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ANALYSIS OF TRIPARTITE GAME IN POWER SYSTEMS BASED ON UNCERTAIN NASH EQUILIBRIUM THEORY

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 117-128页

作者： Wu, Liang Yan, Xiaolin Yao, Zhangsong Zhao, Teng School of Management and Ningxia Key Laboratory of intelligent information and Big Data Processing North Minzu University Ningxia Yinchuan750021 China School of Mathematical Sciences Tiangong University Tianjin300387 China School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China School of Mathematics and Information Science North Minzu University Ningxia Yinchuan750021 China

Power systems serve as the fundamental infrastructure for the socioeconomic development of modern societies. Researching power systems can stimulate the growth of the power industry and contribute to the sustainable development of society. This paper aims to address uncertainties prevalent in power systems, including extreme weather events, disruptions in renewable energy supply, and adjustments in economic policies. To achieve this objective, an uncertain Nash equilibrium model is established. Subsequently, a game theoretic analysis is conducted to maximize the interests of users, grid companies, and proxy purchasers. Under the condition of not necessarily continuous differentiability, the Riemann-Stieltjes discrete approximation method has been proposed. The difficulty of calculating the expectation of the optimal revenue function has been resolved. Weak first-order equilibrium condition based on Clarke’s generalized gradient, the convergence and convergence rate of uncertain Nash equilibrium solutions are proved. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： power system tripartite game uncertain Nash equilibrium uncertainty theory

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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of information Technology & Electronic engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network

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IEEE Sensors Journal 2025年第9期25卷 15548-15563页

作者： Ye, Miao Jiang, Zhibang Xue, Xingsi Li, Xingwang Wen, Peng Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454099 China

Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies. However, there are several issues that must be addressed, such as the fact that their ability to capture long-term dependencies is not completely reliable, their computational complexity levels are high, and the spatiotemporal features of WSN timing data are not sufficiently extracted for detecting the correlation anomalies of multinode WSN timing data. To address these limitations, this article proposes a WSN anomaly detection method that integrates frequency-domain features with dynamic graph neural networks (GNNs) under a designed self-encoder reconstruction framework. First, the discrete wavelet transform (DWT) effectively decomposes trend and seasonal components of time series to solve the poor long-term reliability of transformers. Second, a frequency-domain attention mechanism (FDAM) is designed to make full use of the difference between the amplitude distributions of normal data and anomalous data in this domain. Finally, a multimodal fusion-based dynamic graph convolutional network (MFDGCN) is designed by combining an attention mechanism and a graph convolutional network (GCN) to adaptively extract spatial correlation features. A series of experiments conducted on public datasets and their results demonstrate that the anomaly detection method designed in this article exhibits superior precision and recall than the existing methods do, with an F1 score of 93.5%, representing an improvement of 2.9% over that of the existing models. © 2025 IEEE. All rights reserved.

关键词： Normal distribution

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： Optimization algorithms

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Laplacian Sparse Dynamic Slow Feature Analysis for Process Monitoring and Fault Isolation

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IEEE Sensors Journal 2025年第9期25卷 15653-15667页

作者： Wu, Ping Xue, Yikai Ni, Yuxuan Pan, Haipeng Zhang, Xi He, Yuchen Gao, Jinfeng Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China China Jiliang University Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province Hangzhou314423 China

The process monitoring and fault isolation are essential for ensuring operational safety and maintaining product quality in the industrial processes. This study introduces a novel approach to process monitoring and fault isolation based on the Laplacian sparse dynamic slow feature analysis (LSDSFA). To simultaneously account for sparsity, dynamics, and global structural information, a new optimization framework for LSDSFA is proposed. This framework incorporates the n l1 norm and a Laplacian graph into dynamic slow feature analysis (SFA), with enhancements to process variable representation. The optimization problem is addressed using the alternating direction method of multipliers (ADMMs). The proposed method decomposes the process variable space into latent and residual subspaces. In addition, three monitoring statistics are developed to facilitate effective process monitoring, while fault isolation is achieved through a two-level contribution plot. Specifically, the intrinsic structure of the process data is captured by employing a graph Laplacian operator to explore intervariable correlations. Simultaneously, the incorporation of the graph Laplacian operator and l1 norm into dynamic SFA (DSFA) reduces variable redundancy, thereby exploiting the intrinsic structure of the process data more effectively and improving interpretability through the sparsity. Experimental evaluations conducted on the widely adopted Tennessee Eastman Process (TEP) benchmark and a real-world blast furnace ironmaking process (BFIP) demonstrate the superior performance of the proposed LSDSFA-based method in comparison with related techniques. © 2001-2012 IEEE.

关键词： Blast furnaces

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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Robust primary quantization step estimation on resized and double JPEG compressed images

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Multimedia Tools and Applications 2025年第12期84卷 11097-11118页

作者： Zhang, Lei Chen, XuGuang Niu, YaKun Zuo, XianYu Wang, Huaqing School of Computer and Information Engineering Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng Henan Henan Kaifeng475004 China National Internet Emergency Center Henan Zhengzhou450000 China

As one of the most important forensic tasks, reconstruction of the original information in tampered images is a key step for tampering detection and localization. Currently, a number of methods have been designed to estimate the primary quantization steps of double compressed JPEG images. However, the estimation in the presence of resizing operation remains a challenge. In this paper, we propose a robust primary quantization steps estimation method on resized and double JPEG compressed images. Specifically, the distribution of Discrete Cosine Transform (DCT) coefficients is firstly analyzed on the inverse resized image. Then, a maximum likelihood function together with a filtering strategy is designed to obtain the primary quantization step on Alternating Current (AC) bands. In addition, we find the prominent peak in the Discrete Fourier Transform (DFT) spectrum of the distribution of Direct Current (DC) coefficients is nonlinearly related to the step. Based on this observation, a mapping function derived from the geometric fitting is proposed to estimate the step on DC band. Experimental results demonstrate the proposed method provides superior estimation performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image compression

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