Data Cleaning Based on Multi-sensor Spatiotemporal Correlation

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Data Cleaning Based on Multi-sensor Spatiotemporal Correlation
	Shao, Baozhu 4; Song CH(宋纯贺)2,3; Wang ZF(王忠锋)2,3 ; Li, Zhexi 1; Yu SM(于诗矛)2,3; Zeng P(曾鹏)2,3
	2019
会议日期	August 24-25, 2019
会议地点	Nanjing, China
关键词	Data cleaning Spatiotemporal correlation Sensor networks
页码	235-243
英文摘要	Sensor-based condition monitoring systems are becoming an important part of modern industry. However, the data collected from sensor nodes are usually unreliable and inaccurate. It is very critical to clean the sensor data before using them to detect actual events occurred in the physical world. Popular data cleaning methods, such as moving average and stacked denoise autoencoder, cannot meet the requirements of accuracy, energy efficiency or computation limitation in many sensor related applications. In this paper, we propose a data cleaning method based on multi-sensor spatiotemporal correlation. Specifically, we find out and repair the abnormal data according to the correlation of sensor data in adjacent time and adjacent space. Real data based simulation shows the effectiveness of our proposed method.
产权排序	2
会议录	Machine Learning and Intelligent Communications - 4th International Conference, MLICOM 2019, Proceedings
会议录出版者	Springer
会议录出版地	Berlin
语种	英语
ISSN号	1867-8211
ISBN号	978-3-030-32387-5
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/26023]
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	Song CH(宋纯贺)
作者单位	1.Shenyang Power Supply Company, State Grid Liaoning Electric Power Co., Ltd., 110000, Shenyang, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Liaoning Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., 110000, Shenyang, China
推荐引用方式 GB/T 7714	Shao, Baozhu,Song CH,Wang ZF,et al. Data Cleaning Based on Multi-sensor Spatiotemporal Correlation[C]. 见:. Nanjing, China. August 24-25, 2019.