IEEJ Transactions on Electrical and Electronic Engineering最新文献_第9页

Robust Myoelectric during Long-Time Use by Transfer Learning 通过迁移学习实现长时间使用时的鲁棒肌电

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-29 DOI: 10.1002/tee.70113

Meifang Xu, Zhongjie Liu

{"title":"Robust Myoelectric during Long-Time Use by Transfer Learning","authors":"Meifang Xu, Zhongjie Liu","doi":"10.1002/tee.70113","DOIUrl":"https://doi.org/10.1002/tee.70113","url":null,"abstract":"Muscle fatigue significantly impacts the accuracy of force estimation, posing a challenge for the long-term use of myoelectric interfaces. In this study, we propose a novel transfer learning-based method to enhance force estimation accuracy during muscle fatigue. The approach involves pretraining a force estimator using electromyography (EMG) data from the non-fatigue state and fine-tuning the model with data from the fatigue state. A deep end-to-end convolutional neural network was employed as the force estimator, eliminating the need for hand-crafted features and enabling direct learning from raw EMG signals. The results demonstrate a 75% reduction in the root mean square error (RMSE) of force estimation during muscle fatigue using the proposed transfer learning algorithm. Furthermore, the influence of target training dataset size on transfer learning performance was systematically analyzed, providing valuable insights into optimizing model adaptation. This study highlights the effectiveness of transfer learning in mitigating the effects of muscle fatigue on EMG-based force estimation, enabling the reliable, long-term use of myoelectric interfaces. The findings have significant implications for improving the functionality and convenience of prosthetic devices, ultimately enhancing user satisfaction and quality of life. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 2","pages":"249-257"},"PeriodicalIF":1.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983986","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

Computer Algorithms of Pseudo-Formal Linearization Based on Chebyshev Interpolation for Nonlinear Time-Varying Systems and a Nonlinear Filter 基于切比雪夫插值的非线性时变系统伪形式线性化计算机算法及非线性滤波器

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-29 DOI: 10.1002/tee.70112

Kazuo Komatsu, Hitoshi Takata

引用次数: 0

Optimization of Off-Grid Hydrogen Production System with Shared Energy Storage Plant Based on Master–Slave Game Theory 基于主从博弈论的共享储能离网制氢系统优化

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-29 DOI: 10.1002/tee.70104

Pengfei Zhang, Jiandong Duan

{"title":"Optimization of Off-Grid Hydrogen Production System with Shared Energy Storage Plant Based on Master–Slave Game Theory","authors":"Pengfei Zhang, Jiandong Duan","doi":"10.1002/tee.70104","DOIUrl":"https://doi.org/10.1002/tee.70104","url":null,"abstract":"The off-grid system (OGS) has relatively low voltage and frequency requirements, making it suitable for renewable energy generation modes such as wind and solar power. However, due to the large volatility of the power from the generation side and the upper-bound constraints on its energy supply capacity, OGS faces issues such as low energy utilization efficiency, weak generation-load power matching, and poor system economic performance. To address these challenges, flexible management on the load side is necessary to achieve optimal operation of generation-load power balance. This paper establishes a bi-level optimization model for OGS, incorporating renewable energy power plant (REPP), shared energy storage plant (SESP), and load aggregator (LA), based on master-slave game theory. The model is solved using a combination of Adaptive Differential Evolution (ADE) algorithm and the CPLEX solver. Through simulation examples, the results show that the integration of SESP into the system alleviates the issue of generation-load power mismatch, reducing the OGS's curtailment of electricity by 34.15%. This significantly improves the energy utilization efficiency, while the net profit of the REPP increases by 68.62% and the net profit of the LA rises by 6.59%, effectively achieving a win-win situation among all the stakeholders. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 2","pages":"167-177"},"PeriodicalIF":1.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994064","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

Use Machine Learning to Classify Materials Based on Gamma Scattering Spectra 利用机器学习对基于伽马散射光谱的材料进行分类

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-28 DOI: 10.1002/tee.70120

Huynh Thanh Nhan, Nguyen Duy Thong, Le Hoang Minh, Tran Thien Thanh, Chau van Tao

引用次数: 0

Analysis of a Current–Tripler–Rectifier Full-Bridge DC–DC Converter 电流三倍整流全桥DC-DC变换器分析

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-28 DOI: 10.1002/tee.70116

Jiayi Kong, Chen Zhao, Liyao Wang, Mingjin Xu, Xiaojing Liu

引用次数: 0

A Lightweight Method for Transmission Line Component Detection Based on YOLOv8n 一种基于YOLOv8n的轻型输电线路分量检测方法

