IEEJ Transactions on Electrical and Electronic Engineering最新文献_第2页

EEG-Based Emotion Recognition Using Optimized Logit Boost and Relief Feature Selection 基于脑电图的情绪识别，优化Logit增强和缓解特征选择

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-04-24 DOI: 10.1002/tee.70312

Huy Tran Do, Thinh Xuan Huynh, Nhan Chi Nguyen, Viet Quoc Huynh, Tuan Van Huynh

{"title":"EEG-Based Emotion Recognition Using Optimized Logit Boost and Relief Feature Selection","authors":"Huy Tran Do, Thinh Xuan Huynh, Nhan Chi Nguyen, Viet Quoc Huynh, Tuan Van Huynh","doi":"10.1002/tee.70312","DOIUrl":"https://doi.org/10.1002/tee.70312","url":null,"abstract":"This study proposed a robust framework for recognizing affective states in the valence–arousal space from electroencephalogram signals because the framework combined Relief-based feature selection with LogitBoost classifier and used systematic hyperparameter optimization. The model delivered accuracies of 80.70% for valence and 82.30% for arousal on the DEAP dataset because the pipeline emphasized compact feature subsets and efficient boosting, and the model also reduced computation relative to complex deep networks that demand heavy resources. The Relief began with 140 features and emphasized right frontal and occipital attributes, with F8, O2, AF4, and F4 contributing most because these sensors captured prefrontal control and visual processing linked to emotion. The optimized hyperparameters diverged for the two affective dimensions because the learning rate η stabilized at 0.1 for valence and 0.2 for arousal, which indicated distinct convergence behaviors that reflected different neural substrates and signal statistics across dimensions. These differences suggested a principled path to specialized models that target individual emotional axes because dimension-specific tuning improved stability and yielded better generalization. Together, focused selection and careful optimization offered a credible alternative to deep learning because the approach delivered accurate, adaptive, and compute-efficient EEG emotion recognition suited to clinical and BCI use. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1756-1766"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100629","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

A Novel Communication-Less Control Method for PV-ESS DC Microgrids 一种新型的PV-ESS直流微电网无通信控制方法

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-03-13 DOI: 10.1002/tee.70275

Xiaolan Wu, Haichang Guo, Jingyu Li, Yongzhi Yang, Zhifeng Bai, Guifang Guo

{"title":"A Novel Communication-Less Control Method for PV-ESS DC Microgrids","authors":"Xiaolan Wu, Haichang Guo, Jingyu Li, Yongzhi Yang, Zhifeng Bai, Guifang Guo","doi":"10.1002/tee.70275","DOIUrl":"https://doi.org/10.1002/tee.70275","url":null,"abstract":"In direct current (DC) microgrids, traditional droop control methods facilitate power sharing and stabilize bus voltage. However, this communication-less approach is constrained by challenges including insufficient voltage restoration and imbalances in battery state of charge (SOC). This paper introduces a novel communication-less control strategy designed to address these limitations in photovoltaic energy storage system (PV-ESS) DC microgrids. This method assigns the energy storage unit (ESU) closest to the critical load as the ‘master unit’ setting its output voltage to nominal levels. This master unit injects an alternating current (AC) signal into the DC bus, with the signal frequency modulated by its SOC and output current. Additional ESUs, referred to as ‘slave units’ adjust their output currents based on the frequency of the AC signal and their local SOC. Furthermore, the master unit utilizes a logical signal based on its SOC to switch the photovoltaic module between droop control mode and maximum power point tracking (MPPT) mode. Stability analysis verifies the robustness of the proposed method, and simulation results demonstrate that it surpasses conventional droop control approaches. Specifically, the proposed method enhances load-sharing precision, minimizes voltage deviation, and ensures SOC equalization across the DC microgrid without the need for communication links. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1623-1633"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100305","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

A Method for Recognizing and Classifying Arc Sound Signals in Low-Current Faults 小电流故障中电弧声信号的识别与分类方法

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-05-04 DOI: 10.1002/tee.70284

Lingzhi Wang, Tian Wu, Haojie Wei, Peng Li, Ziheng Pu, Chunhua Fang

{"title":"A Method for Recognizing and Classifying Arc Sound Signals in Low-Current Faults","authors":"Lingzhi Wang, Tian Wu, Haojie Wei, Peng Li, Ziheng Pu, Chunhua Fang","doi":"10.1002/tee.70284","DOIUrl":"https://doi.org/10.1002/tee.70284","url":null,"abstract":"Accurately identifying faulty electrical signals in distribution networks remains challenging. However, arc sound signals provide effective features for fault recognition. Due to the complex operational environment of distribution network lines, which are susceptible to interference from environmental noise, several issues arise, including the difficulty in determining optimal parameters for noise reduction, the challenge of extracting effective features from arc sound signals, and the relatively low classification accuracy. To address these challenges, a recognition method based on architectural optimizations of the VGG16 network, combined with machine learning techniques, is proposed for arc fault recognition in distribution networks. First, the Whale Optimization Algorithm is used to optimize the parameters of Variational Mode Decomposition to denoise five types of arc sound signals collected under laboratory conditions. Next, the sound signals in the dataset are transformed into Mel spectrograms, which are then fed into the improved VGG16 neural network for feature extraction. Finally, a combined classifier is used to categorize the sound signals, facilitating fault diagnosis. The experimental results show that the proposed method significantly improves the extraction effect of arc glow sound signal features and has higher accuracy in the recognition and classification of arc glow sound signals compared to existing methods. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1718-1727"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099343","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

