IEEJ Transactions on Electrical and Electronic Engineering最新文献

Modeling Techniques for Light Distribution of White LEDs 白光led光分布的建模技术

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-02-16 DOI: 10.1002/tee.70000

Tomoaki Kashiwao, Kenji Ikeda, Masaru Tsumori, Kanji Bando

引用次数: 0

Classification Method for Fatigue Driving Signals Based on Multiple Classifier Analysis 基于多分类器分析的疲劳驾驶信号分类方法

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-01-23 DOI: 10.1002/tee.24260

Zhendong Mu

{"title":"Classification Method for Fatigue Driving Signals Based on Multiple Classifier Analysis","authors":"Zhendong Mu","doi":"10.1002/tee.24260","DOIUrl":"https://doi.org/10.1002/tee.24260","url":null,"abstract":"This study constructs an ensemble learning model under several classifiers by optimizing the hyperparameters of the base classifier to address the low accuracy issue of fatigue driving detection that uses traditional classifiers. In this study, the fatigue driving electroencephalogram (EEG) signals of 26 participants were analyzed using various classifiers, namely, k-nearest neighbor, back-propagation neural network, support vector machine, random forest, Gaussian naive Bayes, and quadratic discriminant analysis, as base classifiers. This study also used 10-fold cross-validation to evaluate the model and four ensemble learning methods, namely, bagging, boosting, stacking, and voting, for comparative analysis. Through the analysis of the EEG signals of the 26 participants, a conclusion could be drawn that the average recognition rate of the ensemble learning model for the participants was improved to 95% after hyperparameter optimization of the base classifier. Moreover, an ensemble learning model was constructed under multiple classifiers to improve the recognition rate of fatigue driving signals. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 4","pages":"647-655"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Research on a Novel Online Obstacle Avoidance Algorithm in an Asymmetric Teleoperation 一种新的非对称遥操作在线避障算法研究

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-01-23 DOI: 10.1002/tee.24259

Hua Chen, Baoyu Shi

{"title":"Research on a Novel Online Obstacle Avoidance Algorithm in an Asymmetric Teleoperation","authors":"Hua Chen, Baoyu Shi","doi":"10.1002/tee.24259","DOIUrl":"https://doi.org/10.1002/tee.24259","url":null,"abstract":"Teleoperation robots are being used more and more widely. The safety issues of robots during operation are increasingly attracting people's attention; In actual dangerous environments, during the operation of the teleoperation control system, the real-time presence of obstacles com-presses the robot's safe workspace, leading to the failure of planned paths. This study is mainly about a robot online obstacle avoidance algorithm based on offline trajectory, aiming at the problems that ensure that the slave–robot is not affected during operation. Besides, this algorithm achieves the goal of avoiding obstacles by selecting obstacle avoidance parameters and allowing the robot to switch between primary and secondary movements in real-time. Two robot motion scenarios were selected in the article. One is obstacle avoidance with a single arm of slave–robot and the other is coordinated obstacle avoidance with a dual-arm robot for simulation experiments. The simulation experiment results showed that the algorithm proposed in this article is suitable for the designed asymmetric teleoperation system, the slave–robot can actively avoid obstacles under the control of the main robot. The algorithm perfectly satisfied the industry standards, and meet the design requirements of the teleoperation control system. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 4","pages":"656-664"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Statistical Reliability of Data-Driven Science and Technology 数据驱动科学技术的统计可靠性

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-01-21 DOI: 10.1002/tee.24262

Ichiro Takeuchi

引用次数: 0

Band Correlation-Based Multichannel Multiscale Convolution Network for Intelligent Interference Recognition 基于频带相关的多通道多尺度卷积网络智能干扰识别

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2024-12-17 DOI: 10.1002/tee.24226

