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Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

Chinese Physics B 2023年第10期32卷 229-240页

作者：曲英杰陈钊王伟杰马鸿洋 School of Sciences Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

Quantum error correction technology is an important method to eliminate errors during the operation of quantum *** order to solve the problem of influence of errors on physical qubits,we propose an approximate error correction scheme that performs dimension mapping operations on surface *** error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three *** to previous error correction schemes,the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction *** reducing the number of ancilla qubits required for error correction,this approach achieves savings in measurement space and reduces resource consumption *** order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping,we employ a reinforcement learning(RL)decoder based on deep Q-learning,which enables faster identification of the optimal syndrome and achieves better thresholds through conditional *** to the minimum weight perfect matching decoding,the threshold of the RL trained model reaches 0.78%,which is 56%higher and enables large-scale fault-tolerant quantum computation.

关键词： fault-tolerant quantum computing surface code approximate error correction reinforcement learning

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Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

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Computers, Materials & Continua 2024年第6期79卷 5051-5066页

作者： Yuanyao Lu Wei Chen Zhanhe Yu Jingxuan Wang Chaochao Yang School of Information Science and Technology North China University of TechnologyBeijing100144China School of Electrical and Control Engineering North China University of TechnologyBeijing100144China

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing *** to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external *** research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition *** address this,this research has developed a new model for recognizing abnormal traffic *** model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature *** approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network ***,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at *** temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data *** research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test *** results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

关键词： Vehicle abnormal behavior deep learning ResNet dense block soft thresholding

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Multiple unmanned ship coverage and exploration in complex sea areas

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Autonomous Intelligent Systems 2024年第1期4卷 210-223页

作者： Feifei Chen Qingyun Yu Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai200092China Shanghai Municipal Economic and Information Technology Commission Shanghai200125China

This study addresses the complexities of maritime area information collection,particularly in challenging sea environments,by introducing a multi-agent control model for regional information *** on three key areas—regional coverage,collaborative exploration,and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection *** regional coverage,a multi-objective optimization model considering effective area coverage and time efficiency is proposed,utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning,achieving a 99.67%effective coverage rate in *** exploration is tackled through a comprehensive optimization model,solved using an improved greedy strategy,resulting in a 100%static target detection and correct detection *** obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm,ensuring autonomous obstacle avoidance without communication or *** confirm zero collaborative *** research offers practical solutions for multi-agent exploration and coverage in unknown sea areas,balancing workload and time efficiency while considering ship dynamics constraints.

关键词： Multi-agent control model Regional coverage Collaborative exploration Agent obstacle avoidance

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Broad-Learning-System-Based Model-Free Adaptive Predictive control for Nonlinear MASs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 381-393页

作者： Hongxing Xiong Guangdeng Chen Hongru Ren Hongyi Li the School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology the College of Electronic and Information Engineering Southwest University IEEE

In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.

关键词： Broad learning technique denial-of-service (DoS) model-free adaptive predictive control (MFAPC) nonlinear multi-agent systems (NMASs)

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Decoupling control of Six-Pole Axial-Radial Active Magnetic Bearing Based on Improved Linear Active Disturbance Rejection Optimized by Least Square Support Vector Machine

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Progress in Electromagnetics Research B 2024年 106卷 73-84页

作者： Wang, Zhen Liu, Gai Ju, Jintao Zhu, Huangqiu School of Electrical and Information Engineering Jiangsu University Zhenjiang212013 China School of Electrical and Control Engineering Xuzhou University of Technology Xuzhou221018 China School of Electrical and Information Engineering Changzhou Institute of Technology Changzhou213032 China

To improve the coupling problem between radial degrees of freedom in six-pole axialradial active magnetic (AR-AMB), a decoupling control method based on an improved linear active disturbance rejection decoupling control strategy optimized by the least square support vector machine (LSSVM-ILADRC) is proposed. Firstly, the structure and working principle of the six-pole AR-AMB are introduced, and the mathematical model of suspension force is derived. Secondly, cascaded linear extended state observers (LESOs) are used to estimate the disturbance in degrees of freedom step by step, with LESO1 providing an initial estimate of the total disturbance, and LESO2 estimating and compensating for the difference between the initial estimate and the actual disturbance. The regression prediction function of LSSVM is employed to enhance the response speed and estimation accuracy of the LESO to the disturbance. Finally, the simulation and experimental research show that the proposed LSSVM-ILADRC decoupling control method has better decoupling performance and anti-interference performance than the ILADRC decoupling control method. © (2024), Electromagnetics Academy. All rights reserved.