Identification of Photovoltaic Cell Parameters Using Mayflies Optimization Algorithm with Lévy Mutation 基于lsamvy突变Mayflies优化算法的光伏电池参数辨识

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-03-07 DOI: 10.1002/tee.70273

Wei Yang, Lulu Cai, Qiyun Ge, Youxi Wei, Chundi Jiang, Fei Wu

{"title":"Identification of Photovoltaic Cell Parameters Using Mayflies Optimization Algorithm with Lévy Mutation","authors":"Wei Yang, Lulu Cai, Qiyun Ge, Youxi Wei, Chundi Jiang, Fei Wu","doi":"10.1002/tee.70273","DOIUrl":"https://doi.org/10.1002/tee.70273","url":null,"abstract":"Deriving the true parameters of photovoltaic cells' model from measured data is an essential issue for providing reasonable analysis, simulation, optimization and control of photovoltaic systems. However, the parameter identification of PV cells still faces great difficulty due to its nonlinearity and multi-modality. To address this issue, an improved mayflies optimization algorithm, called Lévy mutation mayflies optimization algorithm (LM-MOA) is proposed to identify the parameters of PV cells by minimizing the error between the simulated and measured current. In LM-MOA, a mutation operation based on Lévy random number is performed on each mayflies after updating their position, which enables mayflies to perform more efficient search in an enlarged range. In this way, the global search ability of mayflies is enhanced. The performance of LM-MOA is examined through extensive experiments in identifying the parameters of single diode model, double diode model and PV module. Experimental results show that the performance of LM-MOA is superior to most existing meta-heuristic algorithms and can obtain more accurate and reliable identification results. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1694-1707"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100028","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

The Influence of Audio Information Manipulation on the Perceived Atmosphere in Telepresence Systems for Remote Work 音频信息处理对远程办公网真系统感知氛围的影响

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-04-10 DOI: 10.1002/tee.70285

Ariyasu Ando, Yoshihiro Konagaya, Issei Hashimoto, Akihito Kobayashi, Kent Nagumo, Akio Nozawa

{"title":"The Influence of Audio Information Manipulation on the Perceived Atmosphere in Telepresence Systems for Remote Work","authors":"Ariyasu Ando, Yoshihiro Konagaya, Issei Hashimoto, Akihito Kobayashi, Kent Nagumo, Akio Nozawa","doi":"10.1002/tee.70285","DOIUrl":"https://doi.org/10.1002/tee.70285","url":null,"abstract":"Remote work, which enables employees to work from home, has emerged as a prominent working style in recent years. However, unlike traditional office environments where the ‘atmosphere of the space’ is naturally shared, remote work relies on screen-based communication, making it challenging to convey this atmosphere. This challenge has been associated with communication gaps, reduced team cohesion, a diminished sense of belonging, and heightened feelings of loneliness. This study investigates the relationship between the manipulation of audio information and the perceived ‘atmosphere of the space’ in telepresence systems for remote work. Building on prior research suggesting that audio with ‘high individuality and low context’ is suitable for conveying atmosphere, this study examines how different manipulations of audio information influence perception. The investigation explores two key themes: first, the psychological effects of transmitting audio at various information levels in a simulated remote work environment. Second, it examines the use of unintelligible audio, where linguistic meaning is suppressed to prioritize individuality while minimizing context, and investigates its influence on the perceived atmosphere. This approach exploratorily investigates the connection between sound characteristics and the transmission of ambiance, providing a foundation for designing audio processing for telepresence. © 2026 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1675-1683"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tee.70285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100029","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

Measuring Magnetostriction in Electric Motor Stator Cores Considering Manufacturing Factors and Material Anisotropy 考虑制造因素和材料各向异性的电机定子铁心磁致伸缩测量

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-03-15 DOI: 10.1002/tee.70277

