2022 IEEE Industrial Electronics and Applications Conference (IEACon)最新文献

Investigation of Switching and Figure of Merits in New Charge Balanced n-Vertical Trench Epilayer p⁺ -Shield Super Junction Accumulation Mode MOSFET 新型电荷平衡n-垂直沟槽涂层p + -屏蔽超级结积累模式MOSFET的开关性能及优点图研究

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951835

M. E. Lodhi, S. Loan, A. Q. Ansari

{"title":"Investigation of Switching and Figure of Merits in New Charge Balanced n-Vertical Trench Epilayer p⁺ -Shield Super Junction Accumulation Mode MOSFET","authors":"M. E. Lodhi, S. Loan, A. Q. Ansari","doi":"10.1109/IEACon55029.2022.9951835","DOIUrl":"https://doi.org/10.1109/IEACon55029.2022.9951835","url":null,"abstract":"In this work, we have simulated a charged balanced new vertical Trench Epilayer p⁺ -Shield Accumulation Mode MOSFET device (SJnVTEpSPMOS) with Super Junction in its drift region. This modifies the tradeoff relation between RON and Breakdown voltage (BV) using ATLAS 2-D numerical simulation incorporating different physics models, breaking the traditional Silicon Limit. Our proposed device has the gate and drain well isolated due to the presence of p⁺ -shield at the bottom of the Trench, Also the channel is inaccessible from the super junction drift layer. The proposed device shows a significant outcome in term of Breakdown voltage, switching and FOM comparing with the Conventional Accumulation Mode MOSFET. We get high breakdown voltage of 31. 0v, Gate to drain charge value as low as 23.6 pC, FOM of 22.65 m$Omega$.nC with device cell pitch of 3$mu$. Our further simulation study shows a significant decrease in Gate to drain Capacitance. We have introduced for the first time the advantage of vertical n Epilayer with lateral (horizontal) p⁺ -shield along with Super Junction in drift region, with great improvement in both Baliga’s FOMs.","PeriodicalId":197307,"journal":{"name":"2022 IEEE Industrial Electronics and Applications Conference (IEACon)","volume":"34 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123460325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a Modified 63-Level Asymmetric Multi-Level Inverter to Minimize THD 一种改进的63电平非对称多电平逆变器的研制

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951803

Md. Rafiul Islam, Md. Mahmudul Hasan, R. Kannan

引用次数: 1

Wearable Women Safety Device 可穿戴式女性安全装置

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951850

Chandan Gautam, A. Patil, Akanksha Podutwar, Maitreyee Agarwal, Pranali Patil, Apurva A Naik

引用次数: 3

Expansion of Sign Algorithm and Sensitivity Analysis for Adaptive Bandpass Filters With Tunable Center-Frequency 中心频率可调自适应带通滤波器的符号算法扩展及灵敏度分析

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951759

Y. Hinamoto, A. Doi, S. Nishimura

引用次数: 0

Fault detection system of single-phase grounding based on electric field sensor 基于电场传感器的单相接地故障检测系统

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951738

Xianmin Mu, Qiyu Sheng, Jialin Li, Siqi Yu, Jiahao Zhang

{"title":"Fault detection system of single-phase grounding based on electric field sensor","authors":"Xianmin Mu, Qiyu Sheng, Jialin Li, Siqi Yu, Jiahao Zhang","doi":"10.1109/IEACon55029.2022.9951738","DOIUrl":"https://doi.org/10.1109/IEACon55029.2022.9951738","url":null,"abstract":"In order to ensure the safety of power workers, the electric field strength value around the transmission line can be obtained in real time, and the ground fault line can be found at the first time. In this paper, a single-phase ground fault detection system based on electric field sensor is proposed. The low-pass conditioning circuit is used to collect the induced voltage generated by the sensing unit in the electric field, and then the electric field strength near the line is calculated in real time; Perform finite element simulation on single-phase grounding faults of transmission lines to observe changes in the electric field in space; Establish an ESP-NOW protocol communication system based on the ESP32 module, collect the measurement data of the electric field sensor and transmit it to the QT software interface for display;;Build a power frequency uniform electric field test platform, measure and correct the electric field strength of the PCB sensing unit, and verify the COMSOL electric field strength simulation value; Use the verified electric field strength simulation value to calibrate the electric field measurement and sensing unit of the parallel semicircle plate, establish a transmission line model in the laboratory, and measure the electric field strength before and after the single-phase ground fault occurs in the transmission line. Through the electric field intensity measurement curve, it is verified that the electric field sensor can obtain the change of electric field intensity and the fault line information in real time after the single-phase grounding fault occurs in the transmission line, so as to ensure personal safety.","PeriodicalId":197307,"journal":{"name":"2022 IEEE Industrial Electronics and Applications Conference (IEACon)","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131704269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and fabrication of threat alerting system for continuous monitoring of patients with seizure and heart attack risk using IoT 利用物联网设计和制造持续监测癫痫和心脏病发作风险的威胁警报系统

