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Continuous Feature Representation of Fault Location Based on Electrical Distance for Generalizable Data-Driven Dynamic Security Assessment

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IEEE Transactions on Power systems 2025年

作者： Qi, Hang Li, Changgang Liu, Yutian Sun, Runjia Yan, Jiongcheng Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Jinan250061 China

Pre-fault dynamic security assessment (DSA) of power systems needs to consider different fault locations. Existing data-driven DSA methods lack generalizable fault location features, failing to accurately assess unlearned fault locations, i.e., fault locations not in the training set. To address this issue, this paper proposes a continuous feature representation method of fault location based on electrical distance, aiming to construct a pre-fault DSA model generalizable to unlearned fault locations. Firstly, fault location is represented continuously as an electrical coordinate with the electrical distance to preselected reference nodes. Then, the representation ability of electrical coordinates to fault locations is optimized by reference node selection and metric learning. Finally, with electrical coordinates and steady-state features as input, a hybrid DSA model composed of convolutional and radial basis function networks is constructed to assess system angular and voltage dynamics with different fault locations. The generalization performance of the proposed DSA method on unlearned fault locations is verified on New England 39-bus system and a provincial system of China. © 1969-2012 IEEE.

关键词： data-driven Fault location feature representation power system pre-fault dynamic security assessment

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Optimal Scheduling Considering Multiple Reserves Based on Active Power Curtailment Operation of Wind Power

Optimal Scheduling Considering Multiple Reserves Based on Ac...

引用

Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Zihan Wang Mingqiang Wang Chang Xu Jialiang Wang Yizhe Zhang Caishuai Liang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Under the current general trend of the power industry's transformation towards clean energy, wind power generation, as an important form of renewable energy utilization, is steadily increasing its proportion in the power system. However, the inherent volatility and uncertainty of wind power bring numerous challenges to the stable operation of the power system. Firstly, based on the traditional unit commitment model, this paper comprehensively considers the capacity reserve and ramping reserve of the power system, and constructs a unit commitment model that considers the supply and demand constraints of both types of reserves simultaneously. Then, on the basis of this model, an in-depth and comprehensive discussion is carried out, focusing on the key operation strategy of the active power curtailment operation of wind power. This not only helps to improve the power system's ability to accommodate wind power, but also enables a better response to the challenges of volatility and uncertainty brought by wind power to the power system, thus ensuring the safe, stable and economic operation of the power system. Finally, the model is transformed into a mixed-integer linear programming model that can be directly called by the commercial solver CPLEX, and the effectiveness of the model is verified by a modified IEEE 26-node distribution network.

关键词： Uncertainty Fluctuations Costs Supply and demand Power supplies Wind power generation Power system stability Stability analysis Power system reliability Wind turbines

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Integrated Dispatching Model for Power system Incorporating Multiple Modules

Integrated Dispatching Model for Power System Incorporating ...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Chang Xu Mingqiang Wang Zihan Wang Yizhe Zhang Jialiang Wang Fengbin Sun Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

The widespread integration of renewable energy presents significant challenges for power system scheduling. Traditional power system scheduling is mainly based on the concept of “coordinating on a multi-time-level and gradually refining at each level,” using a sequential scheduling framework. However, with the growing integration of renewable energy, the uncertainty in power systems has significantly increased, leading to severe issues such as conflicts in the coordination between different modules. This paper first introduces the scheduling framework of the traditional sequential scheduling model. Then, addressing the problems of module coordination and insufficient flexibility, an integrated multi-time-scale scheduling model is proposed. This model combines day-ahead and intraday scheduling models, optimizing decision variables including unit start/shut states, operation points, and reserves simultaneously to maximize the collaborative potential between different scheduling modules. After linearization, the proposed model is converted into a mixed-integer linear programming (MILP) model, which can be solved directly using commercial optimization solvers. Finally, case analyses demonstrate the effectiveness and superiority of the proposed integrated model compared to the traditional sequential scheduling model.

关键词： Renewable energy sources Analytical models Uncertainty Fluctuations Processor scheduling Refining Regulation Power systems Power system reliability Load modeling

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Robust Consensus Based Secondary control for Microgrids Considering Communication Delay and Noises: A Kalman Filter Based Approach

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IEEE Transactions on Smart Grid 2025年

作者： Zhao, Qi Zhang, Wen Zhang, Tingting Chen, Jian Ye, Hua Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China Shandong University Shandong Research Institute of Industrial Technology Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China

To achieve the frequency and voltage regulation as well as active and reactive power sharing, the consensus-based secondary controls of microgrids rely on communication network to exchange signals between neighbor inverters, which may be affected by the communication delay and noises. Unlike the existing methods that focus solely on communication delay, this paper proposes a Kalman filter-based robust consensus control to address the communication delay and noises comprehensively. Based on the discrete differential equation of the signal transmitted via communication network, the multi-step prediction model of the delayed signal is firstly built by considering the prediction errors. The state space model is then constructed by taking the predicted signals at each step as the state variables while the delayed and noise-involved signal and its differential value are seen as the measurements. A Kalman filter is further designed to comprehensively handle the communication delay, noises and prediction errors to estimate the true signal generated from neighbor inverters. With the estimated signals of all neighbor inverters, the consensus control is performed to coordinate the grid-following and grid-forming inverters within the microgrid. The effectiveness of the proposed method is demonstrated with simulations and experiments performed on the modified IEEE-33 bus-based microgrid. © 2010-2012 IEEE.

关键词： Kalman filters

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Polar-segmented vector optical field and its anomalous focal properties

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Optics Express 2025年第10期33卷 21030-21039页

作者： Gao, Xu-Zhen Song, Xin-Yu Chen, Jing-Rui Wang, Dan Xu, Shi-Tong Niu, Mingsheng Cao, Hongzhong Ma, Rende Pan, Yue School of Physics and Physical Engineering Qufu Normal University Qufu273165 China Shandong Provincial Key Laboratory of Laser Polarization and Information Technology Qufu Normal University Qufu273165 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronics Taiyuan University of Technology Shanxi Taiyuan030024 China

In recent years, study of spatially structured light has become an attractive and promising area, providing more ways to explore unusual and anomalous effects in optics. Here, we propose a kind of polar-segmented vector optical field (VOF), and demonstrate anomalous focal properties. Due to the appearance of the longitudinal component in tight focusing processes, the transverse energy flow and transverse spin angular momentum (SAM) are achieved, which can be applied in areas such as manipulating particles, as the transverse energy can transport the particles and the SAM can spin the particles in more degrees. Overall, the proposed polar-segmented VOF and the corresponding anomalous focal properties are crucial for advancing theories and technologies in optics, underscoring its importance in both fundamental research and practical applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Focusing

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