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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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FOSS: A Self-Learned Doctor for Query Optimizer

arXiv

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arXiv 2023年

作者： Zhong, Kai Sun, Luming Ji, Tao Li, Cuiping Chen, Hong Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China Shanghai Yunxi Technology Co. Ltd. China

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 441-454页

作者： Yongliang Yang Zhijie Liu Qing Li Donald C.Wunsch the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China the Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear *** asymmetric output constraints and input saturation are *** asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear *** avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each *** deal with the actuator saturation,an additional auxiliary dynamical system is *** is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately *** simulation examples are conducted to verify the presented adaptive fuzzy controller design.

关键词： Asymmetric barrier Lyapunov function input saturation Nussbaum function time-varying prescribed performance

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Study on Magnetic Field Coupling in Integrated Magnetic Suspension Spherical Induction Motor

Study on Magnetic Field Coupling in Integrated Magnetic Susp...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： He, Wei Zhao, Jing Yang, Lei Guo, Youguang Beijing Institute of Technology School of Automation Beijing China Beijing Institute of Control Engineering Beijing Key Laboratory of Long-Life Technology of Precise Rotation and Transmission Mechanisms Beijing China University of Technology Sydney School of Electrical and Data Engineering Sydney Australia

ISBN: (纸本)9798350301571

Due to characteristics of 3-degree-of-freedom motion and structure of integrated magnetic suspension spherical induction motor (IMSSIM), there is a complex magnetic coupling problem in this machine. This paper studies the magnetic coupling problem in the IMSSIM. The structure of IMSSIM is investigated and the magnetic coupling in the motor is studied. © 2023 IEEE.

关键词： 3-degree-of-freedom Integrated magnetic suspension spherical induction motor magnetic coupling

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A Character-Word Graph Attention Networks for Chinese Text Classification 12

A Character-Word Graph Attention Networks for Chinese Text C...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Yang, Shigang Liu, Yongguo University of Electronic Science and Technology of China Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering Chengdu China

ISBN: (纸本)9781665438582

Text classification is an important task in natural language processing. Different from English, Chinese text owns two representations, character-level and word-level. The former has abundant connotations and the latter owns specific meanings. Current researches often simply concatenated two-level features with little processing and failed to explore the affiliation relation-ship between Chinese characters and words. In this paper, we proposed a character-word graph attention network (CW-GAT) to explore the interactive information between characters and words for Chinese text classification. A graph attention network is adopted to capture the context of sentences and the interaction between characters and words. Extensive experiments on six real Chinese text datasets show that the proposed model outperforms the latest baseline methods. © 2021 IEEE.

关键词： Classification (of information)

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An Intelligent Design Method Based on Case-based Reasoning and Reinforcement Learning

An Intelligent Design Method Based on Case-based Reasoning a...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Yu Huang Ru Wang Zhuqin Wei Guoxin Wang Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology School of Machanical Engineering Beijing Institute of Technology Beijing China

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.

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Adaptive neural prescribed performance control for PMSM with time delays

Adaptive neural prescribed performance control for PMSM with...

引用

2022 Chinese Automation Congress, CAC 2022

作者： Li, Tandong Li, Shaobo Zhang, Junxing GuiZhou University School of Mechanical Engineering Guiyang China GuiZhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9781665465335

In this paper, an adaptive neuro-prescribed performance control is proposed for the PMSM with time delays. First, a novel prescribed performance function is proposed, and the constrained system is converted into unconstrained. Then, the time delays and the unknown nonlinearities are addressed by combining the proper Lyapunov-Krasovskii functionals with the neuro-learning techniques. Furthermore, the first-order filters are introduced to overcome the "explosion of complexity"generated from the backstepping procedures. After that, stability analysis shows that the closed-loop system are gradually stable. Finally, the simulations illustrate the effectiveness of the suggested scheme. © 2022 IEEE.

关键词： Time delay

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Research on Channelization Techniques of Radio Astronomical Wideband Signal with Oversampled Polyphase Filter Banks

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Research in Astronomy and Astrophysics 2023年第8期23卷 188-205页

作者： Meng Zhang Hai-Long Zhang Ya-Zhou Zhang Jie Wang Shao-Cong Guo Qiao Meng Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Radio Astronomy Chinese Academy of SciencesNanjing 210008China National Astronomical Data Center Beijing 100101China School of Information Science and Engineering Southeast UniversityNanjing 211100China School of Cyber Science and Engineering Qufu Normal UniversityQufu 273165China

Digital channelization decomposes a wideband signal into multiple adjacent sub-bands using Parallel *** can effectively reduce the pressure on the radio astronomy digital backends system and make wideband signal processing *** at the problems of signal attenuation at sub-band edge,spectral leakage and aliasing encountered in wideband signal channelization,algorithms to reduce the problems are *** design a Critically Sampled Polyphase Filter Bank(CS-PFB)based on the Finite Impulse Response digital filter with a Hamming Window and systematically analyze the frequency response characteristics of the *** on the channelized structure of the CS-PFB,an Over Sampled Polyphase Filter Bank(OS-PFB)is designed by data reuse,and the filtering frequency response characteristics of CS-PFB and OS-PFB are compared and *** the wideband baseband data generated by the CASPSR(Collaboration for Astronomy Signal processing and electronics research Parkes Swinburne Recorder),we implement sub-band division and 16-band output of these data based on the 2×oversampling OS-PFB,and the problem of spectrum inversion in the sub-bands is *** removing 25%of redundant data in the head and tail of each sub-band,we recombine the sub-bands into a *** wideband signal is almost identical to the original observed ***,the experimental results show that the OS-PFB can improve the channel *** the 400 MHz baseband data of J0437-4715,we compare the pulse profile obtained from the original baseband data with the pulse profile obtained after the channelization and *** phase and amplitude information of the pulse profiles are consistent,which verifies the correctness of our channelization algorithm.

关键词： (stars:)pulsars general-methods analytical-miscellaneous-methods data analysis

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DBMGNet: A Dual-Branch Mamba-GCN Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Wang, Hao Zhuang, Peixian Zhang, Xiaochen Li, Jiangyun University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering Shunde Graduate School Beijing100083 China

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) excel at local feature modeling but are limited to Euclidean space. Transformers offer long-range dependence modeling but suffer from high computational complexity. In contrast, graph convolutional networks (GCNs) can process information in non-Euclidean space, compensating for the limitations of CNNs. Meanwhile, the state-space model (SSM) Mamba, thanks to its linear complexity and strong long-range dependence modeling, shows great potential to offer an alternative to Transformers for HSI classification. To address the limitations of CNNs and Transformers while exploiting the potential of Mamba, we propose a dual-branch hybrid architecture named DBMGNet that combines Mamba with GCN for the HSI classification. In the Mamba branch, we design band selection enhanced bidirectional Mamba (BSEBM), which leverages Mamba's long-range dependence modeling and sequential modeling capabilities to process spatial-spectral information. In the GCN branch, we apply reparameterized Chebyshev graph convolution (RCGC) to model similarity dependencies in non-Euclidean space, along with designing an adjacency matrix based on the intrinsic characteristics of HSIs. Extensive experiments demonstrate that our DBMGNet achieves the state-of-the-art performance of HSI classification against 13 mainstream approaches. The code for this work will be available at: https://***/Wanghao00pro/DBMGNet. © 1980-2012 IEEE.

关键词： NP-hard

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