Xiang Wang, Zining Zhao, Qi Wu, Haitao Xiao, Gang Li, Yibo Zhou, Wenjie Wang

{"title":"Band Correlation-Based Multichannel Multiscale Convolution Network for Intelligent Interference Recognition","authors":"Xiang Wang, Zining Zhao, Qi Wu, Haitao Xiao, Gang Li, Yibo Zhou, Wenjie Wang","doi":"10.1002/tee.24226","DOIUrl":"https://doi.org/10.1002/tee.24226","url":null,"abstract":"In recent years, with the development and extensive application of wireless communication technology, the communication system should have stronger anti-Jamming ability. Therefore, interference recognition is particularly important as a prerequisite for anti-interference. However, the existing traditional and intelligent interference recognition algorithms have problems such as complicated feature extraction and low recognition accuracy under low interference-to-noise ratio. In order to solve the above problems, this paper introduces parallel multi-channel multi-scale convolution to improve the speed and accuracy of network recognition. In addition, combined with frequency band correlation and long-short-term memory network (LSTM), an innovative wireless communication interference identification model based on frequency band correlation is proposed, which uses LSTM to detect the frequency band correlation of interference signals and improve the accuracy of interference identification under low Jamming noise ratio (JNR). Experiments prove that the model proposed in this article has faster recognition speed and better generalization. The introduction of frequency band correlation increases the recognition accuracy to more than 99% with low JNR. Therefore, the model proposed in this paper is an effective and available model in complex electromagnetic environments. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 5","pages":"736-748"},"PeriodicalIF":1.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Detection and Classification of Surface Cracks Using Deep Learning Based Autoencoders in Eddy Current Testing 涡流检测中基于深度学习自编码器的表面裂纹检测与分类

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2024-12-16 DOI: 10.1002/tee.24243

Barrarat Fatima, Helifa Bachir, Bensaid Samir, Rayane Karim, Lefkaier IbnKhaldoun

{"title":"Detection and Classification of Surface Cracks Using Deep Learning Based Autoencoders in Eddy Current Testing","authors":"Barrarat Fatima, Helifa Bachir, Bensaid Samir, Rayane Karim, Lefkaier IbnKhaldoun","doi":"10.1002/tee.24243","DOIUrl":"https://doi.org/10.1002/tee.24243","url":null,"abstract":"Industrial equipment subjected to rigorous conditions of high speed and pressure leads to the development of cracks on metal surfaces. These cracks reduce the service life and threaten the safety of parts, and the deeper the crack, the greater the resulting damage. Crack detection and crack depth evaluation continue to take center stage in quantitative non-destructive testing and evaluation (NDT&E 4.0). The accuracy of the rotating uniform eddy current (RUEC) probe in achieving fast and efficient detection of surface cracks is corroborated by a comparison with previous experimental results. Next, accurate crack depth classification is achieved by building deep learning model based on a sparse autoencoder (SAE) and a multi-layer perceptron (MLP) model. These classifiers are combined with eddy current testing (ECT) data, including the normal magnetic component Bz. As a result, evaluation metrics such as accuracy increased by up to 100% with both precision and recall scores of 1 for the deep sparse autoencoder classifier compared to MLP performance. The originality of our approach is evident in the application of deep SAE, which achieves high classification accuracy. Furthermore, the integration of our high-resolution NDT&E RUEC probe with advanced machine learning models for depth classification is both novel and impactful. This unique combination offers a comprehensive framework for crack analysis, from precise detection to detailed characterization. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 5","pages":"676-687"},"PeriodicalIF":1.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Trends in High Voltage Switchgear Research and Technology 高压开关设备研究与技术的发展趋势

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2024-12-15 DOI: 10.1002/tee.24244

Martin Seeger, Felipe Macedo, Uwe Riechert, Markus Bujotzek, Arman Hassanpoor, Jürgen Häfner

{"title":"Trends in High Voltage Switchgear Research and Technology","authors":"Martin Seeger, Felipe Macedo, Uwe Riechert, Markus Bujotzek, Arman Hassanpoor, Jürgen Häfner","doi":"10.1002/tee.24244","DOIUrl":"https://doi.org/10.1002/tee.24244","url":null,"abstract":"High voltage switchgear is an essential element for the transformation of energy systems towards sustainable and low carbon footprint technologies by electrification of society and industry. This contribution highlights some important research and technology trends in high voltage (HV) switchgear development for reaching greener and smarter electricity transmission systems. In AC transmission, the focus is on the replacement of SF6, which is a strong greenhouse gas, in HV switchgear. Condition assessment is an important field within the “digitalization” of transmission systems to ensure reliability at reduced costs. Research activities and trends in these fields are discussed. Furthermore, HVDC transmission systems will be important for the future electricity system. As more point-to-point links are built, and as the need for HVDC transmission increases with a growing integration of renewables and rising demand for electricity, more complex multi-terminal HVDC grid topologies appear. Activities in this field are also presented with a focus on HVDC circuit-breakers and gas-insulated HVDC systems, which have been emerging in the last years. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 3","pages":"322-338"},"PeriodicalIF":1.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tee.24244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VMD-Based Feature Extraction and Enhanced GWO-DBN for Health Assessment of Automatic Transfer Switching Equipment 基于vmd的特征提取和增强的GWO-DBN用于自动交换设备健康评估

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2024-12-12 DOI: 10.1002/tee.24242