Long Chen, Hainan Huang, Tong Ben, Bo Yin

{"title":"Measuring Magnetostriction in Electric Motor Stator Cores Considering Manufacturing Factors and Material Anisotropy","authors":"Long Chen, Hainan Huang, Tong Ben, Bo Yin","doi":"10.1002/tee.70277","DOIUrl":"https://doi.org/10.1002/tee.70277","url":null,"abstract":"Magnetostriction in non-oriented electrical steel constitutes the primary excitation mechanism for vibration in motor stator cores. While magnetostrictive anisotropy stems from a combination of intrinsic material characteristics and extrinsic factors—including grain size heterogeneity, residual stresses, and crystallographic texture—the manufacturing processes of stator cores introduce significant additional residual stresses. These process-induced stresses further degrade magnetostrictive performance. Prevailing research, predominantly conducted on unprocessed silicon steel laminations under idealized conditions, has substantially underestimated magnetostrictive behavior in operational cores. To address this limitation, this study implemented a magnetostriction measurement system utilizing resistance strain gauges. Comprehensive characterization was performed on stator cores manufactured from 25SW1300-grade steel, with particular emphasis placed on quantifying the anisotropy mechanisms and the specific influence of manufacturing processes. Obtained results found that the stator core loss exceeded that of single-piece silicon steel sheets by more than 8%. Critically, localized magnetostrictive strain was found to be amplified by welded operations by 3 μm/m, representing a twofold increase relative to non-welded regions. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1600-1608"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100063","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

Experimental Evaluation of Sample-Number Dependence of Spatial Resolution in PPRM-OCDR PPRM-OCDR空间分辨率样本数依赖性实验评价

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-05-31 DOI: 10.1002/tee.70325

Yuta Higa, Keisuke Motoda, Takaki Kiyozumi, Yosuke Mizuno

引用次数: 0

Acquisition and Classification of a Surface EMG Dataset for Finger Movement 手指运动表面肌电信号数据集的获取与分类

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-07-03 DOI: 10.1002/tee.70371

Thuan Nguyet Phan, Thuy Nguyen Thi Le, Tuan Van Huynh

{"title":"Acquisition and Classification of a Surface EMG Dataset for Finger Movement","authors":"Thuan Nguyet Phan, Thuy Nguyen Thi Le, Tuan Van Huynh","doi":"10.1002/tee.70371","DOIUrl":"https://doi.org/10.1002/tee.70371","url":null,"abstract":"Electromyograms (EMGs) are signals generated by changes in the membrane potential of living cells when they are stimulated. There have been many studies using electromyographic (EMG) signals to recognize hand gestures; however, most of them focus on wrist and hand gestures due to more pronounced muscle contractions compared to finger gestures. In this study, we propose a classifier for finger gestures based on an electromyography dataset self-collected from 10 subjects. The EMG signals, collected from the fixed electrodes of the Muscle BioAmp Patchy sensor from UpSideDown Labs, have six features extracted from their time domain and then used in Artificial Neural Network and K-Nearest Neighbors classification models. The experimental results show that the highest classification accuracy achieved was 85.4% ± 2.9% for recognizing 10 different finger gestures. Even though only a single signal channel with a simple configuration was used, the system still achieved an acceptable classification accuracy for recognizing 10 different finger gestures, demonstrating the feasibility of EMG-based finger gesture recognition despite weaker muscle activations compared to wrist and hand gestures. The method we propose provides a cost-effective solution while ensuring efficiency, making it suitable for deployment in practical applications and reducing computational costs. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1751-1755"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099472","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

Optically-Driven Wireless Microelectronics for IoT and Wearable Biomedical Devices 用于物联网和可穿戴生物医学设备的光驱动无线微电子

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-10-04 Epub Date: 2026-09-02 DOI: 10.1002/tee.70422

Takashi Tokuda, Masato Saito

引用次数: 0

An Industrial Load Identification Method for Distribution Grid Smart Terminal 配电网智能终端的工业负荷识别方法

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-09-02 Epub Date: 2026-06-30 DOI: 10.1002/tee.70272

Shuaitao Bai, Yuwei Shang, Wanxing Sheng, Limei Zhou, Haijun Xiong, Ling Zhou

{"title":"An Industrial Load Identification Method for Distribution Grid Smart Terminal","authors":"Shuaitao Bai, Yuwei Shang, Wanxing Sheng, Limei Zhou, Haijun Xiong, Ling Zhou","doi":"10.1002/tee.70272","DOIUrl":"https://doi.org/10.1002/tee.70272","url":null,"abstract":"This paper proposes a non-intrusive industrial load identification method for distribution grid smart terminals using low-frequency power data. Existing high-frequency feature-based methods face challenges such as high sampling costs and processing complexity in industrial scenarios. To address this, a hybrid CNNSE2-LSTM model is developed: a 1D convolutional network with Squeeze-Excitation blocks (CNNSE2) extracts spatial features by enhancing local correlations and recalibrating channel weights, while a parallel LSTM captures long-term temporal dependencies. The model is trained on 15-min interval smart terminal data from a manufacturing enterprise, covering three environmental protection devices and four production devices. Experimental results show average accuracy, precision, recall, F1 score, and G-means exceeding 90%, outperforming LSTM, CNN-LSTM, and attention-based FCN-LSTM models. The method reduces reliance on costly high-frequency sampling while maintaining high identification accuracy, offering a cost-effective solution for grid security and energy management in industrial applications. Future work will focus on improving generalization for diverse equipment via transfer learning and unsupervised techniques. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 10","pages":"1440-1451"},"PeriodicalIF":1.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148862551","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