Recognition and Reconstruction of Conduction Leakage Signal via Power Line Based on PSO-SVM Method

Changlin ZHOU; Zhisheng QIAN; Qinmin WANG; Daojie YU; Junping CHENG

doi:10.11999/JEIT171136

Volume 40 Issue 9

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2206-2211

Changlin ZHOU, Zhisheng QIAN, Qinmin WANG, Daojie YU, Junping CHENG. Recognition and Reconstruction of Conduction Leakage Signal via Power Line Based on PSO-SVM Method[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2206-2211. doi: 10.11999/JEIT171136

Citation:

Changlin ZHOU, Zhisheng QIAN, Qinmin WANG, Daojie YU, Junping CHENG. Recognition and Reconstruction of Conduction Leakage Signal via Power Line Based on PSO-SVM Method[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2206-2211. doi: 10.11999/JEIT171136

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Recognition and Reconstruction of Conduction Leakage Signal via Power Line Based on PSO-SVM Method

doi: 10.11999/JEIT171136

PLA Information Engineering University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61271104, 61201056)

Received Date: 2017-12-04
Rev Recd Date: 2018-05-09

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

In order to identify the red signal in the conduction leakage signal of the display power line effectively, a Particle Swarm Optimization-Support Vector Mechine (PSO-SVM) algorithm based on Particle Swarm Optimization (PSO) algorithm for parameter optimization is proposed. Firstly, the conducted leakage signal is filtered, then the PSO-SVM is used to train and classify the conducted leakage signals and compared with the SVM classification. Finally, the display image is reconstructed using PSO-SVM. The result shows that the the red signal can be effectively identified, and the identification rate is significantly higher than the SVM classifier.
- Conducted leakage,
- Power line,
- Recognition,
- Particle Swarm Optimization-Support Vector Mechine (PSO-SVM),
- Reconstruction

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References(16)

References

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