# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 1 Introduction  
Electronic devices have always produced emissions, many of which are unintended. These emissions result from the finite ability to shield and filter the components that comprise the devices. The emissions from these sources are \"processed\" in a variety of intended and unintended ways before leaving the device, which produces identifying waveforms (herein referred to as _features_) that can be measured by sensors over-the-air and over conductors that are connected to the devices. The underlying principles dictating this behavior are described in detail by [PERSON] (2018). The propagation path for emissions in power systems is discussed in detail by [PERSON] et al. [PERSON] et al. (2018). Each device, even of the same type, has a slightly different signature due to imperfections in the components and manufacturing processes. Some of these \"processing elements\" are linear and some are nonlinear in nature. [PERSON] et al. [PERSON] et al. (2018) presented a successful attempt at performing device classification via the use of dimensionally aligned signal projection and linear discriminant analysis. The data used in [PERSON]'s work and in this paper were acquired via nonintrusive load monitoring (NILM) methods. [PERSON] (2018) provides a fundamental description of NILM. [PERSON] et al. [PERSON] et al. (2018) showed improvements with a subset of the original data set employing additional novel preprocessing techniques and a convolutional neural network classification approach. In this work, the authors show the effect of preprocessing techniques on a deep learning network using a mixture of the same devices as well as other devices within the data set, as described by [PERSON] et al. [PERSON] et al. (2018), with data augmentation via increased levels of additive white Gaussian noise (AWGN). The authors used a deep neural network (ResNet architecture) for classification and performed experimentation with methodical variations of preprocessing and data degradation to better understand the features of importance to the ResNet and limitations of it in poor features-to-noise ratio (FNR) scenarios for this application. Although the ResNet has performed well in a variety of scenarios, it was chosen here as a representative of a deep network with which to evaluate this paradigm of hypothesis generation regarding the feature selection of the network and hypothesized view by the network regarding the characteristics of what the network views as features and noise. It is anticipated that this technique could be applied with any deep network and this method could be used to help guide and/or supplement further explainability efforts. There is no expectation that the ResNet network is optimal in this particular scenario. Other attempts have been made to use deep architectures, as in research from [PERSON] and [PERSON] (2018). However, that effort focused on a different architecture without any attempt at preprocessing. [PERSON] and [PERSON] presented raw time-domain samples to a long short-term memory architecture. Successful attempts have been made to convert the one-dimensional time series unintended conducted emissions (UCE) data into images, such as in research from [PERSON] et al. [PERSON] et al. (2018) for image recognition techniques to be applied. In this work, the authors analyzed the performance differentials of methodical variations of preprocessing techniques while their data set transitioned from good to poor via the addition of AWGN prior to the preprocessing and deep learning neural network device classification based on the UCE data. The authors demonstrate the value of preprocessing in the classification process, the trade-offs involved in the preprocessing choices, and a novel method for using preprocessing to assist in the explainability of deep networks used in the classification of time-series UCE data obtained via NILM. This paper is divided into three main sections.