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Electronics, Volume 12, Issue 17 (September-1 2023) – 201 articles

Cover Story (view full-size image): This paper presents the design of a 110 GHz amplifier based on the quasi-linear method. The power gain can be boosted to the maximum achievable gain (Gmax) via the use of a linear, lossless, and reciprocal feedback network, though this leads to a simultaneous decrease in output power. Based on quasi-linear analysis, for an amplifier with Gmax gain, when the K-factor is equal to one the output power is zero. To avoid the very low output power of amplifiers, a new approach is proposed to balance power gain and output power. A 110 GHz six-stage feedback amplifier was designed using the proposed approach and fabricated using 40 nm CMOS technology. The measured power gain is 26.5 dB and the saturation output power is 13 dBm at 110 GHz. View this paper
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15 pages, 21169 KiB  
Article
A 3D Point Cloud Feature Identification Method Based on Improved Point Feature Histogram Descriptor
by Chunxiao Wang, Xiaoqing Xiong, Xiaoying Zhang, Lu Liu, Wu Tan, Xiaojuan Liu and Houqun Yang
Electronics 2023, 12(17), 3736; https://doi.org/10.3390/electronics12173736 - 04 Sep 2023
Viewed by 1037
Abstract
A significant amount of research has been conducted on the segmentation of large-scale 3D point clouds. However, efficient point cloud feature identification from segmentation results is an essential capability for computer vision and surveying tasks. Feature description methods are algorithms that convert the [...] Read more.
A significant amount of research has been conducted on the segmentation of large-scale 3D point clouds. However, efficient point cloud feature identification from segmentation results is an essential capability for computer vision and surveying tasks. Feature description methods are algorithms that convert the point set of the point cloud feature into vectors or matrices that can be used for identification. While the point feature histogram (PFH) is an efficient descriptor method, it does not work well with objects that have smooth surfaces, such as planar, spherical, or cylindrical objects. This paper proposes a 3D point cloud feature identification method based on an improved PFH descriptor with a feature-level normal that can efficiently distinguish objects with smooth surfaces. Firstly, a feature-level normal is established, and then the relationship between each point’s normal and feature-level normal is calculated. Finally, the unknown feature is identified by comparing the similarity of the type-labeled feature and the unknown feature. The proposed method obtains an overall identification accuracy ranging from 71.9% to 81.9% for the identification of street lamps, trees, and buildings. Full article
(This article belongs to the Section Artificial Intelligence)
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12 pages, 1541 KiB  
Article
Stabilized Temporal 3D Face Alignment Using Landmark Displacement Learning
by Seongmin Lee, Hyunse Yoon, Sohyun Park, Sanghoon Lee and Jiwoo Kang
Electronics 2023, 12(17), 3735; https://doi.org/10.3390/electronics12173735 - 04 Sep 2023
Viewed by 977
Abstract
One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome [...] Read more.
One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome the problem, we introduce neural networks to reconstruct stable and precise faces in time. The reconstruction network extracts the 3DMM parameters from video sequences to represent 3D faces in time. Meanwhile, our displacement networks learn the changes in facial landmarks. In particular, the networks learn changes caused by facial identity, facial expression, and temporal cues, respectively. The proposed facial alignment network exhibits reliable and precise performance in reconstructing static and dynamic faces by leveraging these displacement networks. The 300 Videos in the Wild (300VW) dataset is utilized for qualitative and quantitative evaluations to confirm the effectiveness of our method. The results demonstrate the considerable advantages of our method in reconstructing 3D faces from video sequences. Full article
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14 pages, 15947 KiB  
Article
A Method for Restoring γ-Radiation Scene Images Based on Spatial Axial Gradient Discrimination
by Kun-Fang Li, Jie Feng, Yu-Dong Li, Lin Wen, Yong-Jia Kan and Qi Guo
Electronics 2023, 12(17), 3734; https://doi.org/10.3390/electronics12173734 - 04 Sep 2023
Viewed by 676
Abstract
Clear and reliable visual information is the premise and basis of work for nuclear robots. However, the ubiquitous γ rays in the nuclear environment will produce radiation effects on CMOS cameras and bring in complex visual noise. In this paper, combining the mechanism [...] Read more.
Clear and reliable visual information is the premise and basis of work for nuclear robots. However, the ubiquitous γ rays in the nuclear environment will produce radiation effects on CMOS cameras and bring in complex visual noise. In this paper, combining the mechanism and characteristics of γ radiation noise, a method for restoring γ-radiation scene images based on spatial axial gradient discrimination is proposed. Firstly, interframe difference is used to determine the position of radiated noise on the image. Secondly, the gray gradients of different axes at noise pixels are calculated, and two axes with lager gray gradients are selected. Then, the adaptive medians are selected on the two axes, respectively and are weighted according to the gradient as the new value of the noise pixel. Finally, the Wallis sharpening filter is applied to enhance the detailed information and deblur the image. Plenty of experiments have been carried out on images collected in real γ radiation scenes, and image quality has been significantly improved, with Peak Signal to Noise ratio (PSNR) reaching 30.587 dB and Structural Similarity Index Mean (SSIM) reaching 0.82. It is obvious that this method has advanced performance in improving the quality of γ-radiation images. It can provide method guidance and technical support for the software module design of the anti-nuclear radiation camera. Full article
(This article belongs to the Section Microelectronics)
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30 pages, 2160 KiB  
Article
Performance Analysis of Several Intelligent Algorithms for Class Integration Test Order Optimization
by Wenning Zhang, Qinglei Zhou, Li Guo, Dong Zhao and Ximei Gou
Electronics 2023, 12(17), 3733; https://doi.org/10.3390/electronics12173733 - 04 Sep 2023
Viewed by 672
Abstract
Integration testing is an essential activity in software testing, especially in object-oriented software development. Determining the sequence of classes to be integrated, i.e., the class integration test order (CITO) problem, is of great importance but computationally challenging. Previous research has shown that meta [...] Read more.
