| 余阳,吴银锋,冯仁剑,万江文.应用于管道气体泄漏监测的WSN层级式数据融合方法[J].高技术通讯(中文),2012,22(1):1~7 |
| 应用于管道气体泄漏监测的WSN层级式数据融合方法 |
| A hierarchical data fusion method for detection of the leak of gas pipelines based on wireless sensor network |
| 修订日期：2010-09-02 |
| DOI： |
| 中文关键词: 泄漏检测， 无线传感器网络（WSN）， 支持向量机（SVM）， 证据理论 |
| 英文关键词: leak detection, wireless sensor network (WSN), support vector machine (SVM), evidence theory |
| 基金项目:863计划（2009AA01Z201），国家自然科学基金（60974121）和北京市自然科学基金（8102025）资助项目 |
| 作者 | 单位 | | 余阳 | 北京航空航天大学仪器科学与光电工程学院北京 | | 吴银锋 | 北京航空航天大学仪器科学与光电工程学院北京 | | 冯仁剑 | 北京航空航天大学仪器科学与光电工程学院北京 | | 万江文 | 北京航空航天大学仪器科学与光电工程学院北京 |
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| 中文摘要: |
| 为提高利用无线传感器网络（WSN）监测天然气管网泄漏的准确性和可靠性，提出一种小波支持向量机（SVM）和证据理论相结合的层级式数据融合算法。该算法利用小波变换方法对原始信号进行数据级消噪处理，并提取对泄漏敏感的特征参数；建立SVM多分类器模型，以特征参数作为输入向量在普通节点处进行泄漏检测特征级融合；采用改进的证据组合规则，在Sink节点处进行决策级证据组合，得到管网状态的最终决策。实验结果表明，该方法可有效地提高泄漏源位置检测的正确率，降低检测过程中的漏检率和误警率 |
| 英文摘要: |
| To improve the accuracy and reliability of the leak monitoring of gas pipelines by using wireless sensor networks(WSN), this paper puts forward a hierarchical data fusion algorithm based on the combination of the wavelet support vector machine (SVM) method and the evidence theory. The algorithm is described below. In the signal level fusion, the noise elimination for primitive signals is conducted using the wavelet transform technology, and leak characteristic parameters are totally extracted as well. In the attribute fusion, a multi classifier model based on SVM is constructed, and characteristic parameters as input vectors are sent to the multi classifier for initial recognition. In the decision level fusion, the evidence combination is accomplished using the improved evidence combination methods at the sink node for final decision making. The experimental results show that the approach could improve the precision of the leak location detection and reduce the undetected rate as well as the false alarm rate. |
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