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Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for Electricity Theft Detection With Imbalanced and Missing Data

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IEEE TRANSACTIONS ON SMART GRID 2025年第3期16卷 2439-2450页

作者： Yang, Honggang Lian, Cheng Xu, Bingrong Ding, Ruijin Zhao, Pengbo Zeng, Zhigang Wuhan Univ Technol Sch Automat Wuhan 430074 Peoples R China Chinese Acad Sci Inst Hydrobiol State Key Lab Breeding Biotechnol & Sustainable Aq Wuhan 430072 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Mini Wuhan 430074 Peoples R China

The widespread adoption of advanced metering infrastructure has provided abundant data, enabling the integration of deep learning techniques into smart grids. However, it has also led to more sophisticated and concealed methods of electricity theft. Due to the challenges posed by data imbalance and missing values caused by device malfunctions and communication issues, existing deep learning models often perform poorly. To address these issues, this paper proposes a multi-step training framework named DING, which incorporates diffusion generation, self-supervised pre-training, normalized condition imputation, and generation-balanced fine-tuning. First, sufficient balanced smart meter data is generated using a diffusion model. Second, a pre-trained encoder is trained on the generated data, extracting unbiased low-dimensional features that can be used for downstream classification tasks and as conditions to guide the training of the imputation model. Next, an imputation model is trained based on a diffusion state-space equation. Finally, fine-tuning is performed on the balanced data. Experiments on a real dataset from the State Grid Corporation of China demonstrate that the proposed method outperforms previous models for both electricity theft detection and imputation tasks.

关键词： Imputation Electricity Time series analysis Data models Smart meters Feature extraction Training Deep learning Diffusion models Mathematical models Diffusion model electricity theft detection imbalanced data time series imputation

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SMA-PDPPO: Safe Multiagent Primal-Dual Deep Reinforcement Learning for Industrial Parks Energy Trading

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第3期21卷 2640-2649页

作者： Lu, Renzhi Wu, Ning Yang, Tao Chen, Ying Sun, Mingyang Wang, Dong Peng, Xin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Tsinghua Univ Elect Engn Beijing 100084 Peoples R China Peking Univ Coll Engn Dept Ind Engn & Management Beijing 100091 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Energy trading in industrial parks has great potential for reducing carbon emissions and lowering energy bills. This article proposes a safe multiagent deep reinforcement learning algorithm for optimizing the energy trading strategy in industrial parks to achieve less reliance on the main grid and save energy costs. Specifically, an industrial park that contains multiple industrial users with both thermal and electrical load requirements is considered, in which the different users can trade energy with each other and with the main grid based on their own strategies. Unlike the existing studies, the time-phased energy trading problem is transformed into a constrained partially observable Markov game, which models the industrial users and objectives of the buyers and sellers. Finally, a novel multiagent primal-dual proximal policy optimization algorithm that guarantees safety is developed to achieve the optimal trading strategies between the main grid and multiple users. Numerical simulations with real-world data demonstrate that the proposed algorithm allows higher total revenue for sellers and lower total costs for buyers in the park, limits each user's bid or offer to a relatively safe range, and increases the amount of electricity traded locally, while reducing trading with the grid.

关键词： Deep reinforcement learning (DRL) electricity market energy management energy trading industrial park Deep reinforcement learning (DRL) electricity market energy management energy trading industrial park

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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Deep Reinforcement Learning-Based Cooperative Frequency controller for Hydropower Dominated Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Hu, Xiong Yang, Xiaoyun Nie, Xing Yao, Chi Liu, Zhi-Wei Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Nanchang Univ Sch Infrastruct Engn Nanchang 330031 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Hydropower dominated systems often experience negative damping and severe power flow oscillations due to improperly tuned governor parameters, even with power system stabilizers. Conventional methods optimize these parameters in a single-machine infinite bus model, neglecting interactions between generations during primary frequency control. This article proposes a deep reinforcement learning based cooperative primary frequency controller using a goal representation heuristic dynamic programming model to address this challenge. The controller minimizes power flow oscillations and frequency deviation through cooperative action among hydrogenerations. Case studies on an IEEE 9 bus system demonstrate that the proposed method achieves superior damping of power oscillations and achieves a much shorter regulation time in the primary frequency control compared to the traditional PID controllers.

