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Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 123-134页

作者： Yanni Wan Jiahu Qin Xinghuo Yu Tao Yang Yu Kang Department of Automation University of Science and Technology of ChinaHefei 230027China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China School of Engineering RMIT UniversityVIC 3000Australia State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China Department of Automation State Key Laboratory of Fire ScienceInstitute of Advanced TechnologyUniversity of Science and Technology of ChinaHefei 230027China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of SciencesBeijing 100190China

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both *** PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption *** challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the *** address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’*** proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price *** comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

关键词： Demand response management(DRM) Markovian decision process(MDP) Monte Carlo simulation reinforcement learning(RL) smart grid

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DFT Architecture for Click-Based Bundled-Data Asynchronous Circuits

DFT Architecture for Click-Based Bundled-Data Asynchronous C...

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2022 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2022

作者： Zhu, Ruimin Xu, Zeyang Huang, Yuhao Xiao, Shanlin Yu, Zhiyi School of Microelectronics Science and Technology Sun Yat-sen University China School of Electronics and Information Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (数字)9781665492690

ISBN: (纸本)9781665492690

Event-driven asynchronous circuits are gaining attention because of their low power consumption and robustness. Among asynchronous circuits, the Bundled data (BD) circuit used by Loihi has attracted attention because it can obtain a similar area as a synchronous circuit. Click circuit is a mainstream BD circuit, but due to the lack of DFT (Design For Test) architecture, the Click-based asynchronous circuit cannot be widely used. This paper proposes a DFT architecture suitable for BD circuits, which can be accomplished using traditional EDA tools rather than developing new ones. This paper verifies the proposed DFT architecture on a five-stage pipeline processor based on the RISC-V instruction set. The result is 99.62% coverage for stuck-at faults, 1.8398% area overhead, and 6.2259% power overhead. © 2022 IEEE.

关键词： Timing circuits

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Bandstop Cavity Filter Based Decoupling Method for 1×2 MIMO Slot Antennas

Bandstop Cavity Filter Based Decoupling Method for 1×2 MIMO...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Mingqi Li Nan Yang Kai Lu Peng Fei Hu Yunliang Long School of Electronics and Information Technology (School of Microelectronics) and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

In this paper, a bandstop cavity filter is presented for decoupling of 1×2 MIMO (multiple-input multiple-output) slot antennas. A cavity is formed by placing two rectangular waveguides (RWGs) side by side with shared narrow wall removed. By fine tuning the cavity size, the TE 120 and TE 130 modes of the cavity can be excited to obtain two transmission zeros (TZs) through their bypass coupling with non-resonating TE 10 mode, thus forming a bandstop filter. In this paper, this bandstop filter is used to feed and decouple 1×2 45° polarized slot antennas. Because of the bandstop characteristic of the cavity filter, the isolation between two feeding ports can be high in nature, suitable for MIMO applications. Moreover, the field distribution in cavity is asymmetric due to field superposition, so only one slot antenna can radiate, and the radiation patterns are also restored. To verify the effectiveness of this method, a prototype is designed. Simulated results show that isolation is higher than 35 dB in band of interest and typical unidirectional radiation patterns are also obtained.

关键词： Couplings Slot antennas Transmitting antennas Prototypes Rectangular waveguides Antenna feeds Filtering theory

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Hybrid signal fusion for target detection in distributed PA-MIMO radar systems on moving platforms

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhou, Dingsen Yang, Minglei Lian, Hao Greco, Maria Sabrina Gini, Fulvio Xidian University National Key Laboratory of Radar Signal Processing Shaanxi Xi'an710071 China Science and Technology on Underwater Test and Control Laboratory Dalian116013 China University of Pisa Department of Information Engineering Pisa56122 Italy

