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43rd Chinese control Conference, CCC 2024

作者： Ye, Lu Zheng, Zhao Ziyu, Zhou Haojie, Wang Dongzhuo, Zhao North China Electric Power University School of Control and Computer Engineering Baoding071003 China

ISBN: (纸本)9789887581581

In order to address the issue of low segmentation accuracy in the weak flame region of waste incineration flame images and the potential loss of texture details at the flame edge, this study proposes an algorithm for segmenting incinerator flame images based on multi-step image enhancement. The algorithm consists of several steps. Firstly, a single-scale Retinex algorithm is employed to enhance the details of the noise-reduced image. Subsequently, an adaptive gamma correction method based on the inverse color transform is proposed to enhance the contrast between the image foreground and background, in conjunction with the inverse color transform algorithm. Finally, a 2D-Otsu algorithm is utilized to segment the image and extract the target flame region. Experimental results demonstrate that the proposed incinerator flame image segmentation algorithm based on multi-step image enhancement achieves superior segmentation performance, effectively preserving more detailed information in the weak flame region. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Premixed flames

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai201804 China Ant Group Shanghai200122 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Fudan University School of Computer Science Shanghai200438 China Tongji University Department of Control Science and Engineering Shanghai201804 China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne3122 Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this article, we propose a hypergraph learning framework named learning from heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterward, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experimental results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Intelligent Internet of Things in Mammography Screening Using Multicenter Transformation Between Unified Capsules

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IEEE Internet of Things Journal 2023年第2期10卷 1536-1545页

作者： Wang, Boyan Hu, Xuegang Zhang, Jinglin Xu, Chenchu Gao, Zhifan Hefei University of Technology School of Computer Science and Information Engineering Hefei China Shandong University School of Control Science and Engineering Jinan250061 China Anhui University School of Computer Science and Technology Hefei230093 China Sun Yat-sen University School of Biomedical Engineering Guangzhou510275 China

Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the be

关键词： Mammography

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RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay

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Journal of Systems Science & Complexity 2022年第4期35卷 1262-1277页

作者： XU Juanjuan ZHANG Zhaorong School of Control Science and Engineering Shandong UniversityJinan250061China School of Electrical Engineering and Computer Science University of NewcastleNewcastleCallaghanNSW2308Australia

This paper is concerned with the multi-agent systems with both packet dropout and input delay.A novel receding horizon control(RHC)based consensus protocol is proposed by solving a distributed RHC based optimization *** novelty of the optimization problem lines in the involvement of the neighbours’predictor information in the cost *** on the derived RHC based consensus protocol,the necessary and sufficient condition for the mean-square consensus is *** addition,the authors give a specific sufficient condition to guarantee the mean-square consensus.

关键词： Consensus delay packet dropout predictor-like protocol receding horizon control

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Coordinated Operation of Virtual Coupling High-Speed Trains Under a Novel Sequential Model Predictive control Framework

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无人系统（英文） 2024年第2期12卷 359-373页

作者： Weiqi Bai Zixuan Zhang Xingtang Wu Yizhan Xiong Hairong Dong School of Automation Science and Electronic Engineering Beihang UniversityBeijing 100191P.R.China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijingP.R.China School of Control and Computer Engineering North China Electric Power UniversityBeijingP.R.China

This paper deals with the coordinated control problem of the multiple high-speeds operating under the virtual coupling mode with special consideration of the coupled safety inter-train distance constraints and other independent constraints affecting train ***-ering the inertial lag phenomenon within longitudinal dynamics,this study employs a third-order nonlinear control model to characterize train *** cope with the intricate running constraints explicitly,a centralized constrained model predictive(MPC)control problem is formulated for the regulation of the *** the framework of sequential solving approach,a novel distributed model predictive control(DMPC)algorithm is designed based on a distributed train running information exchange *** algorithm decomposes the centralized MPC problem into a sequence of local problems,allowing for sequential computation and meeting the real-time control ***,the algorithm mitigates solution conservatism arising from the distributed optimization mechanism by accounting for potential hypothetical control policies among a train's topological neighbors when designing its control *** feasibility and effectiveness of the proposed results are rigorously confirmed through theoretical analysis and illustrated by numerical experiment results.

关键词： Coordinated control multiple high-speed trains distributed model predictive control

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Energy Efficient Dual User Association for Large Scale IRS-Aided mmWave Communication Networks

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China Communications 2025年第1期22卷 182-195页

作者： Chen Guolin Deng Yiqin Huang Xiaoxia Fang Yuguang School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Control Science and Engineering Shandong UniversityJinan 250100China Department of Computer Science City University of Hong KongHong Kong 999077China

The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention *** the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS ***,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and *** problem is efficiently addressed in two ***,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE *** reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and *** results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

关键词： energy efficiency intelligent reflecting surface,millimeter-wave communication

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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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A Weighted Slope One Collaborative Filtering Algorithm for Improved User Relevance

A Weighted Slope One Collaborative Filtering Algorithm for I...

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2023 International Conference on Artificial Intelligence and Automation control, AIAC 2023

作者： Li, Ruizhi Liu, Qicheng Yantai University School of Computer and Control Engineering Yantai264005 China

ISBN: (纸本)9798350383805

Considering the low accuracy of recommendation results of current collaborative filtering algorithms caused by data sparsity, this paper proposes a weighted Slope One collaborative filtering algorithm for improved user relevance (WSOCF-IUR). The proposed algorithm takes into account the influence of the number of different user comments and the number of common rating items on recommendation accuracy. In addition, by incorporating user activity and improving the Pearson correlation coefficient, the algorithm reduces the influence of interfering data on recommendations and improving the overall recommendation accuracy. Experimental results demonstrate that the proposed algorithm achieves better recommendation results than other collaborative filtering recommendation algorithms. © 2023 IEEE.

关键词： Collaborative filtering

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GraphSA: Smart Contract Vulnerability Detection Combining Graph Neural Networks and Static Analysis 26

GraphSA: Smart Contract Vulnerability Detection Combining Gr...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： He, Long Zhao, Xiangfu Wang, Yichen Yang, Jiahui Sun, Xuelei School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9781643684369

Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others. © 2023 The Authors.

关键词： Smart contract

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Defense for Advanced Persistent Threat with Inadvertent and Malicious Insider Threats

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无人系统（英文） 2024年第2期12卷 341-358页

作者： Ziqin Chen Guanpu Chen Yiguang Hong Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghaiP.R.China School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

In this paper,we propose a game theory framework to solve advanced persistent threat problems,especially considering two types of insider threats:malicious and *** this framework,we establish a unified three-player game model and derive Nash equilibria in response to different types of insider *** analyzing these Nash equilibria,we provide quantitative solutions to advanced persistent threat problems pertaining to insider ***,we have conducted a comparative assessment of the optimal defense strategy and corresponding defender's costs between two types of insider ***,our findings advocate a more proactive defense strategy against inadvertent insider threats in contrast to malicious ones,despite the latter imposing a higher burden on the *** theoretical results are substantiated by numerical results,which additionally include a detailed exploration of the conditions under which different insiders adopt risky *** conditions can serve as guiding indicators for the defender when calibrating their monitoring intensities and devising defensive strategies.

关键词： Security game advanced persistent threat insider threats Nash equilibrium

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