2022 IEEE 5th International Conference on Electronics Technology (ICET)最新文献_第6页

A Novel Superpixel Cosegmentation for Change Detection in Remote Sensing Imagery 一种用于遥感图像变化检测的超像素共分割方法

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824542

Weiyong Tong, Yu-xiang Zhang, Hu Song, Qingqing Song

引用次数: 0

Edge-Computing Oriented Robust Routing Scheme in PLC-IoT Networks 面向边缘计算的PLC-IoT网络鲁棒路由方案

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9825145

Libin Zheng, Wenbin Chen, Yang Tian

引用次数: 0

A 13-Bit High Speed Two-Step Single Slope ADC Design Method for Hundreds of Mpxiel CMOS Image Sensors 一种用于数百像素CMOS图像传感器的13位高速两步单斜率ADC设计方法

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9825414

Ruiming Xu, Zhongjie Guo, Xinqi Cheng, Changxu Su, Chen Li, Yangle Wang

引用次数: 1

High-Efficiency Ink-Jet Printed OLED with Printable Electron Transport Layer 具有可打印电子传输层的高效喷墨印刷OLED

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824053

Xuebin Chen, Ting Shi, Shengdong Zhang, Ming Liu

引用次数: 1

The Target Detection Method of Pulse-Doppler Radar Based on Graph Attention Networks 基于图注意网络的脉冲多普勒雷达目标检测方法

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824889

Yang Yang, Yang Zhao, Haibo Wang, Dong Cao, Linyan Liu

引用次数: 0

A New Multistage Filtering Method for Real-Time Tide Observation Method 实时潮汐观测方法的多级滤波新方法

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824841

Hai Guo, Yanyan Wang, Yifei Zhang, Peng Wang, G. Wang

引用次数: 0

Optimized Allocation of Protection Centers in Communication Network of Wide Area Protection 广域保护通信网中保护中心的优化配置

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824209

Tingting Sun, Jiejie Huang

引用次数: 0

Clock Optimization Techniques 时钟优化技术

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9823968

Ang Boon Chong, Tan Say Hong, Koh Jid Ian, Mohamad Halim Izzat, Loo Gin Hao, Chin Kevin Che Chau

引用次数: 2

Electromotive Force Based State of Charge Estimation for Primary Battery Powered Devices 基于电动势的原电池供电器件电荷状态估计

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824740

H. Dai, Wei Liu, Jing Mao, Jian Xie, Shunren Hu

{"title":"Electromotive Force Based State of Charge Estimation for Primary Battery Powered Devices","authors":"H. Dai, Wei Liu, Jing Mao, Jian Xie, Shunren Hu","doi":"10.1109/icet55676.2022.9824740","DOIUrl":"https://doi.org/10.1109/icet55676.2022.9824740","url":null,"abstract":"Due to its low cost, primary battery such as alkaline battery and carbon battery is still one of the main options for powering low power devices, such as wireless sensor nodes. In order to prolong the survival time, it is necessary to accurately estimate the State of Charge (SOC) of the battery and then dynamically adjust the working modes of device. Among the existing SOC estimation methods, Electromotive Force (EMF) is generally recognized as an accurate indicator. Taking popular alkaline and carbon batteries as examples, this paper studies the relationship of EMF and SOC through intermittent discharge experiments with both constant current and constant resistance loads. Experimental results show that the change patterns of SOC with EMF are very similar for batteries with the same chemical material. Meanwhile, the discharge mode (constant current or constant resistance) and load have very little effect on the relationship of EMF and SOC. Then, the SOC estimation models based on EMF are established using different fitting methods. Verification results show that, for the target primary batteries, the piecewise linear fitting method is the most accurate, and its estimation error is no more than 1.4%.","PeriodicalId":166358,"journal":{"name":"2022 IEEE 5th International Conference on Electronics Technology (ICET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132066697","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

Dual Mix-up Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction 机器剩余使用寿命预测的双混合对抗域自适应

2022 IEEE 5th International Conference on Electronics Technology (ICET) Pub Date : 2022-05-13 DOI: 10.1109/icet55676.2022.9824464

Yanjun Dong, Chunhua Zhou, Zhou Wu, Mianzhi Cheng

{"title":"Dual Mix-up Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction","authors":"Yanjun Dong, Chunhua Zhou, Zhou Wu, Mianzhi Cheng","doi":"10.1109/icet55676.2022.9824464","DOIUrl":"https://doi.org/10.1109/icet55676.2022.9824464","url":null,"abstract":"Remaining useful life (RUL) prediction is one of the core issues in the equipment maintenance process. It aims to accurately forecast machines’ run-to-failure life span using previous and current state data. As various data-driven models are proving to be effective, because RUL labels for machines in particular conditions are difficult to obtain, domain adaptation approaches begins to be explored in the RUL prediction issue. We propose a novel Dual Mix-up Adversarial Domain Adaptation (DMADA) approach to further improve the RUL forecasting accuracy, building on existing RUL domain adaptation studies. In DMADA, both time-series mix-up and domain mix-up regularization are conducted. Virtual samples generated by linear interpolation lead to enriched and more continuous sample space. The linear interpolation encourages consistency of prediction in-between samples and allows the model to explore the feature space more thoroughly. At the same time, the domain mixup conserves the invariance of learned features. The two mixup regularizations combined promote both the transferability and the discriminability of extracted features, which is essential to satisfactory unsupervised domain adaptation performance. Thorough experiments on the C-MPASS dataset are conducted and satisfactory results prove the proposed approach effective.","PeriodicalId":166358,"journal":{"name":"2022 IEEE 5th International Conference on Electronics Technology (ICET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116291156","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