Efficient and Accurate Classification Enabled by a Lightweight CNN
文献类型：会议论文
| 作者 | Miao, Weiwei1; Zeng, Zeng1; Wang, Chuanjun1; Chen, Yueqin5; Song CH(宋纯贺)2,3,4
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| 出版日期 | 2020 |
| 会议日期 | MAY 15-18, 2020 |
| 会议地点 | ELECTR NETWORK |
| 关键词 | edge device lightweight convolutional neural network(L-CNN) fault diagnosis |
| 页码 | 989-992 |
| 英文摘要 | With the rapid development of cloud computing technology, many applications such as image recognition and fault diagnosis are applied in the power grid, and the data is collected and uploaded to the cloud for processing. However, the amount of data and the amount of calculation required by the model are too large, so that the cloud computing model cannot solve the current problem well. Edge computing refers to processing data at the edge of the network, which can reduce request response time, improve battery life, and reduce network bandwidth while ensuring data security and privacy. However, existing edge devices are difficult to meet complex models' demands. In order to solve the above problems, this paper proposes a lightweight CNN model that can be operated on the edge device. Experiments prove that the model is a reliable method for fault diagnosis at the edge. |
| 产权排序 | 3 |
| 会议录 | 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS 2020)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-6136-5 |
| WOS记录号 | WOS:000610526500192 |
| 源URL | [http://ir.sia.cn/handle/173321/28323]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Zeng, Zeng |
| 作者单位 | 1.Grid Jiangsu Electric Power CO., LTD. Information & Communication Branch Nanjing, China 2.Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.China Information Consulting & Designing Institute Co., Ltd |
| 推荐引用方式 GB/T 7714 | Miao, Weiwei,Zeng, Zeng,Wang, Chuanjun,et al. Efficient and Accurate Classification Enabled by a Lightweight CNN[C]. 见:. ELECTR NETWORK. MAY 15-18, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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