Distributed phased multiple-input multiple-output (PA-MIMO) radar systems on moving platforms combine the spatial diversity of MIMO radars with the coherent gain of phased-array radars, making them ideal for dynamic environments. However, challenges such as motion-induced channel variability, Doppler shifts, and complex parameter estimation often hinder target detection. Traditional methods like non-coherent fusion (NCF) and coherent fusion (CF) struggle to adapt to such variability, leading to suboptimal performance. In this paper, we propose a hybrid signal fusion (HSF) scheme that integrates joint space-time-phase compensation (JSTPC) with a compressed fusion network. The JSTPC module mitigates motion-induced phase errors and ensures precise alignment of received signals, while the compressed fusion network combines CF and NCF stages, leveraging their respective strengths to adaptively manage channel correlation variability. We also develop a joint parameter estimation algorithm to accurately extract direction of departure, direction of arrival, and Doppler frequency, thereby ensuring precise signal reconstruction and enhancing the detection process by estimating key parameters in real time. Theoretical analyses of detection thresholds and probabilities, derived using moment-matching techniques, further validate the reliability of the proposed scheme. Monte Carlo simulations and theoretical analyses for diverse radar configurations and channel correlation scenarios show that the proposed HSF scheme achieves 2 dB detection gain improvement over NCF. © 2025 IEEE.

关键词： Frequency estimation

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FIXDRIVE: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation

FIXDRIVE: Automatically Repairing Autonomous Vehicle Driving...

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International Conference on Software Engineering (ICSE)

作者： Yang Sun Christopher M. Poskitt Kun Wang Jun Sun School of Computing and Information Systems Singapore Management University Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose Fixdrive, a framework that analyses driving records from near-misses or law violations to generate AV driving strategy repairs that reduce the chance of such incidents occurring again. These repairs are captured in μDrive, a high-level domain-specific language for specifying driving behaviours in response to event-based triggers. Implemented for the state-of-the-art autonomous driving system Apollo, Fixdrive identifies and visualises critical moments from driving records, then uses a Multimodal Large Language Model (MLLM) with zero-shot learning to generate μDrive programs. We tested Fixdrive on various benchmark scenarios, and found that the generated repairs improved the AV's performance with respect to following traffic laws, avoiding collisions, and successfully reaching destinations. Furthermore, the direct costs of repairing an AV—15 minutes of offline analysis and $0.08 per violation-are reasonable in practice.

关键词： Visualization Runtime Large language models Zero shot learning Maintenance engineering Trajectory Software reliability Driver behavior Autonomous vehicles Vehicles

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Human–Robot Coordination control for Sit-to-Stand Assistance in Hemiparetic Patients With Supernumerary Robotic Leg

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 16591-16602页

作者： Jie Zuo Jun Huo Xiling Xiao Yanzhao Zhang Jian Huang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Hubei Key Laboratory of Brain-Inspired Intelligent Systems and the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China Department of Rehabilitation Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

In light of global aging and prevalent stroke-related hemiplegia, this study addresses challenges in robot-assisted Sit-to-Stand (STS) movements, a daily activity prone to falls. Supernumerary Robotic Legs (SRL) serve as independent support, enhancing stability and limb movement range. Existing coordination control methods lack personalization for STS assistance, requiring solutions for human intent transmission and rapidly optimize coordination control challenges in the non-coupled human-robot system. The proposed human-SRL coordination control algorithm, grounded in personalized SRL-human coupling models, incorporates surface electromyography (sEMG) signals to design an intent-driven variable stiffness impedance control. The inclusion of incremental learning enables rapid optimization of impedance parameters, facilitating real-time adjustments in SRL assistance for adaptive coupling with users. Practical experiments involving both healthy participants and hemiparetic patients validate the algorithm’s effectiveness during STS. The results validate substantial reductions in STS time (39.54%) and muscle activity (28.01%), highlighting the efficacy of the proposed algorithm-controlled SRL support for hemiparetic individuals. Note to Practitioners—To prevent misalignment between the SRL and natural limb movements, which can impose additional strain on the body, this study combines the principles of joint bio-mimicry to develop a personalized human-robot coupling model, and also propose an sEMG-based adaptive impedance control algorithm. Our research fills a gap in the nuanced analysis of robot-assisted support effects on both the affected and unaffected sides of hemiparetic patients, while also possessing the potential for transfer or extension to other types of rehabilitation robotic platforms. Although the SRL shows promising results, it is important to note the limitations of a small sample size and the need for further exploration of the needs of diverse patient po

关键词： Robots Robot kinematics Limbs Impedance Legged locomotion Adaptation models Couplings Muscles Biological system modeling Automation

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DDoS Attack Detection for Cloud control System Based on BiLSTM Algorithm

DDoS Attack Detection for Cloud Control System Based on BiLS...