Guojin Liu, Yuze Yang, Daming Liu, Lekang Wang

{"title":"VMD-Based Feature Extraction and Enhanced GWO-DBN for Health Assessment of Automatic Transfer Switching Equipment","authors":"Guojin Liu, Yuze Yang, Daming Liu, Lekang Wang","doi":"10.1002/tee.24242","DOIUrl":"https://doi.org/10.1002/tee.24242","url":null,"abstract":"This article proposes a method for assessing the health condition of automatic transfer switching equipment (ATSE) during the switching process. The method combines variational mode decomposition (VMD) with deep belief networks (DBN) for non-invasive monitoring and fault diagnosis. First, the VMD method is introduced to address mode mixing, using sample entropy to determine the decomposition iterations of VMD. Wavelet packet energy entropy is then extracted as the feature for health condition assessment. Subsequently, the Gray Wolf Optimization (GWO) algorithm is enhanced with a nonlinear convergence factor and a dynamic weight strategy to improve performance and avoid local optima. The enhanced GWO is used to optimize the network parameters of the DBN, which then serves as the pattern recognition algorithm for assessing ATSE health. Comparative experimental analysis demonstrates that the proposed method effectively addresses the health condition assessment of ATSE vibration signals, exhibiting high diagnostic accuracy. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 5","pages":"801-811"},"PeriodicalIF":1.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Automotive Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment 基于计算机人工智能环境的汽车安全辅助驾驶技术

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2024-12-12 DOI: 10.1002/tee.24238

Haibo Yan

{"title":"Automotive Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment","authors":"Haibo Yan","doi":"10.1002/tee.24238","DOIUrl":"https://doi.org/10.1002/tee.24238","url":null,"abstract":"A reasonable driving behavior decision model can choose the appropriate driving behavior according to the actual situation, thus improving the safety and efficiency of driving. To achieve an intelligent and humanized driving experience, this study explores the decision-making process behind driving behaviors. We have established a decision-making model for driving behaviors rooted in the finite state machine (FSM) paradigm. This model selects the most suitable driving action based on the car's current state, the surrounding environment, and the driver's intention. Given the intricate and varied nature of driving behaviors, we have incorporated a deep reinforcement learning (DRL) algorithm. This enables the optimization of decision-making strategies through dynamic interactions between the driver and the environment. Our findings reveal that this model adeptly handles complexities in real-world driving scenarios, thereby enhancing driving safety. In automotive contexts, FSM ensures the selection of apt driving actions aligned with the vehicle's status, environmental cues, and the driver's intentions. This innovative model surpasses traditional decision-making frameworks, paving the way for advancements in intelligent driving technology, and demonstrating remarkable adaptability and potential for further optimization. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 4","pages":"634-646"},"PeriodicalIF":1.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Risk-Averse Distributionally Robust Environmental-Economic Dispatch Strategy Based on Renewable Energy Operation: A New Improved Whale Optimization Algorithm 基于可再生能源运行的分布式鲁棒环境经济调度策略：一种新的改进鲸鱼优化算法

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2024-12-10 DOI: 10.1002/tee.24239

Yubing Liu, Guangkuo Gao, Wenhui Zhao

{"title":"Risk-Averse Distributionally Robust Environmental-Economic Dispatch Strategy Based on Renewable Energy Operation: A New Improved Whale Optimization Algorithm","authors":"Yubing Liu, Guangkuo Gao, Wenhui Zhao","doi":"10.1002/tee.24239","DOIUrl":"https://doi.org/10.1002/tee.24239","url":null,"abstract":"Although the use of optimization techniques for environmental and economic dispatch of integrated electricity and natural gas systems has been widely applied, there are still significant challenges in meeting the multiple energy demands of conventional and renewable energy sources, mainly wind-solar. In this study, Wasserstein distance is introduced to measure the randomness of wind-solar power generation to construct the uncertainty set. A multi-objective distributionally robust optimization (MODRO) environmental-economic scheduling model for risk aversion that minimizes the risk cost, the system operation cost, and the carbon emission cost is proposed to achieve the balance between the risk cost, the operation cost, and the pollutant emission. To solve the model efficiently, the multi-objective whale optimization algorithm (IMOWOA) was improved and used the 4-node power system and the 7-node natural gas system as case studies. The results show that the MODRO environmental-economic scheduling model can measure the operational risk due to the stochastic fluctuation of wind-solar energy sources, and provide an effective decision-making tool for policymakers. Considering P2G technology and gas turbines at the same time, it promotes the coupled operation of electric-gas integrated systems and achieves good economic efficiency. Thus, the model provides an effective solution for the stability, economy, and cleanliness of the integrated electric gas system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 5","pages":"696-711"},"PeriodicalIF":1.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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