论文标题
通过石灰解释意外辐射的排放分类
Explanation of Unintended Radiated Emission Classification via LIME
论文作者
论文摘要
在使用电子设备期间，出现了意外辐射的排放。识别和减轻这些排放的影响是现代动力工程和相关控制系统的关键要素。电气系统的信号处理可以识别这些排放的来源。一个称为火焰MOE的数据集包括消费电子产品的捕获的意外辐射排放。该数据集进行了分析，以构建用于设备识别的下一代方法。为此，构建了基于将RESNET-18图像分类体系结构应用于短时间的短段变换电压特征的短段变换的神经网络。使用此分类器，确定了18个设备类别和背景类的精度。通过将石灰应用于此分类器并在同一设备的许多分类中汇总结果，可以确定分类器用于做出决策的频段。使用从同一父数据分布上在非常相似的数据集上训练的分类器的集合，可以恢复对设备输出的强大功能集合有用的功能。石灰应用所提供的其他理解增强了URE分析网络的训练性，可信度和可转移性。
Unintended radiated emissions arise during the use of electronic devices. Identifying and mitigating the effects of these emissions is a key element of modern power engineering and associated control systems. Signal processing of the electrical system can identify the sources of these emissions. A dataset known as Flaming Moes includes captured unintended radiated emissions from consumer electronics. This dataset was analyzed to construct next-generation methods for device identification. To this end, a neural network based on applying the ResNet-18 image classification architecture to the short time Fourier transforms of short segments of voltage signatures was constructed. Using this classifier, the 18 device classes and background class were identified with close to 100 percent accuracy. By applying LIME to this classifier and aggregating the results over many classifications for the same device, it was possible to determine the frequency bands used by the classifier to make decisions. Using ensembles of classifiers trained on very similar datasets from the same parent data distribution, it was possible to recover robust sets of features of device output useful for identification. The additional understanding provided by the application of LIME enhances the trainability, trustability, and transferability of URE analysis networks.
