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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hu Yan Lixin Li Fangchun Di Jin Hua Qiqiang Sun |
| Copyright Year | 2011 |
| Abstract | Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time evolution characters into account. In addition, to make the classifier be more self-adaptive, an incremental learning module is introduced. The proposed classifier has been successfully applied to oil and gas pipeline intrusion detection systems. Practical results show that it can distinguish nuisance events from intrusion events at a high rate of 94.86% and for seven kinds of intrusions, the recognition rate is 95.29%, fully satisfies the real application requirement. |
| Starting Page | 158 |
| Ending Page | 161 |
| File Size | 250023 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457710858 |
| DOI | 10.1109/ISCID.2011.141 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-10-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vibrations Support vector machines Optical fiber sensors Perimeter Intrusion detection Intrusion detection Artificial neural networks Feature extraction Artificial neural network Support vector machine Reliability Smart Vibration signal |
| Content Type | Text |
| Resource Type | Article |
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