IEEJ Transactions on Electrical and Electronic Engineering最新文献_第3页

3D Prior Electrical Resistivity Model Building for Magnetotelluric Inversion Using Neural Network Algorithm to the Kevitsa Mineral Deposit, Finland 芬兰Kevitsa矿床大地电磁反演三维先验电阻率神经网络模型建立

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-03 DOI: 10.1002/tee.70087

Cuong Van Anh Le, Tuan Van Huynh, Trang Thi Thu Doan, Thuan Van Nguyen

{"title":"3D Prior Electrical Resistivity Model Building for Magnetotelluric Inversion Using Neural Network Algorithm to the Kevitsa Mineral Deposit, Finland","authors":"Cuong Van Anh Le, Tuan Van Huynh, Trang Thi Thu Doan, Thuan Van Nguyen","doi":"10.1002/tee.70087","DOIUrl":"https://doi.org/10.1002/tee.70087","url":null,"abstract":"Geological information can be interpreted from data collected from drill holes and geophysical method surveys. Limited number of expensive drill holes cannot provide enough information for the vast interest area, but geophysical methods can tell the soil properties through physical parameters as resistivity or inverse of conductivity and seismic velocity if their measurement stations can be sufficiently located. Useful prior geophysical parameter models play a vital role in directing their inversion flow and recovering the good, predicted earth model. Geophysical parameters and chemical elements have strong correlations. This inspires 3D electrical resistivity model building from existences of data collected from drill holes in the Kevitsa Ni-Cu-PGE mineral deposit, Northern Finland. In our work, shallow neural network is applied to predict conductivity value from available mineral chemical elements for each drill hole. Then, 3D interpolation can build 3D conductivity model from all the conductivity values as being measured or predicted. The processed 3D geophysical parameter model can show great match with seismic data and other drill hole information. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 11","pages":"1891-1895"},"PeriodicalIF":1.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196286","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

Simultaneous Estimation of Generator Inertia and Damping in Power Systems Using Short-Time Transient Data Considering Primary Frequency Regulation 考虑一次频率调节的电力系统暂态数据同步估计发电机惯量和阻尼

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-06-30 DOI: 10.1002/tee.70088

Yukai Wang, Akihiko Yokoyama, Jumpei Baba

引用次数: 0

Thermal Defect Detection of Electrical Components in Transmission Lines Based on Infrared Images and Deep Learning† 基于红外图像和深度学习的输电线路电气元件热缺陷检测[j]

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-06-23 DOI: 10.1002/tee.70060

Chuanyang Liu, Jingjing Liu, Yiquan Wu, Zuo Sun

{"title":"Thermal Defect Detection of Electrical Components in Transmission Lines Based on Infrared Images and Deep Learning†","authors":"Chuanyang Liu, Jingjing Liu, Yiquan Wu, Zuo Sun","doi":"10.1002/tee.70060","DOIUrl":"https://doi.org/10.1002/tee.70060","url":null,"abstract":"The safe operation of transmission lines will be an important guarantee for domestic economic construction, and it is urgent to improve the fault diagnosis and identification ability of electrical components in transmission lines. In order to realize electrical components identification and thermal defect detection from massive aerial images, in this study, a cascaded detection method is proposed based on infrared images and YOLO models. Firstly, two infrared datasets used for classification and localization are created, totaling 4887 infrared images. Secondly, to enhance the accuracy and robustness of electrical components identification, a similarity-based attention mechanism module, a cross-level weighted feature pyramid network, and Wise IoU are introduced to the original YOLOv7. Finally, the improved YOLOv7 model and comparative models are trained and then tested on the infrared datasets. The mAP of the improved model reaches 97.4%, which is 6% higher than that of the original YOLOv7. More importantly, by cascading the improved YOLOv7 and YOLOv7-tiny for thermal defect detection, the AP value of the proposed method (87.91%) is more than 20% higher than that of YOLOv7 (67.17%). The experimental results show that the cascaded model is superior to mainstream object detection models in electrical components identification and thermal defect detection, and it is expected to be deployed on embedded devices for real-time inspection of transmission lines. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 11","pages":"1730-1744"},"PeriodicalIF":1.1,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197024","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

Determining Reserve Power Shared by Each Consumer to Aggregate Solid Oxide Fuel Cells and Storage Batteries for Frequency Containment Reserve 确定每个消费者共享的储备功率，以聚合固体氧化物燃料电池和蓄电池以实现频率控制储备

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-06-12 DOI: 10.1002/tee.70083

Nanae Kaneko, Yu Fujimoto, Wataru Hirohashi, Yasuhiro Hayashi, Tomoyuki Hirai, Shota Tajima, Toshinari Momose

引用次数: 0

Comparative Analysis of Hybrid-Magnetic-Circuit Memory Machines with Different Leakage Flux Paths 不同漏磁路径混合磁路存储机的比较分析

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-06-02 DOI: 10.1002/tee.70068

Xifang Zhao, Heyun Lin, Xianxian Zeng, Yuxiang Zhong, Hui Yang

引用次数: 0

Applying RetinaNet Machine Learning Models for Hot-Spot Detection in Thermal Images of Photovoltaic Panel 应用retanet机器学习模型进行光伏板热图像的热点检测

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-06-01 DOI: 10.1002/tee.70067