Integration testing is an essential activity in software testing, especially in object-oriented software development. Determining the sequence of classes to be integrated, i.e., the class integration test order (CITO) problem, is of great importance but computationally challenging. Previous research has shown that meta heuristic algorithms can devise class integration test orders with lower test stubbing complexity, resulting in software testing cost reduction. This study focuses on the comparable performance evaluation of ten commonly used meta heuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CS), firefly algorithm (FA), bat algorithm (BA), grey wolf algorithm (GWO), moth flame optimization (MFO), sine cosine algorithm (SCA), salp swarm algorithm (SSA) and Harris hawk optimization (HHO). The objective of this study is to identify the most suited algorithms, narrowing down potential avenues for future researches in the field of search-based class integration test order generation. The standard implementations of these algorithms are employed to generate integration test orders. Additionally, these test orders are evaluated and compared in terms of stubbing complexity, convergence speed, average runtime, and memory consumption. The experimental results suggest that MFO, SSA, GWO and CS are the most suited algorithms. MFO, SSA and GWO exhibit excellent optimization performance in systems where fitness values are heavily impacted by attribute coupling. Meanwhile, MFO, GWO and CS are recommended for systems where the fitness values are strongly influenced by method coupling. BA and FA emerge as the slowest algorithms, while the remaining algorithms exhibit intermediate performance. The performance analysis may be used to select and improve appropriate algorithms for the CITO problem, providing a cornerstone for future scientific research and practical applications. Full article
(This article belongs to the Special Issue The Latest Progress in Software Development and Testing)
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16 pages, 4112 KiB  
Article
Portable Skin Lesion Segmentation System with Accurate Lesion Localization Based on Weakly Supervised Learning
by Hai Qin, Zhanjin Deng, Liye Shu, Yi Yin, Jintao Li, Li Zhou, Hui Zeng and Qiaokang Liang
Electronics 2023, 12(17), 3732; https://doi.org/10.3390/electronics12173732 - 04 Sep 2023
Cited by 1 | Viewed by 784
Abstract
The detection of skin lesions involves a resource-intensive and time-consuming process, necessitating specialized equipment and the expertise of dermatologists within medical facilities. Lesion segmentation, as a critical aspect of skin disorder assessment, has garnered substantial attention in recent research pursuits. In response, we [...] Read more.
The detection of skin lesions involves a resource-intensive and time-consuming process, necessitating specialized equipment and the expertise of dermatologists within medical facilities. Lesion segmentation, as a critical aspect of skin disorder assessment, has garnered substantial attention in recent research pursuits. In response, we developed a portable automatic dermatology detector and proposed a dual-CAM weakly supervised bootstrapping model for skin lesion detection. The hardware system in our device utilizes a modular and miniaturized design, including an embedded board, dermatoscope, and display, making it highly portable and easy to use in various settings. Our software solution uses a convolutional neural network (CNN) with a dual-class activation map (CAM) weakly supervised bootstrapping model for skin lesion detection. The model boasts two key characteristics: the integration of segmentation and classification networks, and the utilization of a dual CAM structure for precise lesion localization. We conducted an evaluation of our method using the ISIC2016 and ISIC2017 datasets, which yielded findings that demonstrate an AUC of 86.3% for skin lesion classification for ISIC2016 and an average AUC of 92.9% for ISIC2017. Furthermore, our system achieved diagnostic results of significant reference value, with an average AUC of 92% when tested on real-life skin. The experimental results underscore the portable device’s capacity to provide reliable diagnostic information for potential skin lesions, thereby demonstrating its practical applicability. Full article
(This article belongs to the Section Systems & Control Engineering)
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32 pages, 9865 KiB  
Article
Transfer and CNN-Based De-Authentication (Disassociation) DoS Attack Detection in IoT Wi-Fi Networks
by Samson Kahsay Gebresilassie, Joseph Rafferty, Liming Chen, Zhan Cui and Mamun Abu-Tair
Electronics 2023, 12(17), 3731; https://doi.org/10.3390/electronics12173731 - 04 Sep 2023
Viewed by 1224
Abstract
The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are [...] Read more.
The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this work was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for de-authentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate our own dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive research were carried out with performance evaluation through multiple metrics and the results reveal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive training and evaluation of the proposed model, and comparative analysis with existing models, show that this work allows improved early detection and prevention of de-authentication and disassociation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures. Full article
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17 pages, 15056 KiB  
Article
Improving Monocular Depth Estimation with Learned Perceptual Image Patch Similarity-Based Image Reconstruction and Left–Right Difference Image Constraints
by Hyeseung Park and Seungchul Park
Electronics 2023, 12(17), 3730; https://doi.org/10.3390/electronics12173730 - 04 Sep 2023
Viewed by 1093
Abstract
This paper introduces a novel approach for self-supervised monocular depth estimation. The model is trained on stereo–image (left–right pair) data and incorporates carefully designed perceptual image quality assessment-based loss functions for image reconstruction and left–right image difference. The fidelity of the reconstructed images, [...] Read more.
