Electronics最新文献_第9页

Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms 用于风电场功率控制的飞轮储能系统的硬件在环仿真

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183610

Li Yang, Qiaoni Zhao

{"title":"Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms","authors":"Li Yang, Qiaoni Zhao","doi":"10.3390/electronics13183610","DOIUrl":"https://doi.org/10.3390/electronics13183610","url":null,"abstract":"Flywheel energy storage systems (FESSs) are widely used for power regulation in wind farms as they can balance the wind farms’ output power and improve the wind power grid connection rate. Due to the complex environment of wind farms, it is costly and time-consuming to repeatedly debug the system on-site. To save research costs and shorten research cycles, a hardware-in-the-loop (HIL) testing system was built to provide a convenient testing environment for the research of FESSs on wind farms. The focus of this study is the construction of mathematical models in the HIL testing system. Firstly, a mathematical model of the FESS main circuit is established using a hierarchical method. Secondly, the principle of the permanent magnet synchronous motor (PMSM) is analyzed, and a nonlinear dq mathematical model of the PMSM is established by referring to the relationship among d-axis inductance, q-axis inductance, and permanent magnet flux change with respect to the motor’s current. Then, the power grid and wind farm test models are established. Finally, the established mathematical models are applied to the HIL testing system. The experimental results indicated that the HIL testing system can provide a convenient testing environment for the optimization of FESS control algorithms.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"53 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fundus Image Generation and Classification of Diabetic Retinopathy Based on Convolutional Neural Network 基于卷积神经网络的糖尿病视网膜病变眼底图像生成与分类

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183603

Peiming Zhang, Jie Zhao, Qiaohong Liu, Xiao Liu, Xinyu Li, Yimeng Gao, Weiqi Li

{"title":"Fundus Image Generation and Classification of Diabetic Retinopathy Based on Convolutional Neural Network","authors":"Peiming Zhang, Jie Zhao, Qiaohong Liu, Xiao Liu, Xinyu Li, Yimeng Gao, Weiqi Li","doi":"10.3390/electronics13183603","DOIUrl":"https://doi.org/10.3390/electronics13183603","url":null,"abstract":"To detect fundus diseases, for instance, diabetic retinopathy (DR) at an early stage, thereby providing timely intervention and treatment, a new diabetic retinopathy grading method based on a convolutional neural network is proposed. First, data cleaning and enhancement are conducted to improve the image quality and reduce unnecessary interference. Second, a new conditional generative adversarial network with a self-attention mechanism named SACGAN is proposed to augment the number of diabetic retinopathy fundus images, thereby addressing the problems of insufficient and imbalanced data samples. Next, an improved convolutional neural network named DRMC Net, which combines ResNeXt-50 with the channel attention mechanism and multi-branch convolutional residual module, is proposed to classify diabetic retinopathy. Finally, gradient-weighted class activation mapping (Grad-CAM) is utilized to prove the proposed model’s interpretability. The outcomes of the experiment illustrates that the proposed method has high accuracy, specificity, and sensitivity, with specific results of 92.3%, 92.5%, and 92.5%, respectively.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning for Network Intrusion Detection in Virtual Networks 深度学习用于虚拟网络中的网络入侵检测

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183617

Daniel Spiekermann, Tobias Eggendorfer, Jörg Keller

{"title":"Deep Learning for Network Intrusion Detection in Virtual Networks","authors":"Daniel Spiekermann, Tobias Eggendorfer, Jörg Keller","doi":"10.3390/electronics13183617","DOIUrl":"https://doi.org/10.3390/electronics13183617","url":null,"abstract":"As organizations increasingly adopt virtualized environments for enhanced flexibility and scalability, securing virtual networks has become a critical part of current infrastructures. This research paper addresses the challenges related to intrusion detection in virtual networks, with a focus on various deep learning techniques. Since physical networks do not use encapsulation, but virtual networks do, packet analysis based on rules or machine learning outcomes for physical networks cannot be transferred directly to virtual environments. Encapsulation methods in current virtual networks include VXLAN (Virtual Extensible LAN), an EVPN (Ethernet Virtual Private Network), and NVGRE (Network Virtualization using Generic Routing Encapsulation). This paper analyzes the performance and effectiveness of network intrusion detection in virtual networks. It delves into challenges inherent in virtual network intrusion detection with deep learning, including issues such as traffic encapsulation, VM migration, and changing network internals inside the infrastructure. Experiments on detection performance demonstrate the differences between intrusion detection in virtual and physical networks.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"26 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Analog Integrated Multiloop LDO: From Analysis to Design 模拟集成多回路 LDO：从分析到设计

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183602

Konstantinos Koniavitis, Vassilis Alimisis, Nikolaos Uzunoglu, Paul P. Sotiriadis

引用次数: 0

Dual Convolutional Malware Network (DCMN): An Image-Based Malware Classification Using Dual Convolutional Neural Networks 双卷积恶意软件网络（DCMN）：使用双卷积神经网络进行基于图像的恶意软件分类

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183607

Bassam Al-Masri, Nader Bakir, Ali El-Zaart, Khouloud Samrouth

引用次数: 0

Maritime Object Detection by Exploiting Electro-Optical and Near-Infrared Sensors Using Ensemble Learning 通过集合学习利用电子光学和近红外传感器进行海上物体探测

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183615

Muhammad Furqan Javed, Muhammad Osama Imam, Muhammad Adnan, Iqbal Murtza, Jin-Young Kim