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-28 DOI: 10.1002/tee.70100

Guiwen Lan, Zhan Zhong, Ruidong Guo, Hanqiang Huang, Zirui Xu, Xinyue Ren

{"title":"A Lightweight Method for Transmission Line Component Detection Based on YOLOv8n","authors":"Guiwen Lan, Zhan Zhong, Ruidong Guo, Hanqiang Huang, Zirui Xu, Xinyue Ren","doi":"10.1002/tee.70100","DOIUrl":"10.1002/tee.70100","url":null,"abstract":"Many existing methods for transmission line component detection are unfeasible to deploy on resource-constrained devices. In this paper, an improved YOLOv8n for transmission line component detection is proposed to make a balance between detection efficiency and model size by using many lightweight blocks in the YOLOv8n's neck. To pay more attention to the information of interest so as to reduce computational load, an EMA module and a lightweight RepViT block are embedded in the C2f module of the 12th layer of the neck network. A C2f_SCConv module is designed and used to replace the C2f module in the 18th layer of the neck network in order to improve feature fusion across different layers. The C2f modules in the 15th and 21st layers of the neck network are replaced with a C2f_Faster module respectively, meanwhile maintaining accuracy and reducing the number of convolutional layers to improve detection speed. The Wise-IoU loss function is chosen to make the improved model more flexible and robust in object detection. Experimental results show that the proposed model obtains a significant increase in detection accuracy with a lightweight model size. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 3","pages":"364-373"},"PeriodicalIF":1.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130394","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

Study on Electromagnetic-Temperature Field Performance of Double Squirrel-Cage Dual-Rotor Permanent Magnet Induction Motor with Different Load Conditions 不同负载条件下双鼠笼型双转子永磁感应电动机的电磁温度场性能研究

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-26 DOI: 10.1002/tee.70115

Siyuan Jiang, Junheng Niu, Sen Miao, Jingle Zhou, Xuanfeng Shangguan, Xiaozhuo Xu

{"title":"Study on Electromagnetic-Temperature Field Performance of Double Squirrel-Cage Dual-Rotor Permanent Magnet Induction Motor with Different Load Conditions","authors":"Siyuan Jiang, Junheng Niu, Sen Miao, Jingle Zhou, Xuanfeng Shangguan, Xiaozhuo Xu","doi":"10.1002/tee.70115","DOIUrl":"https://doi.org/10.1002/tee.70115","url":null,"abstract":"The permanent magnet rotors of the Permanent Magnet Induction Motor (PMIM) are currently designed to rotate freely. There are relatively few studies on the operation of permanent magnet rotors under load. This paper presents a thorough examination of the performance of Double Squirrel-Cage Dual-Rotor Permanent Magnet Induction Motors (DDPMIM) under varying load distribution ratios. The analysis includes permanent magnet demagnetization, steady-state operational characteristics, and the distribution of the three-dimensional temperature field. This study aims to fill the research gap in this area. First, the working principle of DDPMIM is analyzed. Second, the influence of the load distribution ratio on permanent magnet demagnetization is analyzed. Furthermore, the effects of the load distribution ratio on the efficiency, power factor, and temperature field of the motor are discussed during steady-state operation. The research demonstrates that compared with the DDPMIM with load running on the squirrel-cage rotor, whether the permanent magnet rotor is loaded alone or the squirrel-cage and the permanent magnet rotor share the load together, the efficiency of DDPMIM is improved and the temperature is reduced, but the risk of permanent magnet irreversible demagnetization is increased. The research results suggest a new practical application for the DDPMIM. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 2","pages":"267-276"},"PeriodicalIF":1.1,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983972","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

An Optimization Method for Coil Design Problems Based on Physarum Solver and Evolutionary Computation 基于绒泡菌求解和进化计算的线圈设计问题优化方法

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-26 DOI: 10.1002/tee.70106

Takahiro Sato, Kengo Sugahara, Yuki Hidaka

引用次数: 0

Enhancing Electricity Demand Forecasting Based on a Hybrid Deep-Learning Framework 基于混合深度学习框架增强电力需求预测

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-26 DOI: 10.1002/tee.70101

Shuo Sun, Zhendong Cui, Dong Zhang, Jianhui Wang

{"title":"Enhancing Electricity Demand Forecasting Based on a Hybrid Deep-Learning Framework","authors":"Shuo Sun, Zhendong Cui, Dong Zhang, Jianhui Wang","doi":"10.1002/tee.70101","DOIUrl":"10.1002/tee.70101","url":null,"abstract":"Accurate electricity demand forecasting is crucial for the stable operation of the power system. A hybrid electricity demand forecasting framework, CVS-iLSTNet, was proposed, which integrates Complete Ensemble Empirical Mode Decomposition (CEEMDAN), Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Network (LSTNet) to enhance forecasting accuracy and robustness. First, feature selection combining Recursive Feature Elimination (RFE) and Random Forest (RF) analyzes the relationship between electricity demand and influencing factors. Second, the Kolmogorov-Arnold Network (KAN) layer is introduced to improve its nonlinear representation capabilities. Next, CEEMDAN decomposes the raw time series data to extract intrinsic modal functions (IMFs). VMD further decomposes these IMFs to extract finer modes, which are then combined with the corresponding IMFs. Finally, SSA-iLSTNet enhances the prediction of complex patterns by hyperparameter optimization and combining multiple network structures and algorithms. Experiments were conducted using real electricity demand data from Spain and the United States. The results demonstrate that the proposed model delivers reliable and timely predictions both in short-term and long-term demand forecasting, outperforming state-of-the-art models. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 3","pages":"331-340"},"PeriodicalIF":1.1,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130289","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

Evaluation of Line Side Prospective Transient Recovery and Determination of Current Zero Crossing for Short Line Fault Interruption 短路故障中断线侧前瞻性暂态恢复评估及电流过零的确定

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-26 DOI: 10.1002/tee.70105

Tadashi Koshizuka, Amir-Pouya Azad

引用次数: 0