This paper introduces a novel approach for self-supervised monocular depth estimation. The model is trained on stereo–image (left–right pair) data and incorporates carefully designed perceptual image quality assessment-based loss functions for image reconstruction and left–right image difference. The fidelity of the reconstructed images, obtained by warping the input images using the predicted disparity maps, significantly influences the accuracy of depth estimation in self-supervised monocular depth networks. The suggested LPIPS (Learned Perceptual Image Patch Similarity)-based evaluation of image reconstruction accurately emulates human perceptual mechanisms to quantify the quality of reconstructed images, serving as an image reconstruction loss. Consequently, it facilitates the gradual convergence of the reconstructed images toward a greater similarity with the target images during the training process. Stereo–image pair often exhibits slight discrepancies in brightness, contrast, color, and camera angle due to factors like lighting conditions and camera calibration inaccuracies. These factors limit the improvement of image reconstruction quality. To address this, the left–right difference image loss is introduced, aimed at aligning the disparities between the actual left–right image pair and the reconstructed left–right image pair. Due to the tendency of distant pixel values to approach zero in the difference images derived from the left and right source images of stereo pairs, this loss progressively steers the distant pixel values of the reconstructed difference images toward a convergence with zero. Hence, the use of this loss has demonstrated its efficacy in mitigating distortions in distant regions while enhancing overall performance. The primary objective of this study is to introduce and validate the effectiveness of LPIPS-based image reconstruction and left–right difference image losses in the context of monocular depth estimation. To this end, the proposed loss functions have been seamlessly integrated into a straightforward single-task stereo–image learning framework, incorporating simple hyperparameters. Notably, our approach achieves superior results compared to other state-of-the-art methods, even those adopting more intricate hybrid data and multi-task learning strategies. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)
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15 pages, 3909 KiB  
Article
Improving Remote Photoplethysmography Performance through Deep-Learning-Based Real-Time Skin Segmentation Network
by Kunyoung Lee, Jaemu Oh, Hojoon You and Eui Chul Lee
Electronics 2023, 12(17), 3729; https://doi.org/10.3390/electronics12173729 - 04 Sep 2023
Viewed by 966
Abstract
In recent years, health-monitoring systems have become increasingly important in the medical and safety fields, including patient and driver monitoring. Remote photoplethysmography is an approach that captures blood flow changes due to cardiac activity by utilizing a camera to measure transmitted or reflected [...] Read more.
In recent years, health-monitoring systems have become increasingly important in the medical and safety fields, including patient and driver monitoring. Remote photoplethysmography is an approach that captures blood flow changes due to cardiac activity by utilizing a camera to measure transmitted or reflected light through the skin, but it has limitations in its sensitivity to changes in illumination and motion. Moreover, remote photoplethysmography signals measured from nonskin regions are unreliable, leading to inaccurate remote photoplethysmography estimation. In this study, we propose Skin-SegNet, a network that minimizes noise factors and improves pulse signal quality through precise skin segmentation. Skin-SegNet separates skin pixels and nonskin pixels, as well as accessories such as glasses and hair, through training on facial structural elements and skin textures. Additionally, Skin-SegNet reduces model parameters using an information blocking decoder and spatial squeeze module, achieving a fast inference time of 15 ms on an Intel i9 CPU. For verification, we evaluated Skin-SegNet using the PURE dataset, which consists of heart rate measurements from various environments. When compared to other skin segmentation methods with similar inference speeds, Skin-SegNet demonstrated a mean absolute percentage error of 1.18%, showing an improvement of approximately 60% compared to the 4.48% error rate of the other methods. The result even exhibits better performance, with only 0.019 million parameters, in comparison to DeepLabV3+, which has 5.22 million model parameters. Consequently, Skin-SegNet is expected to be employed as an effective preprocessing technique for facilitating efficient remote photoplethysmography on low-spec computing devices. Full article
(This article belongs to the Special Issue Deep Learning Approach for Secure and Trustworthy Biometric System)
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15 pages, 3008 KiB  
Article
A Fault Location Analysis of Optical Fiber Communication Links in High Altitude Areas
by Kehang Xu and Chaowei Yuan
Electronics 2023, 12(17), 3728; https://doi.org/10.3390/electronics12173728 - 04 Sep 2023
Cited by 2 | Viewed by 1147
Abstract
Breakage and damage of fiber optic cable fibers seriously affects the normal operation of fiber optic networks, and it is important to quickly and accurately determine the type and location of faults when they occur. Unlike the old traditional methods, the advantages of [...] Read more.
Breakage and damage of fiber optic cable fibers seriously affects the normal operation of fiber optic networks, and it is important to quickly and accurately determine the type and location of faults when they occur. Unlike the old traditional methods, the advantages of wavelet transform in singular signal detection and signal filtering are used to analyze the Optical Time Domain Reflectometer curve signal and the fault detection method of fiber communication links with no relay and a large span in a high altitude area is given, which realizes the accurate detection and location of optical fiber communication link fault events under strong noise. The proposed technology detects fiber optic faults in high-altitude environments, with an average measurement accuracy improvement of 9.8%. The maximum distance for detecting fiber optic line faults is up to 250 km, which increases the system power budget. In the simulation experiment results, the infrastructure nodes of the Wuhan FiberHome Laboratory successfully verified the superiority of this technology. The method has been directly applied to the on-site detection of ultra long optical fiber links in high-altitude areas, which has good financial significance and has certain reference significance for the future real-time detection of optical fiber cables. Full article
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16 pages, 3696 KiB  
Article
Traffic Light Detection by Integrating Feature Fusion and Attention Mechanism
by Chi-Hung Chuang, Chun-Chieh Lee, Jung-Hua Lo and Kuo-Chin Fan
Electronics 2023, 12(17), 3727; https://doi.org/10.3390/electronics12173727 - 04 Sep 2023
Viewed by 996
Abstract
Path planning is a key problem in the design of autonomous driving systems, and accurate traffic light detection is very important for robust routing. In this paper, we devise an object detection model, which mainly focuses on traffic light classification at a distance. [...] Read more.
