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IEEE International Conference on Unmanned Systems (ICUS)

作者： Lele Zhang Zeyu Yang Caocao Yu Ziniu Hu Yunshui Zhou Qingjia Cui State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Wuxi Intelligent Control Research Institute of Hunan University Wuxi China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

In recent years, reinforcement learning has been employed to address the challenge of unmanned driving decision-making, due to its ability to handle uncertainty and continuous optimization. However, compared with rule-based algorithms, its training time is considerable and the reliability of its results is difficult to guarantee. To enhance the security of decision-making processes, this paper proposes a hybrid decision-making algorithm designed to accelerate the training process of reinforcement learning and enhance the reliability of decision-making. The method first evaluates the confidence levels of both rule-based and learning-based strategies by leveraging the data collected throughout the training phase. Subsequently, an activation function is crafted to determine the final strategy with the greatest level of confidence. This guarantees that the final strategy performs at least on par with the rule-based approach. A simulation focused on highway driving was conducted as a case study to illustrate the effectiveness of the proposed method. The results demonstrate that the proposed approach offers greater profitability than the rule-based method and higher reliability compared to the learning-based method.

关键词： Training Road transportation Learning systems Uncertainty Profitability Decision making Reinforcement learning Reliability engineering Security Optimization

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A Doctor Recommendation Framework for Online Medical Platforms Using Multi-Source Heterogeneous Data 2021

A Doctor Recommendation Framework for Online Medical Platfor...

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7th International Conference on computing and Artificial Intelligence, ICCAI 2021

作者： Che, Mingxuan Zhao, Lasheng Jin, Bo Dalian University Department of Information Engineering China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University China School of Innovation and Entrepreneurship Dalian University of Technology China

ISBN: (纸本)9781450389501

The emergence of the online medical platform provides convenience for patients, but at the same time, how to choose the right doctor among thousands of doctors on the platform has become a problem for patients. Nowadays, most doctor recommendation methods meet computation inefficiency issues as the data amount is extreme huge. In addition, the data on different medical platforms cannot be utilized due to inconsistent structures. To address the above two problems, this paper proposes a framework for doctor recommendation using multi-source heterogeneous data with two models: knowledge fusion model and doctor recommendation model. In the knowledge fusion model, two systems are established for co-evolution to integrate multi-source heterogeneity data. One system maps various types of health data to coding strings for evolutionary calculation;the other uses co-evolution to generate a unified coding string. In the doctor recommendation model, the ranking of doctors under different attributes is used as input to obtain the comprehensive ranking of doctors, the method performs effective to fuse knowledge from all kinds of data. The experimental results on 1157 orthopedic surgeons show that our framework greatly improves the computational efficiency while maintaining the accuracy. © 2021 ACM.

关键词： Computational efficiency

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A Stable RTK/MEMS-IMU Tightly-Coupled Algorithm under Differential Corrections Outage

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IEEE Transactions on Vehicular Technology 2025年

作者： Bian, Yougang Cao, Shipeng Wang, Guangcai Qin, Xiaohui Hu, Manjiang Qin, Hongmao Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University Wuxi Intelligent Control Research Institute Wuxi214115 China

The GNSS-RTK and MEMS-IMU integrated navigation system is a commonly used high-precision, low-cost solution for outdoor positioning. However, the significant measurement error of MEMS-IMU can rapidly lead to divergence in navigation state predictions based on inertial error propagation models. To overcome this limitation and enhance accuracy, maintaining the continuity of RTK measurement information is crucial. Nevertheless, due to the instability of wireless transmission links and communication equipment, or disruptions in base station observations, differential corrections often experience outages, resulting in a decline in measurement information quality and affecting the accuracy of integrated navigation. Therefore, this paper proposes a stable RTK/MEMS-IMU tightly coupled algorithm. During outages of differential corrections, the proposed algorithm utilizes historical differential corrections from the base station along with real-time rover station information to construct a stable double-difference carrier phase vector. This approach ensures the stability of measurement information and enhances positioning accuracy. Simulation results have verified the effectiveness of this method, and land tests have demonstrated that, compared to the traditional RTK/MEMS-IMU tightly coupled algorithm, the position and velocity accuracy of this algorithm during outages of differential corrections have been improved by 29.5% and 18.4%, respectively. © 1967-2012 IEEE.

关键词： Base stations

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Adaptive neural tracking control for nonlinear servo system with unknown time-varying parameters and uncertainties

Adaptive neural tracking control for nonlinear servo system ...

