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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Li, Chao Wu, Weimin Institute of Cyber-Systems and Control Zhejiang University The State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9781665472432

Multi-task assignment of multiple Automated Guided Vehicles (AGV) is one of the main processes in an intelligent warehouse. This paper models a dynamic task chain scheduling to improve the energy and time utilization of the system. The charging pile competition prevention mechanism is introduced to solve the problem of unbalanced charging resources allocation in the system. A joint scheduling strategy of charging tasks is proposed to make full use of the information of the destination of the task and shorten the driving distance of AGV. Aiming at the shortcoming of poor local search ability of genetic algorithm, an improved genetic algorithm based on genome is designed. Numerical and simulation experiments show that the proposed algorithm and model perform better than traditional methods. © 2022 IEEE.

关键词： Genetic algorithms

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Research on real-time export container allocation considering loading efficiency 19

Research on real-time export container allocation considerin...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Qian, Jianlin Wu, Weimin Zhu, Yangfei Institute of Cyber-Systems and Control Zhejiang University The State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9781665472432

Determining where to stack the containers on the storage yard of a container terminal is an important problem, because it will make a great importance on the loading efficiency. In order to minimize the container relocation times and the average transportation distance of containers, we propose a penalty based strategy for real-time container allocation. When the sequence of containers arriving at the storage yard is uncertain, this strategy can allocate the location for a single container when it arrives, so as to minimize the container relocation times and transportation distance during shipment as a whole. Experiments show that this strategy can get a relatively good result. © 2022 IEEE.

关键词： Genetic algorithms

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Fast Adaptation Dynamics Model for Robot's Damage Recovery

Fast Adaptation Dynamics Model for Robot's Damage Recovery

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Chen, Ci Wang, Dongqi Yu, Jiyu Xiang, Pingyu Lu, Haojian Wang, Yue Xiong, Rong Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control and Technology Hangzhou310027 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In the process of operating, robots will inevitably encounter damage due to external or internal factors, such as motors blockage. For the legged robot, when the motors of joints are failing, if other motors still act according to the original instructions, it will cause the robot to deviate from the predetermined trajectory, which is unacceptable for legged robots. Inspired by the fact that the model trained by supervised learning on the training set can be generalized to the testing set, our goal is to obtain a dynamic model that can be generalized to all kinds of motor damage situations. It can predict what state will be reached in the next step when an action is applied in the current state. With this dynamics model, we use the Monte Carlo particles to optimize the feasible actions in a model predictive control (MPC) fashion and achieve the expected goal (such as making the robot walk in a straight line). The comparison experiment adopt two meta-learning model and vanilla dynamics model approaches, the results show that the proposed method is superior to the three baselines, which proves the effectiveness of the proposed method. © 2022 IEEE.

关键词： Robots

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On the optimality of uniform velocity-deceleration separation scheme for tethered satellite systems

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control Theory and technology 2024年第4期22卷 503-516页

作者： Peng Yu Ji-Feng Zhang Shuping Tan Jin Guo Yanlong Zhao School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China Beijing Institute of Control Engineering Beijing100094China

For mitigating the libration angle fluctuation of the tethered satellite system,this paper discusses how to make the uniform velocity-deceleration separation scheme achieve the best ***,a judgment condition is established to determine the tether state by comparing the tether length and the relative distance of the sub-satellite and the parent *** on the tethered satellite system dynamics equation and Clohessy-Wiltshire equation,dynamic models are given for four cases of tether ***,the influence of the uniform velocity-deceleration separation scheme on the libration angle is analyzed by taking the libration angle at the separation ending time and the mean absolute value of the libration angle as index ***,the optimality problem of the uniform velocity-deceleration separation scheme is formulated as an optimization problem with constraints,and an approximate solution algorithm is given by combining the back propagation neural network and Newton-Raphson method of multiple initial ***,the effectiveness of the proposed method is verified by a numerical simulation.

关键词： Tethered satellite systems Libration angle Uniform velocity-deceleration separation Neural network

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Knowledge Distillation on Driving Intention Generator: Learn Human-like Semantic Reasoning

Knowledge Distillation on Driving Intention Generator: Learn...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Wang, Hejun Dong, Jiajun Wang, Yunkai Jiao, Yanmei Xiong, Rong Zhejiang University The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China Hangzhou Normal University The School of Information Science and Engineering Hangzhou China

ISBN: (纸本)9798350327182

Semantic information plays a significant role in autonomous vehicles. However, it has remained open how to economically compute and efficiently incorporate semantic information into other frequently applied processes, such as construction of local navigation map, driving decision making and trajectories planning. To address this issue, we introduce Knowledge Distillation to convey semantic reasoning capability. Following the existing intention-guided framework, a semantically incorporated module associates given navigation instruction with acquired visual perception to synthesize driving intention. Our major contribution is to teach the driving intention generator how to propagate considering semantics implicitly. Specifically, we construct a semantic supervisor identical to the driving intention generator. The supervisor transfers semantic reasoning ability to the generator during training process, with which the generator is expected to perform stably and reliably even in extremely sophisticated surroundings or under frequently inaccurate navigation instruction. Through exhaustive experimental validation, our model demonstrates a superior robustness and adaptability compared to the state-of-the-art. Finally, we explain the intrinsic mechanism of our model with visualization. © 2023 IEEE.