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2022 Chinese Automation Congress, CAC 2022

作者： Xu, Shengliang Zheng, Song Fuzhou University College of Electrical Engineering and Automation Fuzhou350116 China Fuzhou University of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou350116 China

ISBN: (纸本)9781665465335

The huge advantages of cloud computing technology and the bottlenecks in the development of traditional network control systems have prompted the birth of cloud control systems to address the shortcomings of traditional network control systems in terms of bandwidth and performance. However, the information security issues faced by cloud control systems are more complex, and distributed denial-of-service (DDoS) attacks are a typical class of attacks that may lead to problems such as latency in cloud control systems and seriously affect the performance of cloud control systems. In this paper, we build a single-capacity water tank cloud control semi-physical simulation system with heterogeneous controllers and propose a DDoS attack detection method for cloud control systems based on bidirectional long short-term memory neural network (BiLSTM), study the impact of DDoS attacks on cloud control systems. The experimental results show that the BiLSTM algorithm can effectively detect the DDoS attack on the cloud control system. © 2022 IEEE.

关键词： Denial-of-service attack

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Yue, Pengyu Xin, Jing Mao, Zhijie Lu, Yongchang Zhang, Youmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China Industrial & Aerospace Engineering Concordia University Department of Mechanical MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350316308

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron. © 2023 IEEE.

关键词： Antennas

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Knowledge-Aided Covariance Matrix Estimation Algorithm Based on Geometric Mean in STAP for Airborne Radar

Knowledge-Aided Covariance Matrix Estimation Algorithm Based...

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IET International Radar Conference 2023, IRC 2023

作者： Jing, Yang Du, Xiaolin Cui, Guolong Zheng, Jibin Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781839539954

The problem of knowledge-aided (KA) covariance matrix estimation in airborne radar space-time adaptive processing (STAP) is studied in this paper. In the traditional KA covariance matrix estimation methods, the insufficient number of available samples and inaccurate prior knowledge can cause serious deterioration of STAP performance. To tackle this problem, a new KA covariance matrix estimation algorithm based on geometric mean is proposed. Specifically, by utilizing the persymmetric structure information as well as the geometric mean in the space of positive definite matrices, a geometric mean estimator is constructed, which is then linearly combined with the prior covariance matrix to obtain a new type of CCM estimators. Simulation results illustrate that the proposed algorithm has good STAP performance with small samples and inaccurate prior knowledge. © The Institution of Engineering & Technology 2023.

关键词： Covariance matrix

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Research on Optimization of Two-Stage Scheduling for Aluminum Extrusion Machine Manufacturing Line

Research on Optimization of Two-Stage Scheduling for Aluminu...

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Chinese Automation Congress (CAC)

作者： Aoran Zhou Lipeng Qiu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In view of the problems of high energy consumption, long inventory time and low production efficiency in the production process of traditional aluminum extrusion machine, this paper establishes an efficient two-stage parallel scheduling optimization model based on the integration of flexible job shop scheduling and assembly sequence planning (IFJSS-ASP). On this basis, an evolutionary algorithm combining strong dominance relationship and simple grouping method (SDREA-SGM) is proposed. The algorithm utilizes a two-stage hybrid chromosome encoding and decoding method to establish a rational mapping from the decision space to the solution space. Furthermore, in order to prevent the model from easily falling into local optima, an adaptive intra-class and inter-class pairing selection strategy has been adopted by the algorithm. This enhances global search and improves local search flexibility. Finally, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales.

关键词： Energy consumption Job shop scheduling Optimization models Aluminum Production Evolutionary computation Encoding Planning Decoding Optimization

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