Nguyen Phuoc Hoang Khang, Nguyen Minh Triet, Huynh Van Tuan, Nguyen Chi Nhan

{"title":"Applying RetinaNet Machine Learning Models for Hot-Spot Detection in Thermal Images of Photovoltaic Panel","authors":"Nguyen Phuoc Hoang Khang, Nguyen Minh Triet, Huynh Van Tuan, Nguyen Chi Nhan","doi":"10.1002/tee.70067","DOIUrl":"https://doi.org/10.1002/tee.70067","url":null,"abstract":"In recent years, the remarkable development of the solar power industry has been undeniable. This led to the necessity of monitoring and maintenance of solar power plants due to many issues and failures that affect the efficiency and lifespan of the system. One of the most common fault cases of solar systems is the hot spot in photovoltaic panels created by the mismatch of cells in the panel due to incompatible environmental conditions or physical damage causing the heat up in lower power produced cells. The hot spot is detected through thermal image capture by a thermal camera and analyzed through image processing techniques with manual adjustment and testing. With recent significant achievements and growth of machine learning methods in image processing and technical diagnosis, it is greatly possible to develop a machine learning model to perform hot spot detection for higher efficiency and automation. In this study, our research group proposes an application of RetinaNet to develop a model capable of detecting hot spots in photovoltaic panels through processing thermal images. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 11","pages":"1882-1890"},"PeriodicalIF":1.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196293","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

Continuous-Control-Set Model-Free Predictive Voltage Control for DAB Converters with Noise Suppression 具有噪声抑制的DAB变换器的连续控制集无模型预测电压控制

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-05-31 DOI: 10.1002/tee.70077

Zheng Yin, Fujin Deng, Yaqian Zhang, Sahar S. Kaddah, Abdelhady Ghanem, Sayed Abulanwar

引用次数: 0

On-Chip Analytical Method for Investigating Protrusive Forces in Growing Plant Roots† 芯片上分析方法研究植物根系生长中的突出力

IF 1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-05-30 DOI: 10.1002/tee.70081

Jing Li, Michitaka Notaguchi, Isaku Kanno, Hirotaka Hida

{"title":"On-Chip Analytical Method for Investigating Protrusive Forces in Growing Plant Roots†","authors":"Jing Li, Michitaka Notaguchi, Isaku Kanno, Hirotaka Hida","doi":"10.1002/tee.70081","DOIUrl":"https://doi.org/10.1002/tee.70081","url":null,"abstract":"In this study, we present an on-chip analytical method using a microfluidic device to characterize the mechanical properties in growing roots. Roots are essential organs for plants and grow under heterogeneous conditions in soil. Especially, the mechanical impedance in soil significantly affects root growth. Understanding the mechanical properties of roots and the physical interactions between roots and soil is important in plant science and agriculture. However, an effective method for directly evaluating the mechanical properties of growing roots has not been established. To overcome this technical issue, we developed a polydimethylsiloxane (PDMS) microfluidic device integrated with a cantilevered sensing pillar for measuring the protrusive force generated by the growing roots. Using the developed device, we analyzed the mechanical properties of the roots in a model plant, Arabidopsis thaliana. The root growth behavior and the mechanical interaction with the sensing pillar were recorded using a time-lapse microscopy system. We successfully quantified the mechanical properties of growing roots including the protrusive force and apparent Young's modulus based on a simple physical model considering the root morphology. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 7","pages":"1134-1140"},"PeriodicalIF":1.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255869","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

Power Deposition Characteristics of Electrically Exploding Microscale Metallic Bridges with High-Voltage Pulse-Forming Circuit 高压脉冲形成电路电爆炸微尺度金属桥的功率沉积特性

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-05-29 DOI: 10.1002/tee.70069

Kyoungjin Kim

{"title":"Power Deposition Characteristics of Electrically Exploding Microscale Metallic Bridges with High-Voltage Pulse-Forming Circuit","authors":"Kyoungjin Kim","doi":"10.1002/tee.70069","DOIUrl":"https://doi.org/10.1002/tee.70069","url":null,"abstract":"In various forms of pyrotechnic devices for aerospace and defense systems, the safety and reliability of device functioning heavily relies on the electrical explosion of a thin metallic bridgewire in an exploding bridgewire (EBW) detonator unit for the ignition of primary explosive charges. This study presents the modeling and simulation of pulsed power deposition in a gold bridgewire of micron-sized diameter which undergoes intense joule heating followed by vaporization and bridgewire burst. The present modeling approach is based on the bulk approximation of bridgewire resistance, and it includes the circuit model of a high-voltage charging capacitor discharge unit (CDU) and the energy conservation principle with the consideration of phase changes in the bridgewire. The prediction results are well validated against the EBW firing test measurements in terms of electrical current traces and burst current for a wide range of CDU charging voltages. Threshold charging voltages for bridgewire burst and explosive ignition are investigated for the cases of varying bridgewire diameter and length, which results are found to be consistent with the EBW firing test analysis. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 9","pages":"1474-1480"},"PeriodicalIF":1.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768084","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

Deep Analysis of Containerized Web Server Performance Using eBPF-Captured Data 使用ebpf捕获数据对容器化Web服务器性能进行深入分析

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-05-28 DOI: 10.1002/tee.70070

Tomonori Takagaki, Kimihiro Mizutani

引用次数: 0