关键词： Frequency control Oscillators Damping Hydroelectric power generation Load flow Optimal control Power system stability Transfer functions Stability analysis Blades Cooperative control deep reinforcement learning hydropower dominated system power flow oscillations primary frequency control

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Gaussian Mixture Model Uncertainty Modeling for Power Systems Considering Mutual Assistance of Latent Variables

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第2期16卷 1483-1486页

作者： Yang, Xiao Li, Yuanzheng Zhao, Yong Li, Yang Hao, Guokai Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132000 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

Gaussian mixture model (GMM) and Dirichlet process mixture model (DPMM) are the primary techniques used to characterize uncertainties in power systems, which are commonly solved by expectation-maximization (EM) algorithm. However, for the massive data of uncertain variables, the algorithm encounters challenges in accurately obtaining GMM and DPMM with a lower time consumption. To address this issue, we propose a method for GMM uncertainty modeling in power systems considering the mutual assistance of latent variables. Specifically, the GMM of uncertain variables is first constructed, and the conditional probability is employed to characterize the mutual assistance of latent variables. Then, an improved EM algorithm is developed to obtain the optimal GMM parameters, in which the expectation step (E-step) and maximization step (M-step) of the algorithm are revised using the conditional probability. Importantly, the closed-form solutions for GMM parameters are rederived in the revised E-step and M-step. Finally, the proposed uncertainty modeling method is compared with the traditional GMM and DPMM on actual wind power and load data from Australia. The proposed method performs the efficiency and accuracy in characterizing uncertainty.

关键词： Uncertainty Correlation Probability distribution Load modeling Wind power generation Renewable energy sources Power system stability Gaussian mixture model uncertainty modeling latent variables EM algorithm

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Feature Transductive Distribution Optimization for Few-Shot image Classification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2230-2243页

作者： Liu, Qing Tang, Xianlun Wang, Ying Li, Xingchen Jiang, Xinyan Li, Weisheng Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China Chongqing Vocat Inst Engn Big Data & Internet Things Sch Chongqing 402260 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing 400065 Peoples R China

Few-shot learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with changing task distributions in the presence of limited annotated samples. However, the learned model is susceptible to overfitting and may fail to identify effective classification boundaries due to the biased distribution resulting from a limited number of training samples. Moreover, if the support samples from different classes in the new task are in close proximity, this may lead to fuzzy or even biased class decision boundaries. To address the issues, we propose a generation-based Feature Transductive Distribution Optimization (FTDO) in our research. Specifically, we calibrate the distribution of novel classes by utilizing high-confidence unlabeled query samples from these novel classes, together with the statistics of similar base classes, to generate a sufficient number of virtual training samples. In addition, we introduce a task commonality removal and discriminability enhancement module, which eliminates commonality from all features in the task along the task-commonality direction, and reinforces the retained discriminative features through a channel transformation function. Our method can be implemented using off-the-shelf pre-trained feature extractors and classification models, without requiring additional parameters. Experiments conducted on four few-shot classification datasets substantiate the superiority of our proposed method.

关键词： Few-shot learning distribution calibration image classification image classification image classification

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IRSEnet: Differentially Private image Generation with Multi-Scale Feature Extraction and Residual Channel Attention 13

IRSEnet: Differentially Private Image Generation with Multi-...

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13th International Conference on intelligent control and Information processing, ICICIP 2025

作者： Li, Jiahao Wang, Zhongshuai Ghazali, Kamarul Hawari Bin Yan, Suqing Lan, Rushi Sun, Xiyan Luo, Xiaonan Guangxi Key Lab. of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Centre for Advanced Industrial Technology University of Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Int. Joint Research Lab. of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9798331516147

Privacy-preserving image generation is particularly crucial in fields like healthcare, where data are both sensitive and limited. However, effective privacy preservation often compromises the visual quality and utility of the generated images due to privacy budget constraints. To address this issue, in this paper, We propose a novel network architecture, IRSEnet, which combines multi-scale feature extraction technology and residual channel attention mechanisms, aiming to enhance the visual quality of generated images and improve the performance of downstream classification tasks under differential privacy. The differential privacy mechanism ensures the security of sensitive data during training, while the multi-scale feature extraction module enhances feature extraction capabilities through parallel convolutional layers at multiple scales. Additionally, the channel attention module dynamically adjusts channel weights to focus on the most discriminative features. Experimental results demonstrate that this model significantly improves the utility of generated images and the accuracy of downstream classification tasks while preserving privacy. Future work will explore the application of this approach on larger datasets and across more diverse tasks. © 2025 IEEE.

关键词： Differential privacy

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Point-Based Stabilization control of Linear Parabolic Partial Differential Equation Systems on Space-Time Scales

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第4期70卷 2552-2559页

作者： Wan, Peng Zeng, Zhigang Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The extensively studied partial differential equation (PDE) systems evolve on continuous time and continuous space, which restricts the investigation scope of PDE systems. A parabolic PDE system on space-time scales is constructed for the first time. This article focuses on global exponential stabilization (GES) of a linear parabolic PDE systems on space-time scales by using point-based collocated and noncollocated observations. First, we give the definitions of space-time scales, which contain four cases: continuous time and continuous space, continuous time and discrete space, discrete time and continuous space, and discrete time and discrete space. Wirtinger inequality on space scales is demonstrated, which provides mathematically analytical technique for PDE systems on space-time scales. Second, point-based collocated stabilization control strategy is developed for parabolic PDE systems on space-time scales. Third, considering the fact that sensors and actuators are always placed at different locations, a noncollocated point-based feedback controller is designed by using the observer estimation states. Different from the existing literature, the spatial domain does not need to be divided again according to the actuator and sensor locations. By using the Lyapunov function method, calculus on space-time scales, integration by parts, and Wirtinger inequality on space scales, sufficient criteria are developed such that the closed-loop systems can achieve GES. Finally, numerical simulation examples are presented to show the effectiveness of the developed point-based controllers.