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第42届中国控制会议

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University

This paper solves the neural network(NN) tracking control problem for uncertain nonlinear servo system with timevarying parameters and *** address the uncertain nonlinear function with unknown time-varying parameters(i.e.,unknown nonlinear spatiotemporal function),the time-varying parameter extraction method is used to separate the time-varying puameters from uncertain nonlinear spatiotemporal function,which yields an unknown state-based boundary *** the tools of NN and adaptive technology,an adaptive neural tracking controller is designed,which guarantees the uniformly ultimately bounded(UUB) performance of the resulting closed-loop *** effectiveness of the designed method is verified by simulations.

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Noise Detection and Removal for Underground Communication Systems Based on L2 N onnal Inner Product

Noise Detection and Removal for Underground Communication Sy...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Bangwei Yu Yong Wang Liangang Qi Yang Liu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate(BER). In this paper, a Normal Inner Product Orthogonal Matching Pursuit(NIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, a cyclic prefix(CP) is added in front of the sent data. Then the observation vector is obtained by making a difference between the CP of the latter set of data and the CP of the former set of data. Then, the columns of the sensing matrix that are most correlated with the observation vectors are selected as candidate support sets by calculating the L2 norm. The least squares method was used to solve for the previous set of pump noise estimates. Finally, the pump noise is reconstructed using the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We discuss the noise characteristics of pump noise and analyze the denoising performance of the NIPOMP algorithm in depth. Simulation results show that the algorithm significantly reduces the system BER in complex channel environments with more drastic fading, including gas-phase, liquid-phase, gas-liquid two-phase, and liquid-solid two-channel environments.

关键词： Liquids Communication systems Simulation Oils Noise Noise reduction Matching pursuit algorithms Vectors Sensors Signal to noise ratio

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Trajectory Optimization and Control Method Based on Multistage Nonlinear Model Predictive Control 4

Trajectory Optimization and Control Method Based on Multista...

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4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023

作者： Zheng, Qingchun Peng, Zhi Zhu, Peihao Zhai, Ran Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China

ISBN: (纸本)9798350314526

To improve the trajectory optimization efficiency of robot manipulators, this paper proposes a novel approach for trajectory optimization and control of manipulators based on multistage nonlinear model predictive control (msNLMPC). This approach is realized by using a neural state space model as the prediction model of nonlinear model predictive control (NLMPC). The simulation results show that the actual trajectory of the manipulator in our proposed scheme coincides with the desired trajectory. The manipulator achieves the control task of following the optimal trajectory. Our proposed method improves the trajectory optimization efficiency of the robot manipulator. © 2023 IEEE.

关键词： Model predictive control

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Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning

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Optimal design for the Torsional Elastic Element Topology of a Rotary Series Elastic Actuator 7

Optimal Design for the Torsional Elastic Element Topology of...

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7th Asia-Pacific Conference on intelligent Robot Systems, ACIRS 2022

作者： Yang, Xishun Luo, Wenbo Liu, Haokun Zhang, Sen Ge, Weimin Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control National Demonstration Center for Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering Tianjin300384 China

ISBN: (数字)9781665485197

ISBN: (纸本)9781665485197

The series elastic actuator improves the compliance of exoskeleton by introducing a elastic element and ensures its safe interaction ability. Therefore, it is regarded as a new driving mechanism by scholars. Suitable elastic element is conducive to realize the miniaturization and lightweight of SEA, but its topology design is complex and lack of guidance. Based on the finite element simulation environment and three-dimensional parametric CAD environment, this paper analyzes the influence of different size parameters and topology on the pressure of the elastic element, summarizes the characteristics of three topology structures, and puts forward the optimal design method of a torsional elastic element topology. Through the research, we can conclude that the compression effect of elastic beam with specific shape is better than that of curved elastic beam, and the width of elastic beam has a great influence on the torsional elastic element. © 2022 IEEE.

关键词： Finite element method

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks 13

A Heuristic Sparsification Method to Improve the Performance...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Zhang, Fangyu Wei, Yanjie Lin, Zhenli Pan, Yi Liu, Jin Centre for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350313925

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced. © 2023 IEEE.

关键词： Topology

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Impulsive Control of Heterogeneous Multi-agent Systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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Chinese Automation Congress (CAC)

作者： Ranran Jiao Bo Li Xiangyong Chen Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol.

关键词： Energy consumption Protocols Automation Event detection Delay effects Control systems Delays Multi-agent systems

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