关键词： Navigation

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A BERT-Based Named Entity Recognition Method of Warm Disease in Traditional Chinese Medicine 18

A BERT-Based Named Entity Recognition Method of Warm Disease...

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18th IEEE Conference on industrial Electronics and Applications, ICIEA 2023

作者： Song, Zijun Xu, Wen Liu, Zhitao Chen, Liang Su, Hongye Polytechnic Institute Zhejiang University Hangzhou China Binzhou Medical University Yantai China Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9798350312201

Traditional Chinese medicine (TCM) documents have been handed down through the ages, containing rich theoretical knowledge and clinical experience. These unstructured data are the foundation for building the digital knowledge system of TCM. However, written in ancient Chinese, the TCM books have complex grammatical rules and terms which are different from modern medicine, inducing difficulty in entity annotation and recognition. In order to solve the problem of lacking labeled data, we construct a dataset with Wenbing Tiaobian, a classic work of TCM on the warm disease, identify six entities and annotate the book with the BIOES method. The BERT-BILSTM-CRF model is used to conduct experiments on the dataset with an F1 value of 91.4%. The results verify the effectiveness of the model in NER tasks and advance the construction of knowledge graphs in TCM. © 2023 IEEE.

关键词： Deep learning

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Multi-Objective fuzzy flexible jobshop of dynamic scheduling optimization based on hybrid-nondominanted sorting algorithm 19

Multi-Objective fuzzy flexible jobshop of dynamic scheduling...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Shen, Wei Wu, WeiMin Niu, HaoYi Institute of Cyber-Systems and Control Zhejiang University The State Key Laboratory of Industrial Control Technolog Hangzhou China

ISBN: (纸本)9781665472432

Multi-objective flexible job-shop scheduling problem is of great significance to improve the efficiency of the production system. However, most studies lack dynamic scheduling experiments. In this paper, a hybrid-nondominated sorting algorithm based on NSGA-II (Non-dominated Sorting Genetic Algorithm) is proposed to optimize problem considering the influence of transportation time window. Co-evolution PSO (Particles Swarm Optimization) based on Cauchy mutation and weight mapping crossover are used to generate new solutions to improve the ability of local search and global detection. The proposed method has better solution with convergence and distributivity. The chromosome is decoded by a dynamic fuzzy greedy interpolation method based on exact time. The algorithm is applied to famous benchmarks to verify the effectiveness of the proposed method in dynamic scene. © 2022 IEEE.

关键词： Chromosomes

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Command filter-based I&I adaptive control for MIMO uncertain systems with input saturation and disturbances

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Science China(Information Sciences) 2023年第12期66卷 64-79页

作者： Qi HAN Zhitao LIU Hongye SU Xiangbin LIU State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University School of Electronic Information Engineering Beijing Jiaotong University

This paper develops a control strategy based on immersion and invariance(I&I) adaptive methodology for a class of multi-input multi-output(MIMO) systems in the presence of parametric uncertainty, input saturation, and external disturbance. To avoid the analytic calculation in the backstepping process, a highgain auxiliary system is constructed to compensate for the effect of command filter error. The first-order command filters are also employed in the construction procedure of the I&I adaptive law to simplify its design and remove the structural conditions on the regressors. A filter-based disturbance observer is developed to counteract the effect of the external disturbance produced by a partially known exogenous *** overcome the input saturation nonlinearity, a smooth function is introduced to approximate the input saturation with an extended state and a bounding estimation law. Stringent analysis guarantees the stability of closed-loop system. Finally, simulated examples confirm the effectiveness of the suggested method.

关键词： adaptive control command filtered backstepping disturbance observer immersion and invariance MIMO systems

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Deep reinforcement learning for optimal denial-of-service attacks scheduling

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Science China(Information Sciences) 2022年第6期65卷 98-106页

作者： Fangyuan HOU Jian SUN Qiuling YANG Zhonghua PANG State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology

We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.

关键词： optimal denial-of-service attack scheduling optimization limited energy deep reinforcement learning

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