{"title":"Maritime Object Detection by Exploiting Electro-Optical and Near-Infrared Sensors Using Ensemble Learning","authors":"Muhammad Furqan Javed, Muhammad Osama Imam, Muhammad Adnan, Iqbal Murtza, Jin-Young Kim","doi":"10.3390/electronics13183615","DOIUrl":"https://doi.org/10.3390/electronics13183615","url":null,"abstract":"Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"101 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neutral-Point Voltage Regulation and Control Strategy for Hybrid Grounding System Combining Power Module and Low Resistance in 10 kV Distribution Network 10 千伏配电网中结合功率模块和低电阻的混合接地系统的中性点电压调节和控制策略

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183608

Yu Zhou, Kangli Liu, Wanglong Ding, Zitong Wang, Yuchen Yao, Tinghuang Wang, Yuhan Zhou

{"title":"Neutral-Point Voltage Regulation and Control Strategy for Hybrid Grounding System Combining Power Module and Low Resistance in 10 kV Distribution Network","authors":"Yu Zhou, Kangli Liu, Wanglong Ding, Zitong Wang, Yuchen Yao, Tinghuang Wang, Yuhan Zhou","doi":"10.3390/electronics13183608","DOIUrl":"https://doi.org/10.3390/electronics13183608","url":null,"abstract":"A single-phase grounding fault often occurs in 10 kV distribution networks, seriously affecting the safety of equipment and personnel. With the popularization of urban cables, the low-resistance grounding system gradually replaced arc suppression coils in some large cities. Compared to arc suppression coils, the low-resistance grounding system features simplicity and reliability. However, when a high-resistance grounding fault occurs, a lower amount of fault characteristics cannot trigger the zero-sequence protection action, so this type of fault will exist for a long time, which poses a threat to the power grid. To address this kind of problem, in this paper, a hybrid grounding system combining the low-resistance protection device and fully controlled power module is proposed. During a low-resistance grounding fault, the fault isolation is achieved through the zero-sequence current protection with the low-resistance grounding system itself, while, during a high-resistance grounding fault, the reliable arc extinction is achieved by regulating the neutral-point voltage with a fully controlled power module. Firstly, this paper introduces the principles, topology, and coordination control of the hybrid grounding system for active voltage arc extinction. Subsequently, a dual-loop-based control method is proposed to suppress the fault phase voltage. Furthermore, a faulty feeder selection method based on the Kepler optimization algorithm and convolutional neural network is proposed for the timely removal of permanent faults. Lastly, the simulation and HIL-based emulated results verify the rationality and effectiveness of the proposed method.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"47 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Transformer for Remote-Sensing Image Captioning with Positional-Channel Semantic Fusion 利用位置-信道语义融合为遥感图像添加字幕的增强变换器

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183605

An Zhao, Wenzhong Yang, Danny Chen, Fuyuan Wei

引用次数: 0

Digital Twin for Modern Distribution Networks by Improved State Estimation with Consideration of Bad Date Identification 通过考虑坏日期识别的改进状态估计实现现代配电网络的数字孪生

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183613

Huiqiang Zhi, Rui Mao, Longfei Hao, Xiao Chang, Xiangyu Guo, Liang Ji

{"title":"Digital Twin for Modern Distribution Networks by Improved State Estimation with Consideration of Bad Date Identification","authors":"Huiqiang Zhi, Rui Mao, Longfei Hao, Xiao Chang, Xiangyu Guo, Liang Ji","doi":"10.3390/electronics13183613","DOIUrl":"https://doi.org/10.3390/electronics13183613","url":null,"abstract":"With the rapid development of modern power systems, the structure and operation of distribution networks are becoming increasingly complex, demanding higher levels of intelligence and digitization. Digital twin, as a virtual cutting-edge technique, can effectively reflect the operational status of distribution networks, offering new possibilities for real-time monitoring, optimization and other functions for distribution networks. Building efficient and accurate models is the foundation of enabling a digital twin of distribution networks. This paper proposes a digital twin operating system for distribution networks with renewable energy based on robust state estimation and deep learning-based renewable energy prediction. Furthermore, the identification and correction of possible bad or missing data based on deep learning are also included to purify the input data for the digital twin system. A digital twin test platform is also proposed in the paper. A case study and evaluations based on a real-time digital simulator are carried out to verify the accuracy and real-time performance of the established digital twin system. In general, the proposed method can provide the basis and foundation for distribution network management and operation, as well as intelligent power system operation.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"411 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Human Activity Recognition on Wearable Devices Using Knowledge Distillation Techniques 利用知识提炼技术在可穿戴设备上高效识别人类活动

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-11 DOI: 10.3390/electronics13183612

Paulo H. N. Gonçalves, Hendrio Bragança, Eduardo Souto

{"title":"Efficient Human Activity Recognition on Wearable Devices Using Knowledge Distillation Techniques","authors":"Paulo H. N. Gonçalves, Hendrio Bragança, Eduardo Souto","doi":"10.3390/electronics13183612","DOIUrl":"https://doi.org/10.3390/electronics13183612","url":null,"abstract":"Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but their application on mobile and wearable devices is constrained by limited computational resources. To address this limitation, we propose a novel method called Knowledge Distillation for Human Activity Recognition (KD-HAR) that leverages the knowledge distillation technique to compress deep neural network models for HAR using inertial sensor data. Our approach transfers the acquired knowledge from high-complexity teacher models (state-of-the-art models) to student models with reduced complexity. This compression strategy allows us to maintain performance while keeping computational costs low. To assess the compression capabilities of our approach, we evaluate it using two popular databases (UCI-HAR and WISDM) comprising inertial sensor data from smartphones. Our results demonstrate that our method achieves competitive accuracy, even at compression rates ranging from 18 to 42 times the number of parameters compared to the original teacher model.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0