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Vulnerability Assessment of Equipment Excited by Disturbances Based on Support Vector Machine and Gaussian Process Regression
IEEE Transactions on Electromagnetic Compatibility ( IF 2.1 ) Pub Date : 2020-11-24 , DOI: 10.1109/temc.2020.3037110
Yu-hao Chen , Yan-zhao Xie , Xiao-yu Ge , Yi Zhou , Dao-zhong Zhang , Yan Jing , Jian Wu , Ai-ci Qiu

In this article, a nonparametric vulnerability assessment method is proposed for the equipment excited by transient electromagnetic disturbances. By carrying out the proposed method, the probability distributions of multistate failure thresholds could be obtained for the cases of lacking prior knowledge, such as the effect mechanism, related distribution characteristics and training data. In fact, the proposed method combines support vector machine (SVM) and Gaussian process regression (GPR). SVM may extract the classification hyperplane from a small number of test samples, and obtain the continuous classification indicators subsequently. Furthermore, the regression results of classification indicators based on GPR can provide both mean values and their confidence intervals. To deal with the multilevel effect assessment, the “one against the rest” strategy is applied and probabilities of each level can be assessed simultaneously. Finally, a case study of an electronic system is carried out to illustrate the applicability and effectiveness of the proposed method.

中文翻译:

基于支持向量机和高斯过程回归的扰动设备脆弱性评估

本文针对瞬变电磁干扰激发的设备提出了一种非参数脆弱性评估方法。通过实施所提出的方法,可以得到影响机制,相关分布特征和训练数据等先验知识不足的情况下多状态失效阈值的概率分布。实际上,所提出的方法结合了支持向量机(SVM)和高斯过程回归(GPR)。SVM可以从少量测试样本中提取分类超平面,然后获得连续的分类指标。此外,基于GPR的分类指标的回归结果可以提供平均值及其置信区间。为了处理多层次的效果评估,应用“一个反对其他”策略,可以同时评估每个级别的概率。最后,以电子系统为例,说明了该方法的适用性和有效性。
更新日期:2020-11-24
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