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Proceedings of the 2022 12th International Conference on Communication and Network Security

作者： Yihe Zhang Shanshan Tu Muhammad Waqas Yongjie Yang Aiming Wu Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China Computer Engineering Department College of Information Technology University of Bahrain Bahrain and School of Engineering Edith Cowan University Australia

ISBN: (纸本)9781450397520

With the developments of the Internet of Things, the demands of low latency, high bandwidth and high-performance computing has increased higher. Therefore, the distributed computing named Fog Computing has proposed to solve the problem above. Fog computing can provide lower transmission latency, faster response time and less network congestion. However, the fog devices are unable to guarantee the security and privacy of data transmission, due to they are vulnerable to attack. Blockchain technology works as a decentralized public ledger to store and share transactions. Blockchain can improve security and protect data privacy of Fog Computing. Moreover, there are still issues in the blockchain-enabled Fog Computing, the two main issues are the energy consumption and computing efficiency. Thus, in this paper, we propose an optimization framework for blockchain-enabled Fog Computing systems to optimize resource allocation. Besides, we adopt the dueling deep reinforcement learning to obtain the optimal resource allocation strategy, with dynamically selecting the fog server, offloading decision, block size. Simulation results show that the proposed framework can reduce the energy consumption and computation overhead of the system, as well as can improve the computing efficiency.

关键词： Fog Computing Deep Reinforcement Learning Blockchain Internet of things

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

arXiv

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arXiv 2023年

作者： Desai, Tushar Oliva, Federico Ferrari, Riccardo M.G. Carnevale, Daniele Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Rome Italy

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model. © 2023, CC BY.

关键词： Simulink

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Optimal Evasion from a Sensing-Limited Pursuer

Optimal Evasion from a Sensing-Limited Pursuer

引用

American control Conference (ACC)

作者： Dipankar Maity Alexander Von Moll Daigo Shishika Michael Dorothy Department of Electrical and Computer Engineering North Carolina Battery Complexity Autonomous Vehicle and Electrification Research Center (BATT CAVE) University of North Carolina Charlotte NC USA Air Force Research Laboratory WPAFB Control Science Center OH USA Army Research Directorate DEVCOM Army Research Laboratory APG MD USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper investigates a partial-information pursuit evasion game in which the Pursuer has a limited-range sensor to detect the Evader. Given a fixed final time, we derive the optimal evasion strategy for the Evader to maximize its distance from the pursuer at the end. Our analysis reveals that in certain parametric regimes, the optimal Evasion strategy involves a ‘risky’ maneuver, where the Evader's trajectory comes extremely close to the pursuer's sensing boundary before moving behind the Pursuer. Additionally, we explore a special case in which the Pursuer can choose the final time. In this scenario, we determine a (Nash) equilibrium pair for both the final time and the evasion strategy.

关键词： Games Nash equilibrium Sensors Trajectory

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Competitive Perimeter Defense of Conical Environments

Competitive Perimeter Defense of Conical Environments

引用

IEEE Conference on Decision and control

作者： Shivam Bajaj Eric Torng Shaunak D. Bopardikar Alexander Von Moll Isaac Weintraub Eloy Garcia David W. Casbeer Department of Electrical and Computer Engineering Michigan State University Department of Computer Science and Engineering Michigan State University Control Science Center Air Force Research Laboratory

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitrarily released at the circumference of the environment and they move radially toward the perimeter with fixed speed. We present a competitive analysis approach to this problem by measuring the performance of multiple online algorithms for the vehicle against arbitrary inputs, relative to an optimal offline algorithm that has information about entire input instance in advance. In particular, we establish two necessary conditions on the parameter space to guarantee (i) finite competitiveness of any algorithm and (ii) a competitive ratio of at least 2 for any algorithm. We then design and analyze three online algorithms and characterize parameter regimes in which they have finite competitive ratios. Specifically, our first two algorithms are provably 1, and 2-competitive, respectively, whereas our third algorithm exhibits different competitive ratios in different regimes of problem parameters. Finally, we provide a numerical plot in the parameter space to reveal additional insights into the relative performance of our algorithms.

关键词： Atmospheric measurements Aerospace electronics Particle measurements

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Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols

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science China(Information sciences) 2022年第1期65卷 56-71页

作者： Yajing ZHANG Qimin XU Xinping GUAN Cailian CHEN Mingyan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management College of Computer Science Chongqing University

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems(ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service(Qo S) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5 th generation(5 G) wireless communication technology provides ultra-reliable and low-latency communication(URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking(TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network(HTSN) co-designed by 5 G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling(SPS) to reduce signaling delay by reserving resources in *** we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing(PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.

