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Partial discharge (PD) detection and diagnosis based on the ultra-high frequency (UHF) signals is one of the most widely adopted methods to evaluate the internal insulation status of high voltage equipment. Benefit from the rapid development of computing hardware and data processing algorithms, the intelligent PD fault diagnosis method based on the UHF data has made considerable progress in the past two decades. This two-part paper aims to give a comprehensive review about the application of signal processing and machine learning technologies in UHF PD detection and diagnosis. These technologies are divided into three categories according to their respective purpose, which are the preprocessing technology, source localization technology and pattern recognition technology. As the first one of the two-part review, we focus on the preprocessing and localization approaches in this paper. Specifically, for the preprocessing topic, the methods for signal denoising, multi-source separation, and pulse segmentation are included. While for the localization topic, the time difference of arrival (TDOA) method, direction of arrival (DOA) method, received signal strength indicator (RSSI) method, and other latest methods are reviewed. For each topic, the basic ideas, recent research progresses, advantages and limitations are discussed in detail. Before the conclusion, we also make a discussion about the application effects of the above technologies and prospect some future directions accordingly. In the second paper, the pattern recognition problems based on the UHF PD data will be concentrated, especially the application of deep learning algorithms.

Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis. During recent years, convolutional neural networks (CNNs) have achieved significant success and are widely applied in RSI scene classification. However, crafted images that serve as adversarial examples can potentially fool CNNs with high confidence and are hard for human eyes to interpret. For the increasing security and robust requirements of RSI scene classification, the adversarial example problem poses a serious problem for the classification results derived from systems using CNN models, which has not been fully recognized by previous research. In this study, to explore the properties of adversarial examples of RSI scene classification, we create different scenarios by testing two major attack algorithms (i.e., the fast gradient sign method (FGSM) and basic iterative method (BIM)) trained on different RSI benchmark datasets to fool CNNs (i.e., InceptionV1, ResNet and a simple CNN). In the experiment, our results show that CNNs of RSI scene classification are also vulnerable to adversarial examples, and some of them have a fooling rate of over 80%. These adversarial examples are affected by the architecture of CNNs and the type of RSI dataset. InceptionV1 has a fooling rate of less than 5%, which is lower than the others. Adversarial examples generated on the UCM dataset are easier than other datasets. Importantly, we also find that the classes of adversarial examples have an attack selectivity property. Misclassifications of adversarial examples of RSIs are related to the similarity of the original classes in the CNN feature space. Attack selectivity reveals potential classes of adversarial examples and provides insights into the design of defensive algorithms in future research.

As one of the most effective detection methods for partial discharge (PD), analysis of the radiated electromagnetic (EM) signals detected by ultra high frequency (UHF) sensor has gained broad attentions. However, the main bottlenecks for this method are probably the massive storage requirements and various noise interferences. Therefore, this paper is focused on investigating a new pulse segmentation technique, which is capable of separating PD pulses from noisy measured data to save the storage space and improve the signal noise ratio (SNR). First, a designed average multi-scale morphological dilate-erode filter (AMMDEF) is developed to obtain the smooth envelope shape of PD pulses. Then, instantaneous phase (IP) of the envelope shape is calculated by the Hilbert Transform (HT) and further smoothed using AMMDEF. Finally, a robust IP based boundary identification criterion (IPBIC) is proposed to accurately extract PD pulses from raw data. Various experiments have been carried out and results show that this method could achieve an average detection error rate (DER) at 0.1375 and an average absolute precision error (APE) at 23.1 ns respectively, even the SNR of signal is as low as 0 dB. Superiority of the developed method over traditional pulse segmentation techniques is also demonstrated.

1-hydroxypyrene (1-OHP) is a kind of polycyclic aromatic hydrocarbons (PAHs) metabolites and has been used widely as a biomarker for evaluating human exposure to PAHs. In this paper, an electrochemical sensor was fabricated based on graphene-MN202 modified glassy carbon electrodes and used to preconcentrate and detect 1-OHP. When 1-OHP was preconcentrated on modified electrodes, the response signals and adsorption stability were enhanced. The electrochemical characteristics of 1-OHP on the modified electrode were investigated by cyclic voltammetry and differential pulse voltammetry. Experimental parameters were optimized, such as the adsorption potential, adsorption time, scan rate, and the pH values of buffer solution. Under the optimized conditions, the peak current was proportional to 1-OHP concentration in a wide range from 0.005 to 12.0 μm ol/L, and the detection limit was 1.72 nmol/L (S/N = 3). Moreover, the fabricated electrode also exhibited good reproducibility and stability, and can be employed to determinate 1-OHP, which is in human urine in situ successfully.