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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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PERIODIC STATISTICAL ANALYSIS OF GAIT PHASE SEGMENTATION 10

PERIODIC STATISTICAL ANALYSIS OF GAIT PHASE SEGMENTATION

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Fu, Mingkai Wang, Shaohong Ma, Chao Key Laboratory of Modern Measurement and Control Technology Ministry of Edu. Beijing Information Science and Technology Beijing China Beijing Key Lab of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology Beijing China

ISBN: (纸本)9781837242672

In order to achieve accurate segmentation of gait phases for precise real-time control of lower limb exoskeleton robots, the periodic statistical analysis of human motion posture by using the inertial measurement unit is conducted. In current research, the data of forward and turning gait types were acquired and every gait type data were devised into eight for different gait phase segmentation. During the process, the phase division values for different types of subjects were calculated and the phase division of different subjects with the same gait type was calculated. The standard deviations for different gaits and phases for the same experimenter are all below 10, primarily oscillated around 4. For gait data from different subjects, the percentages at the same phase are all below 3. The results showed that the results from the feasible method could better accord with the characteristics of each stage of actual *** those results could provide reference for real-time judgment of gait phase and technical support for intelligent control of lower limb exoskeletons. © The Institution of engineering & technology 2024.

关键词： Exoskeleton (Robotics)

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1571-1583页

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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A Novel Integrated Energy Management Strategy of Energy Storage system for a Pure Electric-Driven Mining Hydraulic Excavator

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中国机械工程学报 2025年第2期38卷 459-475页

作者： Tao Qin Yunhua Li Long Quan Yu Yao Liman Yang School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Beijing Tianma Intelligent Control Technology Co. Ltd.Beijing 101399China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of TechnologyTai Yuan 030024China

Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating ***,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects *** solve the above problem,a pure electric-driven mining hydraulic exca-vator based on electric-motor-driven swing platform and hydraulic pumps was used as the research ***-ver,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing ***,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and ***,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the ***,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predic-tive controller and fuzzy *** addition,a digital prototype of the excavator was verified using an original machine *** performance of the different strategies and driven systems were analyzed using digital *** results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02％.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10％.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.

关键词： Pure electric-driven Mining hydraulic excavator Energy management Predictive controller Fuzzy controller

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An Ambiguous Edge Detection Method for Computed Tomography Scans of Coronavirus Disease 2019 Cases

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IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第1期54卷 352-364页

作者： Singh, Pritpal Huang, Yo-Ping Central University of Rajasthan Quantum Optimization Research Lab Department of Data Science and Analytics Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Magong88046 Taiwan The Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan The Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan The Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of the most difficult tasks in combating this epidemic is to identify and segregate the afflicted people. The reverse transcription-polymerase chain reaction test (RT-PCR) is the most common pathology test used to diagnose this infection. Studies show that the RT-PCR test has a low-positive rate and sometimes becomes ineffective in diagnosing infection. In some cases, computed tomography (CT) scans reveal acute pneumonia and pulmonary anomalies. Therefore, CT scans are used together with RT-PCR tests to confirm infected people. Existing artificial intelligence and machine learning techniques require a large number of CT scans for training, which is a time-consuming process. Visual inspection shows that most CT scans of COVID-19 cases have broken, blurred, and ambiguous edges for infectious areas. Another major issue with these images is the heterogeneous intensity of the pixels, high noise, and low resolution. As a result of all these issues, the problem of effective edges/boundaries of various areas of CT scans of COVID-19 cases cannot be resolved by the current edge detection approach. Indeed, improper selection of edges can lead to an incorrect diagnosis of diseases through CT scans of COVID-19 cases. Therefore, there is an urgent need for a diagnostic method in addition to the RT-PCR test that can extract useful information from the minimum number of chest CT scans of suspected COVID-19 cases. This study introduces a new ambiguous edge detection method (AEDM) for identifying the edges/boundaries of different regions in CT scans of COVID-19 cases. The proposed AEDM is developed on the basis of ambiguous set (AS) theory, which is highly efficient in processing ambiguous pixel information. For simulation purposes, various CT scans of COVID-19 cases are c

关键词： Coronavirus

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DP-GenFL: a local differentially private federated learning system through generative data

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science China(information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

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Reinforcement Learning Based Truck-and-Drone Coordinated Delivery

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 754-763页

作者： Wu, Guohua Fan, Mingfeng Shi, Jianmai Feng, Yanghe Central South University School of Traffic Transportation Engineering Changsha410073 China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory College of System Engineering Changsha410073 China National University of Defense Technology College of Systems Engineering Changsha410073 China

Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities and medical items for home-quarantined people. Considering the unmanned operation, agility, and use of clean energy of drones, we propose a novel truck-and-drone coordinated delivery system that does not require direct human contact during the delivery process. All final deliveries are completed by the drone while the truck acts as a movable charging station and a carrier, so that the contagion risks are reduced. Moreover, we spilt the whole delivery problem into the customer-clustering problem and the routing problems of both the truck and the drone. An encoder-decoder framework combined with reinforcement learning is created to solve the routing problems without handcrafted designed heuristics. We design the different problem contexts specific to the truck routing problem and the drone routing problem. The experimental results show that the proposed approach has good generality and can consequently be applied to problems of different scales with high time efficiency. © 2020 IEEE.

关键词： Reinforcement learning

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