Path planning is a key problem in the design of autonomous driving systems, and accurate traffic light detection is very important for robust routing. In this paper, we devise an object detection model, which mainly focuses on traffic light classification at a distance. In the past, most techniques employed in this field were dominated by high-intensity convolutional neural networks (CNN), and many advances have been achieved. However, the size of traffic lights may be small, and how to detect them accurately still deserves further study. In the object detection domain, the scheme of feature fusion and transformer-based methods have obtained good performance, showing their excellent feature extraction capability. Given this, we propose an object detection model combining both the pyramidal feature fusion and self-attention mechanism. Specifically, we use the backbone of the mainstream one-stage object detection model consisting of a parallel residual bi-fusion (PRB) feature pyramid network and attention modules, coupling with architectural tuning and optimizer selection. Our network architecture and module design aim to effectively derive useful features aimed at detecting small objects. Experimental results reveal that the proposed method exhibits noticeable improvement in many performance indicators: precision, recall, F1 score, and mAP, compared to the vanilla models. In consequence, the proposed method obtains good results in traffic light detection. Full article
(This article belongs to the Section Computer Science & Engineering)
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18 pages, 4418 KiB  
Article
Improved LSTM Neural Network-Assisted Combined Vehicle-Mounted GNSS/SINS Navigation and Positioning Algorithm
by Lijun Song, Peiyu Xu, Xing He, Yunlong Li, Jiajie Hou and Haoyu Feng
Electronics 2023, 12(17), 3726; https://doi.org/10.3390/electronics12173726 - 04 Sep 2023
Cited by 1 | Viewed by 768
Abstract
Aiming at the problem of the combined navigation system of on-board GNSS (global navigation satellite system)/SINS (strapdown inertial navigation system), the accuracy of the combined navigation system decreases due to the dispersion of the SINS over time and under the condition of No [...] Read more.
Aiming at the problem of the combined navigation system of on-board GNSS (global navigation satellite system)/SINS (strapdown inertial navigation system), the accuracy of the combined navigation system decreases due to the dispersion of the SINS over time and under the condition of No GNSS signals. An improved LSTM (long short-term memory) neural network in No GNSS signal conditions is proposed to assist the combination of navigation data and the positioning algorithm. When the GNSS signal is normal input, the current on-board combination of the navigation module’s output sensor data information is used for training to improve the LSTM algorithm and to establish the incremental output of the GNSS position of the mapping of the different weights. In No GNSS signal conditions, using the improved LSTM algorithm can improve the combination of navigation and positioning algorithms. Under No GNSS signal conditions, the improved LSTM training model is used to predict the dynamics of SINS information component data. Under No GNSS signal conditions, the combined navigation filtering design is completed, and the error correction of SINS navigation and positioning information is carried out to obtain a more accurate combination of navigation and positioning system accuracy. It can be seen through the actual test experiment using a sports car in the two trajectories under the conditions of No GNSS signals that the proposed algorithm can be compared with the LSTM algorithm. In testing road sections, the proposed algorithm, when compared with the LSTM algorithm to obtain the northward position that the mean square errors were improved by 55.63% and 76.64%, and the eastward position mean square errors were improved by 43.42% and 54.67%. In a straight-line trajectory, improving the effect’s navigation and positioning accuracy and reliability is significant. Full article
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14 pages, 5263 KiB  
Article
A 110 GHz Feedback Amplifier Design Based on Quasi-Linear Analysis
by Ruibing Dong, Yiheng Song and Yang Xing
Electronics 2023, 12(17), 3725; https://doi.org/10.3390/electronics12173725 - 04 Sep 2023
Viewed by 879
Abstract
The power gain and output power of millimeter-wave (mm-Wave) and terahertz (THz) amplifiers are critical performance metrics, particularly when the operating frequencies of amplifiers are near to the maximum oscillator frequency (fmax) of the transistor. This paper presents [...] Read more.
The power gain and output power of millimeter-wave (mm-Wave) and terahertz (THz) amplifiers are critical performance metrics, particularly when the operating frequencies of amplifiers are near to the maximum oscillator frequency (fmax) of the transistor. This paper presents the design of a 110 GHz amplifier based on the quasi-linear method. The power gain can be boosted to maximum achievable gain (Gmax) using a linear, lossless, reciprocal feedback network, though this leads to a simultaneous decrease in output power. Based on quasi-linear analysis, for an amplifier with Gmax gain, when the K-factor is equal to 1, the output power is zero. To avoid the very low output power of amplifiers, a new approach is proposed to balance power gain and output power. A 110 GHz six-stage feedback amplifier was designed using the proposed approach and fabricated using 40 nm CMOS technology. The measured power gain is 26.5 dB, and the saturation output power is 13 dBm at 110 GHz. Full article
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17 pages, 3156 KiB  
Article
Multiobjective Learning to Rank Based on the (1 + 1) Evolutionary Strategy: An Evaluation of Three Novel Pareto Optimal Methods
by Walaa N. Ismail, Osman Ali Sadek Ibrahim, Hessah A. Alsalamah and Ebtesam Mohamed
Electronics 2023, 12(17), 3724; https://doi.org/10.3390/electronics12173724 - 04 Sep 2023
Viewed by 1015
Abstract
In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator [...] Read more.
In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator for mutation step sizes outperformed the other distributions used. The aim of using the chosen Pareto optimal methods was to determine which method can give a better exploration–exploitation trade-off for the solution space to obtain the optimal or near-optimal solution. The best combination for that in terms of winning rate is the Cauchy distribution for mutation step sizes with method 3 of the Pareto optimal procedure. Moreover, different random number generators were evaluated and analyzed versus datasets in terms of NDCG@10 for testing data. It was found that the Levy generator is the best for both the MSLR and the MQ2007 datasets, while the Gaussian generator is the best for the MQ2008 dataset. Thus, random number generators clearly affect the performance of ES-Rank based on the dataset used. Furthermore, method 3 had the highest NDCG@10 for MQ2008 and MQ2007, while for the MSLR dataset, the highest NDCG@10 was achieved by method 2. Along with this paper, we provide a Java archive for reproducible research. Full article
(This article belongs to the Special Issue Evolutionary Computation Methods for Real-World Problem Solving)
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17 pages, 9453 KiB  
Article
An Improved CNN for Polarization Direction Measurement
by Hao Han, Jin Liu, Wei Wang, Chao Gao and Jianhua Shi
Electronics 2023, 12(17), 3723; https://doi.org/10.3390/electronics12173723 - 04 Sep 2023
Viewed by 827
Abstract
Spatially polarization modulation has been proven to be an efficient and simple method for polarization measurement. Since the polarization information is encoded in the intensity distribution of the modulated light, the task of polarization measurement can be treated as the image processing problem, [...] Read more.