关键词： industrial cyber-physical systems (ICPS) 5G-TSN co-design wireless/wired integrated network risk-sensitive reinforcement learning deterministic communication

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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

arXiv

引用

arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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Test-Time Adaptation with Robust Dual-stream Perturbation for Stable Agent Deployment in Dynamic Scenarios

Test-Time Adaptation with Robust Dual-stream Perturbation fo...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Haoyu Xiong Chengfu Yang School of Information Science and Technology Yunnan Normal University Kunming China Department of Education of Yunnan Province Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Test-Time Adaptation (TTA) aims to provide a deployed agent capable of adapting to the target domain distribution using only unlabeled test data. Most existing TTA methods have achieved success under mild conditions, such as independently sampled data from a single or multiple static domains. However, these attempts may cause the deployed agent to fail in dynamic scenarios, where the test environment undergoes continuous changes over time. Previous TTA methods attempt to improve the quality of pseudo-labels by designing data augmentation strategies based on domain information. However, this violates the TTA paradigm, i.e., no characteristics of the dataset can be known in advance. Motivated by this, we propose a Robust Dual-stream Perturbation Test-time adaptation approach, called RDPT, to further stabilize the deployed agent from two aspects: (1) boost the robustness to noisy samples by encouraging the model to be consistent with its original image-level perturbation samples; (2) dynamically filtering samples with high entropy values, taking into account the discrepancy between the original and perturbation samples. Extensive experiments on the CIFAR10C and CIFAR100C benchmarks demonstrate the effectiveness of our method in dynamic scenarios, which is also easy to implement and deploy. 1

关键词： Adaptation models Filtering Federated learning Perturbation methods Computational modeling Predictive models Data augmentation

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Application of artificial intelligence in diagnosis of pulmonary tuberculosis

引用

Chinese Medical Journal 2024年第5期137卷 559-561页

作者： Jingli Du Yue Su Juan Qiao Shang Gao Enjun Dong Ruilan Wang Yanhui Nie Jing Ji Zhendong Wang Jianqin Liang Wenping Gong Tuberculosis Control Team Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Department of Computer Science and Technology College of Management Science and EngineeringCapital University of Economics and BusinessBeijing 100071China The Second Ward of Tuberculosis Department Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China

Tuberculosis(TB)is an infectious disease caused by Mycobacterium *** the diagnostic technology of pulmonary tuberculosis(PTB)has advanced,accurate and differential diagnoses of PTB are still *** recent years,the rapid development of artificial intelligence(AI)and its wide application in the medical field have provided new opportunities for diagnosing and treating TB and *** machine learning model of AI has not only helped physicians improve diagnostic accuracy,but also enabled them to make early preventive diagnoses for individuals at increased risk of ***,AI can guide physicians to formulate targeted treatment strategies for PTB patients with different conditions.

关键词： tuberculosis diagnosis artificial

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Quantized Event-Based Sampled-Data Nonlinear control for PEM Fuel Cell Air Supply 63

Quantized Event-Based Sampled-Data Nonlinear Control for PEM...

引用

63rd IEEE Conference on Decision and control, CDC 2024

作者： Salucci, P. Di Ferdinando, M. Di Benedetto, M.D. Balluchi, A. Di Gennaro, S. Pepe, P. University of l'Aquila Via Vetoio Loc. Coppito Department of Information Engineering Computer Science and Mathematics L'Aquila67100 Italy Center of Excellence Dews Via Vetoio Loc. Coppito L'Aquila67100 Italy Pure Power Control S.r.l. Via Carbonia 8 Cascina Pisa56023 Italy

ISBN: (纸本)9798350316339

The problem of oxygen starvation avoidance in air-feed proton exchange membrane fuel cells is widely known and studied in the literature because it crucially affects the performances of the overall system. To address this problem, many nonlinear control techniques have been provided which, however, do not take into account the effects on the performances induced by the digital hardware commonly used for the practical implementation of control strategies. In this paper, a methodology for the design of quantized sampled-data event-based stabilizers is proposed for a class of time-varying non-linear systems and applied to the problem of oxygen starvation and maximum net power achievement in Proton Exchange Membrane Fuel Cells (PEMFCs). In particular, by exploiting a Lyapunov-based approach and the stabilization in the sample-and-hold sense theory, it is shown that there exist a suitably fast sampling and an accurate quantization of the input/output channels such that the digital event-triggered implementation of a proposed continuous-time stabilizer ensures the semi-global practical stability property of the related closed-loop system. In the theory developed here, time-varying sampling periods and non-uniform quantization of the input/output channels are allowed. Simulations confirm the effectiveness of the theoretical results. © 2024 IEEE.

关键词： Continuous time systems

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