关键词： Support vector machines

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Incremental Weighted Ensemble for Data Streams With Concept Drift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 92-103页

作者： Jiao, Botao Guo, Yinan Yang, Cuie Pu, Jiayang Zheng, Zhiji Gong, Dunwei School of Electromechanical and Information Engineering Beijing100083 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Victoria University of Wellington School of Engineering and Computer Wellington6140 New Zealand Electromechanical and Information Engineering Beijing100083 China Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China

As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical classifier based on an entire chunk newly arrived, which may cause delayed adaptation. To address the issue, two novel ensemble models, named incremental weighted ensemble (IWE) and incremental weighted ensemble for multi-classification (IWE-M), are proposed. At each time step, all base classifiers are incrementally updated on a newly arrived instance. Following that, the instance is collected into a cache array. Once a data chunk is formed, a new base classifier is created. More specially, a forgetting mechanism based on variable-size window is designed to adjust the weight of each base classifier in IWE in terms of its classification accuracy on the latest instances in an online manner. IWE-M, an extension of IWE, aims to solve multiclass problems with local concept drifts. In IWE-M, the weight of a base classifier is expanded to a weight vector. In this way, this ensemble model can retain specific historical information about nondrift regions from a local drift. Experimental results show that the proposed ensemble frameworks outperform six competitive approaches on accuracy and G-mean. © 2020 IEEE.

关键词： Classifiers

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Latency minimization for multiuser computation offloading in fog-radio access networks

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Digital Communications and Networks 2025年第1期11卷 160-171页

作者： Wei Zhang Shafei Wang Ye Pan Qiang Li Jingran Lin Xiaoxiao Wu School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089China

Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user *** this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog *** UE can select one of the fog nodes to offload its task,and each fog node may serve multiple *** tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul *** order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all *** min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a *** MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM *** addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the *** this model,a similar min-max latency optimization problem is formulated and tackled by the inexact *** results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.

关键词： Fog-radio access network Fog computing Majorization minimization WMMSE

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Anti-Interference High-Speed Modulation Decoder for Quantum Key Distribution

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Chinese Physics Letters 2025年第1期42卷 34-39页

作者： Hua-Xing Xu Shao-Hua Wang Chang-Lei Wang Ping Zhang National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academic of Electronics and Information TechnologyBeijing 100041China CETC Academy of Electronics and Information Technology Group Co. Ltd.Beijing 100041China

Quantum key distribution is increasingly transitioning toward network applications,necessitating advancements in system performance,including photonic integration for compact designs,enhanced stability against environmental disturbances,higher key rates,and improved *** this letter,we propose an orthogonal polarization exchange reflector Michelson interferometer model to address quantum channel disturbances caused by environmental *** on this model,we designed a Sagnac reflector-Michelson interferometer decoder and verified its performance through an interference *** interference fringe visibility exceeded 98%across all four coding phases at 625 *** results indicate that the decoder effectively mitigates environmental interference while supporting high-speed modulation *** addition,the proposed anti-interference decoder,which does not rely on magneto-optical devices,is well-suited for photonic integration,aligning with the development trajectory for next-generation quantum communication devices.

关键词： decoder interferometer polarization

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Proximal Policy Optimization With Advantage Reuse Competition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3915-3925页

作者： Cheng, Yuhu Guo, Qingbang Wang, Xuesong Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou221116 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China

In recent years, reinforcement learning (RL) has made great achievements in artificial intelligence. Proximal policy optimization (PPO) is a representative RL algorithm, which limits the magnitude of each policy update to achieve monotonic policy improvement. However, as an on-policy algorithm, PPO suffers from sample inefficiency and poor policy exploratory. To solve above problems, the off-policy advantage is proposed, which calculates the advantage function through the reuse of previous policy, and the proximal policy optimization with advantage reuse (PPO-AR) is proposed. Furthermore, to improve the sampling efficiency of policy update, the proximal policy optimization with advantage reuse competition (PPO-ARC) is proposed, which introduces PPO-AR into the policy calculation and uses the parallel competitive optimization, and it is shown to improve the performance of policy. Moreover, to improve the exploratory of policy update, the proximal policy optimization with generalized clipping (PPO-GC) is proposed, which relaxes the limits of policy update by changing the policy flat clipping boundary. Experimental results on OpenAI Gym demonstrate the effectiveness of our proposed PPO-ARC and PPO-GC. © 2020 IEEE.

关键词： Reinforcement learning

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