Spatially polarization modulation has been proven to be an efficient and simple method for polarization measurement. Since the polarization information is encoded in the intensity distribution of the modulated light, the task of polarization measurement can be treated as the image processing problem, while the pattern of the light is captured by a camera. However, classical image processing methods could not meet the increasing demand of practical applications due to their poor computational efficiency. To address this issue, in this paper, an improved Convolutional Neural Network is proposed to extract the Stokes parameters of the light from the irradiance image. In our algorithm, residual blocks are adopted and different layers are connected to ensure that the underlying features include more details of the image. Furthermore, refined residual block and Global Average Pooling are introduced to avoid overfitting issues and gradient vanishing problems. Finally, our algorithm is tested on massive synthetic and real data, while the mean square error (MSE) between the extracted values and the true values of the normalized Stokes parameters is counted. Compared to VGG and FAM, the experimental results demonstrate that our algorithm has outstanding performance. Full article
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16 pages, 6712 KiB  
Article
A Family of Five-Level Pseudo-Totem Pole Dual Boost Converters
by Qingsong Zhao, Guixi Miao, Hong Dai, Cheng Jing, Jianyuan Xu, Wenjing Li and Hui Ma
Electronics 2023, 12(17), 3722; https://doi.org/10.3390/electronics12173722 - 03 Sep 2023
Viewed by 639
Abstract
In this paper, based on the pseudo-totem pole (PTP) circuit, a family of five-level PTP dual boost converters (PDBC) is proposed. A dual boost converter has some unique advantages, such as having no risk of bridge arm shoot-through and no problems related to [...] Read more.
In this paper, based on the pseudo-totem pole (PTP) circuit, a family of five-level PTP dual boost converters (PDBC) is proposed. A dual boost converter has some unique advantages, such as having no risk of bridge arm shoot-through and no problems related to switch body diode reverse recovery; thus, it has a good potential for applications. First, the derivation process, working principle, modulation and strategy of the topology are analyzed. Further, the number of power devices, switch voltages and current stress of the proposed topology is analyzed. Finally, a representative five-level PDBC experimental prototype is designed with AC input 220 V/50 Hz, DC output 400 V/1 kW, and peak efficiency of 98.27%. The experimental results show that the five-level PDBC proposed in this paper has higher efficiency and the correctness of its topology is verified. Full article
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22 pages, 6052 KiB  
Article
Enabling a Secure IoT Environment Using a Blockchain-Based Local-Global Consensus Manager
by Saleh Alghamdi, Aiiad Albeshri and Ahmed Alhusayni
Electronics 2023, 12(17), 3721; https://doi.org/10.3390/electronics12173721 - 03 Sep 2023
Cited by 4 | Viewed by 1829
Abstract
The Internet of Things (IoT) refers to the network of interconnected devices that can communicate and share data over the Internet. The widespread adoption of smart devices within Internet of Things (IoT) networks poses considerable security challenges for their communication. To address these [...] Read more.
The Internet of Things (IoT) refers to the network of interconnected devices that can communicate and share data over the Internet. The widespread adoption of smart devices within Internet of Things (IoT) networks poses considerable security challenges for their communication. To address these issues, blockchain technology, known for its decentralized and distributed nature, offers potential solutions within consensus-based authentication in IoT networks. This paper presents a novel approach called the local and global layer blockchain model, which aims to enhance security while simplifying implementation. The model leverages the concept of clustering to establish a local-global architecture, with cluster heads assuming responsibility for local authentication and authorization. Implementing a local private blockchain facilitates seamless communication between cluster heads and relevant base stations. This blockchain implementation enhances credibility assurance, strengthens security, and provides an effective network authentication mechanism. Simulation results indicate that the proposed algorithm outperforms previously reported methods. The proposed model achieved an average coverage per node of 0.9, which is superior to baseline models. Additionally, the lightweight blockchain model proposed in this paper demonstrates superior capabilities in achieving balanced network latency and throughput compared to traditional global blockchain approaches. Full article
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16 pages, 3266 KiB  
Article
Sensorless Control Method for SPMSMs Based on Improved Sliding Mode Reaching Rate
by Yuepeng Chen, Aiyi Li, Hui Li, Xu Yang and Wei Chen
Electronics 2023, 12(17), 3720; https://doi.org/10.3390/electronics12173720 - 03 Sep 2023
Cited by 1 | Viewed by 884
Abstract
Due to the advantages of simple structure, small size, and high power density, permanent magnet synchronous motors (PMSM) have attracted the research interest of many scholars both domestically and abroad. However, traditional PMSM equipped with sensors, encoders, and other devices tend to have [...] Read more.
