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Using Multi-sensor Data Fusion in Outlier Detection of Building Strain Monitoring Data

机译:在建筑物应变监测数据的异常检测中使用多传感器数据融合

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摘要

More and more sensor-based information systems have been put into use, and the data from the sensors are growing rapidly. The emergence of multi-sensor data fusion provides more mining methods and can solve the problem from many different viewpoints. This paper presents an improved outlier detection method for the building strain monitoring data by using multi-sensor data fusion. According to the real-time temperature data, the strain data at the same period can be segmented. In each segment the traditional outlier detection is used. Comparing the segment with the non-segment method, it is proved that the method using multi-sensor data fusion improves the efficiency of outlier detection.
机译:越来越多的基于传感器的信息系统已投入使用,并且来自传感器的数据正在迅速增长。多传感器数据融合的出现提供了更多的挖掘方法,并可以从许多不同的角度解决问题。本文提出了一种改进的利用多传感器数据融合的建筑应变监测数据离群值检测方法。根据实时温度数据,可以对同一时期的应变数据进行分段。在每个片段中,都使用传统的离群值检测。通过将分段与非分段方法进行比较,证明了使用多传感器数据融合的方法提高了离群值检测的效率。

著录项

  • 来源
    《Journal of information and computational science》 |2015年第11期|4145-4152|共8页
  • 作者单位

    International WIC Institute, Beijing University of Technology, Beijing 100124, China;

    International WIC Institute, Beijing University of Technology, Beijing 100124, China,Knowledge Representation and Reasoning Group, Vrije University Amsterdam Amsterdam 1081 HV, The Netherlands;

    International WIC Institute, Beijing University of Technology, Beijing 100124, China,Department of Life Science and Informatics, Maebashi Institute of Technology Maebashi 371-0816, Japan;

    International WIC Institute, Beijing University of Technology, Beijing 100124, China;

    National Center for Quality Supervision and Test of Building Engineering, China Academy of Building Research, Beijing 100013, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data Fusion; Outlier Detection; Multi-sensor; Monitoring System;

    机译:数据融合;离群值检测;多传感器监视系统;

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