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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Tan, Lei Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783031466632

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

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Computers, Materials & Continua 2023年第4期75卷 293-309页

作者： Mingshuai Sheng Jingbing Li Uzair Aslam Bhatti Jing Liu Mengxing Huang Yen-Wei Chen School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan

Medical images are used as a diagnostic tool, so protecting theirconfidentiality has long been a topic of study. From this, we propose aResnet50-DCT-based zero watermarking algorithm for use with medicalimages. To begin, we use Resnet50, a pre-training network, to draw out thedeep features of medical images. Then the deep features are transformedby DCT transform and the perceptual hash function is used to generatethe feature vector. The original watermark is chaotic scrambled to get theencrypted watermark, and the watermark information is embedded into theoriginal medical image by XOR operation, and the logical key vector isobtained and saved at the same time. Similarly, the same feature extractionmethod is used to extract the deep features of the medical image to be testedand generate the feature vector. Later, the XOR operation is carried outbetween the feature vector and the logical key vector, and the encryptedwatermark is extracted and decrypted to get the restored watermark;thenormalized correlation coefficient (NC) of the original watermark and therestored watermark is calculated to determine the ownership and watermarkinformation of the medical image to be tested. After calculation, most ofthe NC values are greater than 0.50. The experimental results demonstratethe algorithm’s robustness, invisibility, and security, as well as its ability toaccurately extract watermark information. The algorithm also shows goodresistance to conventional attacks and geometric attacks.

关键词： Medical images deep residual network resnet50-DCT privacy protection robustness security

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

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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 dual-stream spatial-temporal detector for action recognition 4

A dual-stream spatial-temporal detector for action recogniti...

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4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wei, Pingping Li, Jiale Liu, Peiran Li, Li Xu, Yifei Wang, Ling State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiao-Tong University Xi’an710049 China School of Software Xi’an Jiao-Tong University Shaanxi Xi’an710049 China Shaanxi Joint Key Laboratory for Artifact Intelligence Shaanxi 710054 China Xi’an Key Laboratory for Social Intelligence and Complex Data Processing Shaanxi 710054 China Alltuu Inc. Zhejiang Hangzhou310000 China

ISBN: (纸本)9781510682528

In response to the issue that existing human action recognition models can not make full use of complementary information from different modalities, this thesis proposes a multi-path attention module MA to form the MA-GCN model. modalities, this thesis proposes a dual-stream human action recognition model SRHAR that fuses skeleton data and RGB data. This model utilizes LAF proposed in this thesis to fuse skeleton features and RGB features. The introduction of skeleton modality enables the RGB modality to obtain the RGB features. The introduction of skeleton modality enables the RGB modality to obtain complementary information, resulting in more accurate prediction results. This algorithm focuses more on recognition accuracy and has a slower recognition speed, but achieves leading performance in terms of accuracy on public datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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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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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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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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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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