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22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Combining decomposition and graph capsule network for multi-objective vehicle routing optimization

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Intelligent Data Analysis 2025年第1期29卷 116-140页

作者： Zhang, Haifei Ge, Hongwei Li, Ting Zhou, Lujie Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University) Ministry of Education Jiangsu Wuxi China College of Automobile and Transportation Tianjin University of Technology and Education Tianjin China School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan China Medical Image Processing Group Department of Radiology University of Pennsylvania Philadelphia PA United States

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering. © 2024 – IOS Press. All rights reserved.

关键词： attention mechanism decomposition strategy deep reinforcement learning graph capsule network multi objective optimization Urban logistics distribution

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Graph Contrastive Learning With Adaptive Distribution Transformation for Fault Detection and Isolation Using Limited Labeled Faults

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jia, Mingwei Shen, Bingbing Liu, Yi Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Hangzhou Normal University School of Mathematics Hangzhou311121 China McGill University Department of Electrical and Computer Engineering MontrealH3A 0E9 Canada

Fault detection and isolation (FDI) in process industries remain challenging due to misdiagnosed dynamic variations, limited fault labels, and complex variable interactions. To address these challenges, an adaptive distribution transformation-graph contrastive learning (ADT-GCL) is proposed to differentiate between global dynamics and local faults, even when labeled faults are limited. To capture variable interactions at both global and local levels, global and local graphs are established, and a local-global learning strategy is introduced. To prevent the few available fault labels from being misidentified as normal, contrastive tasks first cluster data embeddings before mapping them to labels. For fault isolation, an adaptive distribution transformation loss reconstructs fault embeddings as normal data, thereby increasing reconstruction errors. The T2 statistic is then used to identify fault variables based on these errors. Lastly, case studies on a sugar production process and a multiphase flow process (MFP) demonstrate ADT-GCL’s superiority in distinguishing global dynamics from local faults. © 1963-2012 IEEE.

关键词： Sugar manufacture

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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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DISTRIBUTED computer SYSTEM ON THE BASIS OF THE POOL-processOR CONCEPT.

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Distributed computer control Systems 1983, Proceedings of the Fifth IFAC Workshop.

作者： Plessmann, K.W. Aachen Technical Univ Dep of Process Computer Applications & Process Control Aachen West Ger Aachen Technical Univ Dep of Process Computer Applications & Process Control Aachen West Ger

ISBN: (纸本)0080305466

During the last 6 to 7 years microcomputers have been accepted in industry. Parallel to this fact, there was and is an increasing demand in distributed systems, to cope with the necessities of the running process and the reliability considerations, because the distribution of computer performance over the process is not only a question of demand of performance. For systems in use mainly on the basis of a serial data transfer the distribution is doing nothing else but providing a smaller part of a process with a processing element. This paper deals with a computer system, capable of increasing the performance by using a pool of processors to give the user the possibility to implement as many as necessary. By this, he is no longer forced to provide his system with more performance than the minimum.

关键词： computer SYSTEMS, DIGITAL

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm

Enhancing 6G-Satellite Network Integration with Artificial I...

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Learning and Technology Conference (L&T)

作者： Ibrahim Salah Aziza I. Hussein Mostafa Wageh Lotfy Mohamed Mourad Mabrook Control and Computer Department College of Engineering Almaaqal University Basrah Iraq College of Engineering Energy and Technology Research Center Effat University Saudi Arabia Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

关键词： 6G mobile communication Wireless communication Smart cities Resource management Reliability Internet of Things Stakeholders Artificial intelligence Vehicle dynamics Satellite communications

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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Internal short circuit fault diagnosis for automotive lithium-ion batteries using an adaptive parameter identification model

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Tao, Qian Wang, Yiming Tian, Engang Tao, Hongfeng Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi Jiangsu214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

This paper develops a model-based diagnostic method for internal short circuit (ISC) faults in lithium-ion batteries. This method utilizes a second-order equivalent circuit model (ECM) combined with a recursive least squares method with an adaptive forgetting factor for online parameter identification, with initial parameters optimized by a particle swarm optimization (PSO) algorithm. An extended Kalman filter (EKF) is then employed for state estimation to generate residuals, which are analyzed using the refined cumulative sum (CUSUM) statistical method to detect and diagnose ISC faults. Experimental results show that this method effectively identifies ISC faults in automotive power batteries under various initial state of charge (SOC) levels and fault-free conditions. At 100% initial SOC, fault detection times are 104 seconds for a 15Ω fault, 77 seconds for a 10Ω fault, and 27 seconds for a 1Ω fault;at 90% initial SOC, detection for a 10Ω fault takes 97 seconds, demonstrating high accuracy and robustness. © 2025 Elsevier Ltd

关键词： Lithium-ion batteries

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