Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models

The global deployment of the phasor measurement units (PMUs) enables real-time monitoring of the power system, which has stimulated considerable research into machine learning-based models for event detection and classification. However, recent studies reveal that machine learning-based methods are vulnerable to adversarial attacks, which can fool the event classifiers by adding small perturbations to the raw PMU data. To mitigate the threats posed by adversarial attacks, research on defense strategies is urgently needed. This paper proposes an effective adversarial purification method based on the diffusion model to counter adversarial attacks on the machine learning-based power system event classifier. The proposed method includes two steps: injecting noise into the PMU data; and utilizing a pre-trained neural network to eliminate the added noise while simultaneously removing perturbations introduced by the adversarial attacks. The proposed adversarial purification method significantly increases the accuracy of the event classifier under adversarial attacks while satisfying the requirements of real-time operations. In addition, the theoretical analysis reveals that the proposed diffusion model-based adversarial purification method decreases the distance between the original and compromised PMU data, which reduces the impacts of adversarial attacks. The empirical results on a large-scale real-world PMU dataset validate the effectiveness and computational efficiency of the proposed adversarial purification method

Trends and characteristics of young non-small cell lung cancer patients in the United States

BACKGROUND: Although the median age at diagnosis of non-small cell lung cancer (NSCLC) is 70 years, a subset of patients with NSCLC present at a younger age (<40 years). Little is known about the time-trends in incidence of NSCLC in the young, their characteristics and outcomes. ----- METHODS: The surveillance, epidemiology, and end results database was used to extract NSCLC cases from 1978 to 2010. Yearly incidence rates in various age groups, race, site of disease, histology, treatment patterns, and outcomes were assessed. We modeled Kaplan-Meyer survival curves stratified by age of presentation. ----- RESULTS: Young patients represented 0.6% of incident NSCLC from 1978 to 2010. The incidence of young NSCLC declined significantly during this time-period. Young NSCLCs had a higher proportion of women (51%), Asians or Pacific Islanders (14%), adenocarcinoma histology (59%) and were more likely to present with distant metastases (68%). The young had better all cause and lung cancer-specific survival than the older patients (median survival for localized, regional, and distant disease: not reached, 28, 9 vs. 46, 17, 5 months; p < 0.001 for all groups). Male sex, non-adenocarcinoma histology, and main bronchial primary were independent negative prognostic factors among the young. In contrast to the overall population, black race was a poor prognostic factor among the young. ----- CONCLUSION: The incidence of NSCLC in the young decreased from 1978 to 2010. The clinical characteristics of NSCLC in the young, including demographic distribution, treatment, and outcomes are different from those observed in the older patients

Multi-Resonant-Based Sliding Mode Control of DFIG-Based Wind System under Unbalanced and Harmonic Network Conditions

In general, the integral sliding mode control (ISMC) with an integral sliding surface would lead to tracking errors under unbalanced and harmonic grid voltage conditions. In order to eliminate tracking errors under these conditions, multi-resonant items are added to the conventional integral sliding surface in the proposed strategy, which can be called multi-resonant-based sliding mode control (MRSMC). A comparison of tracking precision on the ISMC and MRSMC is analyzed. In order to regulate the system powers directly, the errors of instantaneous active and reactive powers are selected as the state variables. Finally, the output current harmonics and a majority of the doubly-fed induction generator&rsquo;s (DFIG) electromagnetic torque pulsations can be removed under unbalanced and harmonic grid voltage conditions. Simulation and experimental results are presented to verify the correctness and effectiveness of the proposed strategy

Defogging algorithm of underground coal mine image based on adaptive dual-channel prior

When dark channel prior algorithm is used to deal with underground coal mine images, there are problems of image distortion, lack of details and dark light. In order to solve the above problems, a defogging algorithm of underground coal mine image based on adaptive dual-channel prior is proposed. Firstly, according to the physical model of atmospheric scattering and the special environment of underground coal mine, the dust and fog image degradation model in underground coal mine is established. Secondly, a dual-channel prior model is established by fusing the dark channel and the bright channel to optimize the transmittance. An adaptive weight coefficient is added to improve the precision of the transmittance image. And the gradient guided filtering is adopted to replace the traditional guided filtering to refine the transmittance image. Finally, combined with the mine environment, the atmospheric light value calculation method is improved. And the image is restored according to the dust and fog image degradation model. The experimental results show that the algorithm can effectively remove the fog phenomenon in the image, avoid the halo blur and over-enhancement phenomenon. Compared with dark channel prior algorithm, Retinex algorithm and Tarel algorithm, this algorithm greatly improves the image information entropy and average gradient. The algorithm enriches the detailed information of the restored image and shortens the running time

Research on Optimal Charging of Power Lithium-Ion Batteries in Wide Temperature Range Based on Variable Weighting Factors

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users