关键词： Aerospace electronics Sensors Actuators Observers Germanium Vectors Symmetric matrices Linear matrix inequalities Adaptive control Sensor systems Noncollocated observation observer partial differential equation (PDE) point-based control space-time scales

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Performance analysis and multi-objective optimization of a hybrid system combining reversible solid oxide cell and direct contact membrane distillation

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Xu, Sheng Huo, Haibo Zhu, Hongxiang Xu, Kui Lei, Zhengling Xu, Jingxiang Li, Xi Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Engn Res Ctr Marine Renewable Energy Shanghai 201306 Peoples R China Harbin Engn Univ Coll Power & Energy Engn Harbin 150000 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc Intelligent & Control Educ Minist Wuhan 430074 Peoples R China

To meet the electricity, hydrogen, and freshwater demands of isolated island users, and coping with the intermittent and fluctuating nature of renewable energies, this paper proposes a hybrid system of combining a reversible solid oxide cell (rSOC) with direct contact membrane distillation (DCMD). The rSOC technology is used to achieve energy time-shifting. The waste heat from the operation of rSOC is recovered through DCMD to improve the system's overall performance and produce freshwater. A high-fidelity model of the rSOC-DCMD hybrid system is established and validated. Based on this model, a comprehensive sensitivity analysis including single-parameter sensitivity analysis, Spearman correlation analysis, and multi-parameter sensitivity analysis was performed. The analysis results indicate that current density is the critical factor influencing system performance while fuel utilization and fuel mole fraction primarily affect operating costs. The air excess ratio is crucial for optimizing system efficiency and freshwater productivity by balancing parasitic power and waste heat, however, adjusting the feed flow rate can further improve performance by regulating the temperature of the seawater entering the DCMD. Additionally, spearman correlation analysis further quantifies the conflicting relationship between any two of the performance indicators in the electrolysis cell/fuel cell (EC/FC) mode. In both modes, minimizing operating costs shows a weak conflict with maximizing system efficiency (correlation coefficient < 0.1) but a strong conflict with maximizing freshwater yield (correlation coefficient > 0.3). Through multi-objective optimization and optimal point decision-making, the optimal solution for three performance indicators is identified at each power level, ensuring optimal hybrid system operation under any condition. As a result, within the full power range of the EC/FC mode, the hybrid system can achieve average system efficiencies of 82.2 %/76.6 %, avera

关键词： Reversible solid oxide cell Direct contact membrane distillation Waste heat recovery Sensitivity analysis Multi-objective optimization

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Coupled Urban Risks: A Complex Systems Perspective with a People-Centric Focus

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ENGINEERING 2025年第1期44卷 44-50页

作者： Ouyang, Min Cheng, Zekai Ma, Jiaxin Wang, Hongwei Mitoulis, Stergios Aristoteles Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Univ Birmingham Sch Engn Birmingham B15 2TT England

The complexity of coupled risks, which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives, is inherent to cities functioning as dynamic, interdependent systems. A disruption in one domain ripples across various urban systems, often with unforeseen consequences. Central to this complexity are people, whose behaviors, needs, and vulnerabilities shape risk evolution and response effectiveness. Realizing cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban risks. This paper adopts a complex systems perspective to examine the intricacies of coupled urban risks, emphasizing the critical role of human decisions and behavior in shaping these dynamics. We focus on two key dimensions: cascading hazards in urban environments and cascading failures across interdependent exposed systems in cities. Existing risk assessment models often fail to capture the complexity of these processes, particularly when factoring in human decision-making. To tackle these challenges, we advocate for a standardized taxonomy of cascading hazards, urban components, and their interactions. At its core is a people-centric perspective, emphasizing the bidirectional interactions between people and the systems that serve them. Building on this foundation, we argue the need for an integrated, people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response capacities. By leveraging real-time data, advanced simulations, and innovative validation methods, this framework aims to enhance the accuracy of coupled urban risk modeling. To effectively manage coupled urban risks, cities can draw from proven strategies in real complex systems. However, given the escalating uncertainties and complexities associated with climate change, prioritizing people-centric strategies is crucial. This ap

关键词： Coupled urban risks People-centric Risk management

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