteristic indexes that influence the plasticity and welding properties of these alloys. In this work, the quantitative analysis methods for trace elements under complex alloy matrices by laser-induced breakdown spectroscopy (LIBS) are studied, which provide a foundation for utilizing LIBS technology in the rapid online detection of steel alloy properties. To improve the quantitative analysis accuracy of LIBS, deep learning algorithm methods are introduced. Given the characteristics of LIBS spectra, we explore multi-perspective feature extraction and backward differential methods to extract the spatio-temporal characteristics of LIBS spectra. The Text Convolutional Neural Network (TextCNN) model, combined with multi-perspective feature extraction, displays good stability and lower average relative errors (6.988% for Si, 6.280% for Mn) in the test set compared to the traditional quantitative analysis method and deep neural network (DNN) model. Finally, the backward differential method is employed to optimize the two-dimensional LIBS spectral input matrix, and the results indicate that the average relative errors of Si and Mn elements in the test set decrease to 5.139% and 3.939%, respectively. The method proposed in this work establishes a theoretical basis and technical support for precise prediction and online quality monitoring. Full article

Limited by the load capacity of UAVs, it is difficult for an integrated radio frequency (RF) system aboard a single platform to have both wide-area and comprehensive battlefield sensing capabilities. One possible approach to solve this dilemma is to use multiple UAVs to perceive the scene cooperatively and simultaneously. To this end, this paper mainly discusses the cooperative task planning strategies facing cooperative UAVs with integrated RF systems when performing several tasks simultaneously. First, considering the complexity of the planning problem, the physical model for UAV formation cooperation is discussed. Then, based on the irregular and ad hoc characteristics of cooperative UAV networks, the essential compositions for UAVs cooperation are formulated that includes input information and planning constraints as well as evaluation indicators. Furthermore, to solve the given task planning problem, four new planning strategies are targeted designed for different planning purposes. Finally, a simulated cooperative UAV multitask planning scenario including cooperative detection, cooperative localization, and jamming is designed. Simulation results verify the effectiveness of these strategies as well as their advantages, disadvantages, and the multiscenario adaptability of each strategy. Full article

The exponential growth of the Internet of Things (IoT) is being witnessed nowadays in different sectors. This makes IoT data communications more complex and harder to manage. Addressing such a challenge using a centralized model is an ineffective approach and would result in security and privacy difficulties. Technologies such as blockchain provide a potential solution to enable secure and effective management of IoT data communication in a distributed and trustless manner. In this paper, a novel lightweight blockchain-centric IoT architecture is proposed to address effective IoT data communication management. It is based on an event-driven smart contract that enables manageable and trustless IoT data exchange using a simple publish/subscribe model. To maintain system complexity and overhead at a minimum, the design of the proposed system relies on a single smart contract. All the system operations that enable effective IoT data communication among the different parties of the system are defined in the smart contract. There is no direct blockchain–IoT-device interaction, making the system more useable in wide IoT deployments incorporating IoT devices with limited computing and energy resources. A practical Ethereum-based implementation of the system was developed with the ability to simulate different IoT setups. The evaluation results demonstrated the feasibility and effectiveness of the proposed architecture. Considering varying-scale and varying-density experimental setups, reliable and secure data communications were achieved with little latency and resource consumption. Full article

Internet of Things (IoT) is a massive network based on tiny devices connected internally and to the internet. Each connected device is uniquely identified in this network through a dedicated IP address and can share the information with other devices. In contrast to its alternatives, IoT consumes less power and resources; however, this makes its devices more vulnerable to different types of attacks as they cannot execute heavy security protocols. Moreover, traditionally used heavy protocols for web-based communication, such as the Hyper Text Transport Protocol (HTTP) are quite costly to be executed on IoT devices, and thus specially designed lightweight protocols, such as the Constrained Application Protocol (CoAP) are employed for this purpose. However, while the CoAP remains widely-used, it is also susceptible to attacks, such as the Distributed Denial-of-Service (DDoS) attack, which aims to overwhelm the resources of the target and make them unavailable to legitimate users. While protocols, such as the Datagram Transport Layer Security (DTLS) and Lightweight and the Secure Protocol for Wireless Sensor Network (LSPWSN) can help in securing CoAP against DDoS attacks, they also have their limitations. DTLS is not designed for constrained devices and is considered as a heavy protocol. LSPWSN, on the other hand, operates on the network layer, in contrast to CoAP which operates on the application layer. This paper presents a machine learning model, using the CIDAD dataset (created on 11 July 2022), that can detect the DDoS attacks against CoAP with an accuracy of 98%. Full article

Accurate fault identification is essential for geological interpretation and reservoir exploitation. However, the unclear and noisy composition of seismic data makes it difficult to identify the complete fault structure using conventional methods. Thus, we have developed an attentional U-shaped network (EAResU-net) based on enhanced feature fusion for automated end-to-end fault interpretation of 3D seismic data. EAResU-net uses an enhanced feature fusion mechanism to reduce the semantic gap between the encoder and decoder and improve the representation of fault features in combination with residual structures. In addition, EAResU-net introduces an attention mechanism, which effectively suppresses seismic data noise and improves model accuracy. The experimental results on synthetic and field data demonstrate that, compared with traditional deep learning methods for fault detection, our EAResU-net can achieve more accurate and continuous fault recognition results. Full article