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951826

Melanathuru Sahil, K. L. Vasudev, A. Abhiram, Sunkesula Hasan Basha

引用次数: 0

Adaptive Algorithm Based Sensor-less Dual-Axis Solar Tracking System 基于自适应算法的无传感器双轴太阳跟踪系统

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951747

Vasant Badugu, Prabal Bhatnagar, Jarnu Girdhar, N. Nambath

引用次数: 1

Data-driven lean blowout prediction based on industrial dry low emission gas turbine dataset Using decision tree 基于决策树的工业干式低排放燃气轮机数据驱动精益井喷预测

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951749

Abdulrahman Abdullah Bahashwan, R. Ibrahim, M. Omar, Moch. Faqih

{"title":"Data-driven lean blowout prediction based on industrial dry low emission gas turbine dataset Using decision tree","authors":"Abdulrahman Abdullah Bahashwan, R. Ibrahim, M. Omar, Moch. Faqih","doi":"10.1109/IEACon55029.2022.9951749","DOIUrl":"https://doi.org/10.1109/IEACon55029.2022.9951749","url":null,"abstract":"Operating at an ultra-lean air/gas ratio, a modern dry low-emission gas turbine can achieve extremely low emissions levels and meet the strict environmental regulation on NOx emissions. On the other hand, ultra-lean combustion is highly prone to tripping caused by thermoacoustic combustion instabilities and Lean Blowout (LBO), which can result in large pressure oscillations in the combustor and decreased component durability. The prediction methods employed for the early identification of an LBO event prior to its occurrence provide an innovative approach to handling LBO. Prior research on LBO prediction focused mostly on laboratory-scale combustion utilizing flame characteristics as the only parameter, without associating the whole gas turbine, which was a poor and inadequate model of the industrial gas turbine. However, the data-driven model based on the historical datasets has shown greater accuracy and replicativity and has not thoroughly explored LBO prediction. This study investigates the LBO error using an entirely data-driven technique using a dataset collected from an actual ultra-lean dry low emission (DLE) gas turbine’s log file and applying the decision tree classifier for LBO prediction in comparison to logistic regression. The study shows that the LBO can be detected at least one minute prior to the trip. Furthermore, the decision tree classifier model for LBO prediction shows a robust performance with 99.8% accuracy and outperforms the logistic regression model.","PeriodicalId":197307,"journal":{"name":"2022 IEEE Industrial Electronics and Applications Conference (IEACon)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117065513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study on Deep Feature Extraction Approaches for Visual Tracking of Industrial Robots 面向工业机器人视觉跟踪的深度特征提取方法比较研究

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951767

T. I. Amosa, P. Sebastian, L. I. Izhar, O. Ibrahim

{"title":"A Comparative Study on Deep Feature Extraction Approaches for Visual Tracking of Industrial Robots","authors":"T. I. Amosa, P. Sebastian, L. I. Izhar, O. Ibrahim","doi":"10.1109/IEACon55029.2022.9951767","DOIUrl":"https://doi.org/10.1109/IEACon55029.2022.9951767","url":null,"abstract":"Owing to several breakthroughs in deep-learning research, visual object trackers based on deep learning algorithms have been the de facto method in the tracking community. However, the performance of this category of trackers is largely influenced by the richness and quality of the target feature representation. Thus, to extract features with high discriminative power, special attention must be paid to the feature extraction component of the tracker. Despite the significance of feature extraction paradigm in deep learning-based trackers, limited studies have been dedicated to comparatively examining the feature learning and representation capabilities of various existing deep feature extraction approaches in visual tracking. In this paper, we present a comparative study on two different deep feature extraction methods in visual tracking of industrial robots in video. This work examines and visualizes the feature representation capability of two commonly employed feature extraction methods in deep learning-based tracking; namely, Convolutional Neural Network (CNN)-based and Transformer-based feature extraction methods. To demonstrate the effectiveness of the deep learning-based tracking paradigm, this study adopts a Siamese-based tracker that is trained end-to-end offline with ILSVRC15 datasets as well as a Transformer-based tracking architecture that is also pre-trained on the ImageNet dataset. This study also contributes a dataset consisting of images of industrial robots, which was employed for evaluating the two adopted trackers. From this study, we could conclude that the feature learning and representation of the transformer-based feature extraction pipeline largely improved the overall tracking performance of the tracker.","PeriodicalId":197307,"journal":{"name":"2022 IEEE Industrial Electronics and Applications Conference (IEACon)","volume":"479 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123641895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Series Capacitor Converter with MHz Switching Frequency Based on GaN HEMT 基于GaN HEMT的MHz开关频率串联电容变换器

2022 IEEE Industrial Electronics and Applications Conference (IEACon) Pub Date : 2022-10-03 DOI: 10.1109/IEACon55029.2022.9951765

Jinfeng Zhang, Zhuxuan Tian, Zhenshuai Rong, Yueshi Guan, Yijie Wang, Dianguo Xu

引用次数: 0