Recognition and Reconstruction of Conduction Leakage Signal via Power Line Based on PSO-SVM Method
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摘要: 针对显示器电源线传导泄漏信号中红信号识别的难题，该文提出基于粒子群(PSO)算法优化支持向量机(SVM)的识别方法。首先对传导泄漏信号进行滤波预处理并分段，然后利用粒子群-支持向量机(PSO-SVM)对传导泄漏信号进行训练、分类并与SVM分类性能进行对比，最后应用PSO-SVM实现了显示图像的还原。结果表明此算法可以准确实现电源线传导泄漏信号中红信号的识别，且识别率明显高于SVM分类器。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.
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SONG T L, JEONG Y R, and YOOK J G. Modeling of leaked digital video signal and information recovery rate as a function of SNR[J]. IEEE Transactions on Electromagnetic Compatibility, 2015, 57(2): 164–172 doi: 10.1109/TEMC.2014.2372039 HAYASHI Y I, HOMMA N, TORIUMI Y, et al. Remote visualization of screen images using a pseudo-antenna that blends into the mobile environment[J]. IEEE Transactions on Electromagnetic Compatibility, 2017, 59(1): 24–33 doi: 10.1109/TEMC.2016.2594237 YANG Shanjing, LIAO Xiaoyong, HU Jianlin, et al. Recognition of information leakage of computer via conducted emanations on the power line[C]. IEEE International Conference on Big Data Security on Cloud, Beijing, 2017: 139–144. doi: 10.1109/BigDataSecurity.2017.51. ZHANG Nan, LÜ Yinghua, CUI Qiang, et al. Investigation of unintentional video emanations from a VGA connector in the desktop computers[J]. IEEE Transactions on Electromagnetic Compatibility, 2017, 59(6): 1826–1834 doi: 10.1109/TEMC.2017.2699479 徐艳云, 黄伟庆, 范伟, 等. 基于电源线的电磁信息泄漏建模与实验分析[J]. 中国科学: 信息科学, 2015, 45(10): 1341–1354 doi: 10.1360/N112014-00324XU Yanyun, HUANG Weiqing, FAN Wei, et al. Modeling and experimental research on electromagnetic information leakage from power lines[J]. SCIENTIA SINICA(Informationis) , 2015, 45(10): 1341–1354 doi: 10.1360/N112014-00324 HUANG Weiqing, YANG Shanjing, and HU Jianlin. Analysis on information leakage of computer displays via conducted emission on power line[C]. IEEE International Symposium on Computer, Consumer and Control, Shanghai, 2016: 493–497. doi: 10.1109/IS3C.2016.129. HUANG Weiqing, ZHAO Jianlin, LÜ Zhiqiang, et a. A method to detect and locate the character information submerged in electromagnetic leakage signal[C]. IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chengdu, 2016: 879–882. doi: 10.1109/ITNEC.2016.7560487. 孟庆昕, 杨士莪, 于盛齐. 基于波形结构特征和支持向量机的水面目标识别[J]. 电子与信息学报, 2015, 37(9): 2117–2123 doi: 10.11999/JEIT150139MENG Qingxin, YANG Shie, and YU Shengqi. Recognition of marine acoustic target signals based on wave structure and support vector machine[J]. Journal of Electronics&Information Technology, 2015, 37(9): 2117–2123 doi: 10.11999/JEIT150139 刘国栋, 许静. 基于SVM方法的神经网络呼吸音识别算法[J]. 通信学报, 2014, 35(10): 218–222 doi: 10.3969/j.issn.1000-436x.2014.10.025LIU Guodong and XU Jing. Neural network recognition algorithm of breath sounds based on SVM[J]. Journal on Communications, 2014, 35(10): 218–222 doi: 10.3969/j.issn.1000-436x.2014.10.025 高发荣, 王佳佳, 席旭刚, 等. 基于粒子群优化-支持向量机方法的下肢肌电信号步态识别[J]. 电子与信息学报, 2015, 37(5): 1154–1159 doi: 10.11999/JEIT141083GAO Farong, WANG Jiajia, XI Xugang, et al. Gait recognition for lower extremity electromyographic signals based on PSO-SVM method[J]. Journal of Electronics&Information Technology, 2015, 37(5): 1154–1159 doi: 10.11999/JEIT141083 尚文利, 张盛山, 万明, 等. 基于PSO-SVM的Modbus TCP通讯的异常检测方法[J]. 电子学报, 2014, 42(11): 2314–2320 doi: 10.3969/j.issn.0372-2112.2014.11.029SHANG Wenli, ZHANG Shengshan, WAN Ming, et al. Modbus/TCP communication anomaly detection algorithm based on PSO-SVM[J]. Acta Electronica Sinica, 2014, 42(11): 2314–2320 doi: 10.3969/j.issn.0372-2112.2014.11.029 LEI Li, CHANG Wenbin, ZHOU Shenghan, et al. An identification and prediction model of wear-out fault based on oil monitoring data using PSO-SVM method[C]. Reliability and Maintainability Symposium, Exeter, UK, 2017: 1–6. doi: 10.1109/RAM.2017.7889670. 陈世杰, 连可, 王厚军. 遗传算法优化的SVM模拟电路故障诊断方法[J]. 电子科技大学学报, 2009, 38(4): 553–558 doi: 10.3969/j.issn.1001-0548.2009.04.019CHEN Shijie, LIAN Ke, and WANG Houjun. Method for analog circuit fault diagnosis based on GA Optimized SVM[J]. Journal of University of Electronic Science and Technology of China, 2009, 38(4): 553–558 doi: 10.3969/j.issn.1001-0548.2009.04.019 WANG Yingmin, CUI Tao, ZHANG Fujun, et al. Fault diagnosis of diesel engine lubrication system based on PSO-SVM and centroid location algorithm[C]. International Conference on Control, Automation and Information Sciences, Ansan-si, Korea, 2016: 221–226. doi: 10.1109/ICCAIS.2016.7822464. ZHU Dali, HE Peng, and ZHANG Jingqin. Research of key problem of receiving and recovery of signal based on wide band receiver[C]. International Conference on Internet Technology and Applications, Wuhan, 2010: 1–8. doi: 10.1109/ITAPP.2010.5566616. TANG Yizhou and ZHOU Jiawen. The performance of PSO-SVM in inflation forecasting[C]. International Conference on Service Systems and Service Management, Guangzhou, 2015: 1–4. doi: 10.1109/ICSSSM.2015.7170251. -
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