Due to the advantages of simple structure, small size, and high power density, permanent magnet synchronous motors (PMSM) have attracted the research interest of many scholars both domestically and abroad. However, traditional PMSM equipped with sensors, encoders, and other devices tend to have high equipment costs and rely heavily on the accuracy of the sensors for control effectiveness. Therefore, sensorless control has become a hot trend in the PMSM control field. In response to the chattering problem in sliding mode algorithms, this study first optimized the sliding mode reaching rate of a sensorless control system and applied it to construct a sliding mode observer and speed controller. Next, the improved sliding mode reaching rate-based sensorless control system was modeled and simulated in Matlab/Simulink, and its control performance was compared and analyzed with that of the traditional sliding mode reaching rate and replicated sliding mode reaching rate. Finally, comparative experiments were conducted on a test bench, and the results showed that, under the action of the improved sliding mode reaching rate, the chattering range of the output speed of the motor was +2%~+5%, which optimized the output speed of the PMSM and achieved the purpose of weakening the chattering. Full article
(This article belongs to the Topic Future Generation Electric Machines and Drives)
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26 pages, 2058 KiB  
Article
Effects of Variation in Geometric Parameters and Structural Configurations on the Transmission Characteristics of Terahertz-Range Spoof Surface Plasmon Polariton Interconnects for Interchip Data Communication: A Finite Element Method Study
by K. M. Daiyan, Shaiokh Bin Abi, A. B. M. Harun-Ur Rashid and MST Shamim Ara Shawkat
Electronics 2023, 12(17), 3719; https://doi.org/10.3390/electronics12173719 - 02 Sep 2023
Viewed by 1074
Abstract
Interconnects have become a major obstacle in chip scaling. Spoof surface plasmon polariton (SSPP) modes have gained attention for their ability to manipulate light beyond diffraction limits at a given frequency, leading to SSPP interconnects. This article investigates the transmission characteristics of SSPP [...] Read more.
Interconnects have become a major obstacle in chip scaling. Spoof surface plasmon polariton (SSPP) modes have gained attention for their ability to manipulate light beyond diffraction limits at a given frequency, leading to SSPP interconnects. This article investigates the transmission characteristics of SSPP interconnect pairs placed side by side in the terahertz frequency range with comprehensive performance analysis. The proposed SSPP waveguide pair exhibits a maximum transmission coefficient of around −0.05 dB in the −3 dB band in the terahertz frequency range. Due to field confinement near the metal–dielectric interface, energy remains confined for the designed SSPP interconnect pair system. The proposed SSPP structure shows several bands in the terahertz frequency range, whereas conventional interconnects shows almost zero transmission at such frequencies. Additionally, the effect of geometric parameters on transmission coefficients (S21) and coupling coefficients (S41) has been investigated. Moreover, it has been shown that the bandwidth, as well as the upper cutoff frequency, can be tuned by varying the geometric parameters such as groove height, groove width and groove density. Since global interconnects undergo bending in actual circuits during distant data transmission on chips, geometric mismatches may occur between adjacent pairs of SSPP interconnects. Hence, it has also been examined how bending and mismatches affect transmission and coupling coefficients. Several SSPP schemes have been simulated, among which the best performance is obtained with 2 μm mismatch in groove height. For this optimized design, two corrugated metal interconnects are considered with groove heights of 20 μm and 22 μm, respectively, a groove width of 3 μm, a period of 20 μm, and the number of grooves at 50. For this particular configuration, an ultra-wide passband is found having a bandwidth of almost 400 GHz, with a signal reflection of below −12 dB and little insertion loss of ∼−1.43 dB. Full article
(This article belongs to the Special Issue Spoof Surface Plasmons: Theory, Designs and Applications)
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21 pages, 9026 KiB  
Article
An Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks
by Yong Tao, Haitao Liu, Shuo Chen, Jiangbo Lan, Qi Qi and Wenlei Xiao
Electronics 2023, 12(17), 3718; https://doi.org/10.3390/electronics12173718 - 02 Sep 2023
Viewed by 864
Abstract
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is [...] Read more.
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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17 pages, 3798 KiB  
Article
A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services
by Eva González-Romera, Enrique Romero-Cadaval, Carlos Roncero-Clemente, María-Isabel Milanés-Montero, Fermín Barrero-González and Anas-Abdullah Alvi
Electronics 2023, 12(17), 3717; https://doi.org/10.3390/electronics12173717 - 02 Sep 2023
Cited by 1 | Viewed by 951
Abstract
Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a [...] Read more.
Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a management system for a residential virtual power plant that includes household loads, photovoltaic generation, energy storage systems, and electric vehicles. With the proposed management system, the virtual power plant is economically optimized (as in commercial virtual power plants) while providing ancillary services (as in technical virtual power plants) to the distribution grid. A genetic algorithm with appropriate constraints is designed and tested to manage the energy storage system and the charge/discharge of electric vehicles, with several economic and technical objectives. Single-objective optimization techniques are compared to multi-objective ones to show that the former perform better in the studied scenarios. A deterministic gradient-based optimization method is also used to validate the performance of the genetic algorithm. The results show that these technical targets (usually reserved for larger virtual power plants) and economic targets can be easily managed in restricted-sized virtual power plants. Full article
(This article belongs to the Special Issue Smart Energy Systems Using AI and IoT Solutions)
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16 pages, 2663 KiB  
Article
Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept
by Marco Cascella, Vincenzo Norman Vitale, Michela D’Antò, Arturo Cuomo, Francesco Amato, Maria Romano and Alfonso Maria Ponsiglione
Electronics 2023, 12(17), 3716; https://doi.org/10.3390/electronics12173716 - 02 Sep 2023
Viewed by 1028
Abstract
Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. [...] Read more.
Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts. Full article
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14 pages, 910 KiB  
Article
A Passive Channel Measurement and Analysis Based on a 5G Commercial Network in V2I Communications
by Chen Chen, Dan Fei, Peng Zheng and Bo Ai
Electronics 2023, 12(17), 3715; https://doi.org/10.3390/electronics12173715 - 02 Sep 2023
Viewed by 865
Abstract
To acquire accurate channel characteristics for 5G New Radio (NR) vehicle-to-infrastructure (V2I) communications, in this paper, we propose a 5G passive channel measurement platform based on software defined radio devices and 5G user equipment. Different from active measurement platforms, the proposed measurement platform [...] Read more.
