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arXiv 2024年

作者： Bai, Shaojie Talebi, Mohammad Sadegh Zhao, Chengcheng Cheng, Peng Chen, Jiming College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Computer Science University of Copenhagen Copenhagen2100 Denmark

Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a central agent is responsible for protecting users’ sensitive data, and the (stronger) local model, where the protection occurs directly on the user side. However, they either require a trusted central agent or incur a significantly higher privacy cost, making it unsuitable for many scenarios. This work introduces a trust model stronger than the central model but with a lower privacy cost than the local model, leveraging the emerging shuffle model of privacy. We present the first generic algorithm for episodic RL under the shuffle model, where a trusted shuffler randomly permutes a batch of users’ data before sending it to the central agent. We then instantiate the algorithm using our proposed shuffle Privatizer, relying on a shuffle private binary summation mechanism. Our analysis shows that the algorithm achieves a near-optimal regret bound comparable to that of the centralized model and significantly outperforms the local model in terms of privacy cost. © 2024, CC BY-NC-ND.

关键词： Differential privacy

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Static Deadlock Detection for Interrupt-driven Embedded Software

Static Deadlock Detection for Interrupt-driven Embedded Soft...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Yu Zhang Kaiwen Zhang Xiaofeng Li Yi Xie Jian Guan Bin Gu Guanjun Liu Department of Computer Science and Technology Tongji University Shanghai China Beijing Control Engineering Institute Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Interrupt-driven embedded software is widely used in safety-critical systems, where any occurrence of errors can lead to serious consequences. Deadlock is a common concurrency error, and deadlock detection methods are mainly designed for multi-threaded programs. However, due to the differences in concurrency control and preemption mechanisms between interrupt-driven programs and multi-threaded programs, the existing deadlock detection methods for multi-threaded programs cannot be directly applied to interrupt-driven programs. A static deadlock detection method is proposed for interrupt-driven programs to enhance the reliability and safety of interrupt-driven embedded software. Building upon the traditional context-sensitive lockset analysis, this method takes into account the concurrent semantics of interrupts and extends the lock graph model. Our method effectively reduces false positives by analyzing the concurrent relationships of cycles in a lock graph. Based on these methods, we develop a deadlock detection tool, and experiments are conducted with it. The results demonstrate that the proposed method can effectively detect deadlocks related to interrupts.

关键词： Concurrent computing Analytical models Semantics Buildings System recovery Software reliability Safety Social intelligence Embedded software Context modeling

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Degree of Spiking Neural P Systems Without Delay

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Chinese Journal of Electronics 2023年第3期21卷 419-424页

作者： Keqin JIANG Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Computer and Information Anqing Teachers College Anqing China

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).

关键词： Computational modeling Neurons Parallel processing Delays

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Constructive Function Approximation with Local Models

Constructive Function Approximation with Local Models

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Mediterranean Conference on control and Automation (MED)

作者： Christos N. Mavridis Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach.

关键词： Training Adaptation models Simulation Stochastic processes Aerospace electronics Approximation algorithms Partitioning algorithms

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CoTV: Cooperative control for Traffic Light Signals and Connected Autonomous Vehicles Using Deep Reinforcement Learning

CoTV: Cooperative Control for Traffic Light Signals and Conn...

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IEEE Symposium on Intelligent Vehicle

作者： Jiaying Guo Long Cheng Shen Wang School of Computer Science University College Dublin Ireland School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction in fuel consumption and emissions, improvements in traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Most existing research in sustainable urban traffic control adjusts either traffic light signals or vehicle speed. Adaptive traffic light signal control can increase the intersection throughput and reduce travel time as well as energy consumption and emissions. Connected Autonomous Vehicles (CAVs) can proactively control vehicle acceleration to achieve more stable traffic nearby with relatively higher driving velocity (i.e., lower fuel consumption and CO 2 emissions) and maintain a safe distance from the surrounding traffic (i.e., longer time-to-collision).

关键词： Energy consumption Traffic control Throughput Deep reinforcement learning Smart transportation Safety Maintenance

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Sustainable Mining in the Era of Artificial Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

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General Decay Synchronization of State and Spatial Diffusion Coupled Reaction-diffusion Memristive Neural Networks

General Decay Synchronization of State and Spatial Diffusion...

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2022 Chinese Automation Congress, CAC 2022

作者： Huang, Yanli Zhao, Xin Lu, Jianmou Li, Jiang Tiangong University School of Computer Science and Technology Tianjin China North China Electric Power University School of Control and Computer Engineering Beijing China Tiangong University Center for Engineering Internship and Training Tianjin China

ISBN: (纸本)9781665465335

The purpose of this paper is to investigate the general decay synchronization (GDS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with state coupling and spatial diffusion coupling. Firstly, by devising a suitable controller and choosing an appropriate Lyapunov functional, we analyze the GDS of CRDMNNs with state coupling and derive a sufficient condition for achieving GDS of this kind of network. Secondly, a criterion for GDS of CRDMNNs with spatial diffusion coupling is also proposed by using several inequality techniques. Finally, one numerical example with simulation results is shown to demonstrate the validity of the obtained GDS result. © 2022 IEEE.

关键词： Synchronization

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Towards experimental insight into magnetic flux distribution within the air gap of permanent magnet synchronous drive

Towards experimental insight into magnetic flux distribution...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Hubert Milanowski Adam Krzysztof Piłat Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on analyzing the distribution of magnetic flux within the motor’s spatial air gap, as well as the amplification of harmonics resulting from changes in air gap orientation. Drawing upon experimental findings, a model is proposed to illustrate the three-dimensional distribution of magnetic flux within the gap.

关键词： Solid modeling Magnetic flux density Automation Atmospheric modeling Air gaps Rotors Drives

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