To acquire accurate channel characteristics for 5G New Radio (NR) vehicle-to-infrastructure (V2I) communications, in this paper, we propose a 5G passive channel measurement platform based on software defined radio devices and 5G user equipment. Different from active measurement platforms, the proposed measurement platform only requires a receiver and the channel state information reference signal (CSI-RS) periodically transmitted by the 5G commercial base stations is used as the measurement waveform. The channel impulse response can be computed based on the CSI-RS signal extracted from the received waveform and the standard CSI-RS signal generated according to the signaling information. By using the proposed 5G passive channel measurement platform, we carry out wireless channel measurement for V2I communications in typical urban scenarios. Further, based on the measurement, the small-scale channel fading characteristics including the power delay profile, the number of multipaths, the delay spread of multipaths, and the Ricean K-factor are analyzed. Full article
(This article belongs to the Special Issue Channel Measurement, Modeling and Simulation of 6G)
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16 pages, 20078 KiB  
Article
Cooperative Environmental Perception Task Offloading for Connected and Autonomous Vehicles
by Pin Lv, Jie Huang and Heng Liu
Electronics 2023, 12(17), 3714; https://doi.org/10.3390/electronics12173714 - 02 Sep 2023
Viewed by 827
Abstract
Cooperative environmental perception is an effective way to provide connected and autonomous vehicles (CAVs) with the necessary environmental information. The research goal of this paper is to achieve efficient sharing of cooperative environmental perception information. Hence, a novel vehicular edge computing scheme is [...] Read more.
Cooperative environmental perception is an effective way to provide connected and autonomous vehicles (CAVs) with the necessary environmental information. The research goal of this paper is to achieve efficient sharing of cooperative environmental perception information. Hence, a novel vehicular edge computing scheme is proposed. In this scheme, the environmental perception tasks are selected to be offloaded based on their shareability, and the edge server directly delivers the task results to the CAVs who need the perception information. The experimental results show that the proposed task offloading scheme can decrease the perception information delivery latency up to 20%. Therefore, it is an effective way to improve cooperative environmental perception efficiency by taking the shareability of the perception information into consideration. Full article
(This article belongs to the Special Issue Emerging Technologies in Autonomous Vehicles)
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26 pages, 55800 KiB  
Article
Software Design for Airborne GNSS Air Service Performance Evaluation under Ionospheric Scintillation
by Tieqiao Hu, Gaojian Zhang and Lunlong Zhong
Electronics 2023, 12(17), 3713; https://doi.org/10.3390/electronics12173713 - 02 Sep 2023
Viewed by 722
Abstract
The performance analysis and evaluation of satellite navigation systems under ionospheric scintillation have been a focal point in the field of modern aviation. With the development and upgrading of satellite navigation systems, the performance indicators and evaluation techniques of these systems also require [...] Read more.
The performance analysis and evaluation of satellite navigation systems under ionospheric scintillation have been a focal point in the field of modern aviation. With the development and upgrading of satellite navigation systems, the performance indicators and evaluation techniques of these systems also require continuous iteration and optimization. In this study, based on the ionospheric scintillation model and satellite navigation algorithm, we designed a software tool to evaluate the performance of GNSS aviation services under various ionospheric scintillation intensities. The software is implemented in the C/C++ programming language and provides assessment capabilities for different ionospheric scintillation environments and flight phases. By encapsulating the software task modules using technologies such as dynamic link libraries and thread pools, the software can flexibly adjust the ionospheric scintillation intensity and control the flight trajectory. This ensures the strong scalability and reusability of the software. The software supports the performance evaluation of aviation services during all flight phases of global flights and is compatible with GPS, BDS, GALILEO, and GLONASS systems. Through verification of the accuracy, integrity, continuity, and availability of the GNSS system under different flight phases and ionospheric scintillation effects, the effectiveness of the software design has been validated. Full article
(This article belongs to the Special Issue The Latest Progress in Software Development and Testing)
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16 pages, 1305 KiB  
Article
Adaptive Quantization Mechanism for Federated Learning Models Based on DAG Blockchain
by Tong Li, Chao Yang, Lei Wang, Tingting Li, Hai Zhao and Jiewei Chen
Electronics 2023, 12(17), 3712; https://doi.org/10.3390/electronics12173712 - 02 Sep 2023
Viewed by 866
Abstract
With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks [...] Read more.
With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks while ensuring that user data privacy is not violated has attracted the attention of the industry. Blockchain-based federated learning (FL), proposed as a new decentralized and distributed learning framework for building privacy-enhanced IoT systems, is receiving more and more attention from scholars. The framework provides some advantages, including decentralization, scalability, and data privacy, but at the same time its consensus mechanism consumes a significant amount of computational resources. Moreover, the number of model parameters has increased dramatically, leading to an increasing amount of transmitted data and a vast communication overhead. To reduce the communication overhead, we propose an FL framework in the directed acyclic graph (DAG)-based blockchain system, which achieves efficient and trusted sharing of FL networks. We design an adaptive model compression method based on k-means to compress the FL model and reduce the communication overhead of each round in FL training. Meanwhile, the original accuracy-based tips selection algorithm is optimized, and a tips selection algorithm considering multi-factor evaluation is proposed. Simulation experimental results show that the method proposed in this paper reduces the total bytes of communication of the blockchain-based federated learning system while balancing the accuracy of the FL model compared to previous work. Full article
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0 pages, 9232 KiB  
Article
Robust Visual Recognition in Poor Visibility Conditions: A Prior Knowledge-Guided Adversarial Learning Approach
by Jiangang Yang, Jianfei Yang, Luqing Luo, Yun Wang, Shizheng Wang and Jian Liu
Electronics 2023, 12(17), 3711; https://doi.org/10.3390/electronics12173711 - 02 Sep 2023
Cited by 2 | Viewed by 812 | Correction
Abstract
Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor [...] Read more.
Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor visibility conditions through techniques such as image restoration, data augmentation, and unsupervised domain adaptation, these efforts are predominantly confined to specific scenarios and fail to address multiple poor visibility scenarios encountered in real-world settings. Furthermore, the valuable prior knowledge inherent in poor visibility images is seldom utilized to aid in resolving high-level computer vision tasks. In light of these challenges, we propose a novel deep learning paradigm designed to bolster the robustness of object recognition across diverse poor visibility scenes. By observing the prior information in diverse poor visibility scenes, we integrate a feature matching module based on this prior knowledge into our proposed learning paradigm, aiming to facilitate deep models in learning more robust generic features at shallow levels. Moreover, to further enhance the robustness of deep features, we employ an adversarial learning strategy based on mutual information. This strategy combines the feature matching module to extract task-specific representations from low visibility scenes in a more robust manner, thereby enhancing the robustness of object recognition. We evaluate our approach on self-constructed datasets containing diverse poor visibility scenes, including visual blur, fog, rain, snow, and low illuminance. Extensive experiments demonstrate that our proposed method yields significant improvements over existing solutions across various poor visibility conditions. Full article
(This article belongs to the Section Artificial Intelligence)
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16 pages, 10555 KiB  
Article
Co-Simulation Platform with Hardware-in-the-Loop Using RTDS and EXata for Smart Grid
by Peng Gong, Haowei Yang, Haiqiao Wu, Huibo Li, Yu Liu, Zhenheng Qi, Weidong Wang, Dapeng Wu and Xiang Gao
Electronics 2023, 12(17), 3710; https://doi.org/10.3390/electronics12173710 - 02 Sep 2023
Viewed by 1041
Abstract
The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in [...] Read more.
The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in a large-scale power outage, leading to significant economic losses. To assess the impact of grid faults or attacks, hardware-in-the-loop (HIL) simulation tools that integrate real grid networks and software virtual networks (SVNs) are used. However, scheduling faults and modifying model parameters using most existing simulators can be challenging, and traditional HIL interfaces only support a single device. To address these limitations, we designed and implemented a grid co-simulation platform that could dynamically simulate grid faults and evaluate grid sub-nets. This platform used RTDS and EXata as power and communication simulators, respectively, integrated using a protocol conversion module to synchronize and convert protocol formats. Additionally, the platform had a programmable fault configuration interface (PFCI) to modify model parameters and a real sub-net access interface (RSAI) to access physical grid devices or sub-nets in the SVN, improving simulation accuracy. We also conducted several tests to demonstrate the effectiveness of the proposed platform. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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17 pages, 3187 KiB  
Article
A Multimodal User-Adaptive Recommender System
by Nicolás Torres
Electronics 2023, 12(17), 3709; https://doi.org/10.3390/electronics12173709 - 02 Sep 2023
Viewed by 1497
Abstract
Traditional recommendation systems have predominantly relied on user-provided ratings as explicit input. Concurrently, visually aware recommender systems harness inherent visual cues within data to decode item characteristics and deduce user preferences. However, the untapped potential of incorporating item images into the recommendation process [...] Read more.
Traditional recommendation systems have predominantly relied on user-provided ratings as explicit input. Concurrently, visually aware recommender systems harness inherent visual cues within data to decode item characteristics and deduce user preferences. However, the untapped potential of incorporating item images into the recommendation process warrants investigation. This paper introduces an original convolutional neural network (CNN) architecture that leverages multimodal information, connecting user ratings with product images to enhance item recommendations. A central innovation of the proposed model is the User-Adaptive Filtering Module, a dynamic component that utilizes user profiles to generate personalized filters. Through meticulous visual influence analysis, the effectiveness of these filters is demonstrated. Furthermore, experimental results underscore the competitive performance of the approach compared to traditional collaborative filtering methods, thereby offering a promising avenue for personalized recommendations. This approach capitalizes on user adaptation patterns, enhancing the understanding of user preferences and visual attributes. Full article
(This article belongs to the Special Issue Recommender Systems and Data Mining)
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23 pages, 5567 KiB  
Article
VR Drumming Pedagogy: Action Observation, Virtual Co-Embodiment, and Development of Drumming “Halvatar”
by James Pinkl and Michael Cohen
Electronics 2023, 12(17), 3708; https://doi.org/10.3390/electronics12173708 - 01 Sep 2023
Viewed by 922
Abstract
Virtual Co-embodiment (vc) is a relatively new field of VR, enabling a user to share control of an avatar with other users or entities. According to a recent study, vc was shown to have the highest motor skill learning efficiency out [...] Read more.
Virtual Co-embodiment (vc) is a relatively new field of VR, enabling a user to share control of an avatar with other users or entities. According to a recent study, vc was shown to have the highest motor skill learning efficiency out of three VR-based methods. This contribution expands on these findings, as well as previous work relating to Action Observation (ao) and drumming, to realize a new concept to teach drumming. Users “duet” with an exemplar half in a virtual scene with concurrent feedback to learn rudiments and polyrhythms. We call this puppet avatar controlled by both a user and separate processes a “halvatar”. The development is based on body-part-segmented vc techniques and uses programmed animation, electromechanical drum strike detection, and optical bimanual hand-tracking informed by head-tracking. A pilot study was conducted with primarily non-musicians showing the potential effectiveness of this tool and approach. Full article
(This article belongs to the Special Issue Wearable Sensing Devices and Technology)
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21 pages, 5109 KiB  
Article
Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine
by Yingqi Li, Chao Sun and Yuechan Liu
Electronics 2023, 12(17), 3707; https://doi.org/10.3390/electronics12173707 - 01 Sep 2023
Cited by 1 | Viewed by 941
Abstract
This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine [...] Read more.
This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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