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2024 China Automation Congress, CAC 2024

作者： Zhou, Aoran Qiu, Lipeng Wu, Yali Department of Information and Control Engineering Xi'an University of Technology Shaan'xi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

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 ***, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales. © 2024 IEEE.

关键词： Job shop scheduling

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A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

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Machine Intelligence Research 2022年第4期19卷 331-346页

作者： Song Zhang Nan Zheng Dan-Li Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Due to the popularity of group activities in social media,group recommendation becomes increasingly *** aims to pursue a list of preferred items for a target *** deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and ***,these methods may suffer from data sparsity *** for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and *** interactions,which take place in the range of a group,form a local view of a certain *** addition to local information,groups with common interests may also show similar tastes on ***,group representation can be conducted according to the similarity among groups,which forms a global view of a certain *** this paper,we propose a novel global and local information fusion neural network(GLIF)model for group *** GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local ***,our model also leverages ANN to obtain a group′s global representation based on the similarity among different ***,it fuses global and local representations based on attention mechanism to form a group′s comprehensive ***,group recommendation is conducted under neural collaborative filtering(NCF)*** experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Group recommendation attentive neural network(ANN) global information local information recommender system

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The Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting 9th

The Prospects of Multi-modal Pre-trained Models in Epidemi...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Fei, Jiaqiang Zhao, Pengfei Luo, Tianyi Wang, Jiaojiao Cao, Zhidong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819758029

Accurate epidemic forecasting is imperative in light of the increasing global threat posed by epidemic diseases. Presently, researchers predominantly approach this challenge as a time-series prediction task and have proposed a variety of effective methodologies. However, two critical issues deserve thorough consideration. Firstly, the transmission of infectious diseases is a profoundly intricate process influenced by a multitude of factors, encompassing complex dynamics and propagation mechanisms. In contrast to the multifaceted nature of real-world scenarios, prevailing modeling approaches often exhibit undue simplification, resulting in imprecision and a deficit of interpretability. Secondly, existing methodologies excessively rely on data and require substantial adaptation for new infectious disease scenarios. The endeavor to conduct rapid and precise simulations and predictions during the early stages of transmission is challenging due to limited data availability. Thus, we aim to explore the feasibility and effective strategies of leveraging multi-modal pre-trained technology for epidemic forecasting. This approach offers the potential to comprehensively grasp the transmission process from multi-view perspectives and acquire richer representations by integrating multi-modal data. Moreover, through pre-trained, this methodology captures overarching patterns and principles of infectious disease transmission, facilitating swift and accurate predictions via fine-tuning, particularly in scenarios of sparse early-stage transmission data. We discuss the prospects of multi-modal pre-trained models in epidemic forecasting and provide feasible strategies for constructing the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Requirement-Oriented TT&C Method for Satellite Based on BDS-3 Short-Message Communication system

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Space(Science & Technology) 2023年第1期3卷 311-324页

作者： Chao Li Peijie Liu Shiyuan Fu Yiwen Jiao Wei Zhang Department of Electronic and Optical Engineering Space Engineering UniversityBeijingChina Key Laboratory of Intelligent Space TT&C and Operation(Space Engineering University) Ministry of EducationBeijingChina State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System LuoyangChina Xi’an Satellite Control Center Xi’anChina

With the increasing number of satellites being launched in China,the demand for Telemetry,Tracking,and Command(TT&C)resources is also *** of the global coverage and resource requirements of TT&C management,this paper proposes a TT&C scheme based on the Global Short-Message Communication system(GSMCS)of the BeiDou-3 Navigation Satellite system(BDS-3).The system composition,workflow,and functions of satellite TT&C are analyzed using the GSMCS of *** the coverage analysis of the Medium Earth Orbit(MEO)constellation of BDS-3,scheme’s applicability is pointed out,and the correctness of the analysis results is verified via *** value range of the link distance is derived by calculating the range of the distance variation between any 2 satellites in the BDS-3 MEO constellation and combining the intersatellite routing *** calculation results show that the response time range of the autonomous request management application of in-orbit satellites based on the GSMCS of BDS-3 is 1.407 to 2.448 *** can meet the requirements of TT&C service.

关键词： Message Command service

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New Energy Consumption Capacity Evaluation Method for Isolated Industrial Park Considering Flexible Load Regulation Ability

New Energy Consumption Capacity Evaluation Method for Isolat...

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2024 IEEE IAS Industrial and Commercial Power system Asia, I and CPS Asia 2024

作者： Li, Nan Xu, Hui Tan, Zhenlong Dai, Haonan Zheng, Zhibao Hao, Ling Xu, Fei Chen, Lei Tong, Zihao Tsinghua University State Key Laboratory of Power System Operation and Control Beijing China Co. Ltd. Beijing China Tsinghua University School of Economics and Management Beijing China

ISBN: (纸本)9798350352290

In the context of the global energy transition, China is promoting the synergistic integration of energy sources, grids, loads and storage through national strategic policies. This paper examines the challenges of integrating volatile new energy sources, such as wind and solar, into the grids of isolated industrial parks (IIPs). It proposes a model to assess and optimize the consumption capacity of these resources to balance energy production with grid demand effectively. Initially, a 'resource-power' conversion model is developed to simulate the potential output of wind and solar installations, given the absence of direct connectivity to the grid in newly established IIPs. Then, with the optimization objective of minimizing the overall cost of isolated grid operation, the new energy consumption capacity of IIPs is evaluated, taking into account the actual operating constraints of thermal power units, the maximum new energy consumption that can be technically achieved, and the regulation potential of flexible load participation response to mitigate the impacts caused by fluctuations in new energy sources. Finally, based on the analysis of the actual operation data of the IIPs, the simulation results show that the IIP can be guided by adjusting the installed capacity parameters for planning the installed size of wind and solar in the IIP before connecting to wind and PV. Moreover, fully exploiting flexible load regulation ability can effectively improve the IIP's wind power consumption capacity, which can provide theoretical guidance for optimizing the IIP's scheduling and promoting new energy consumption. © 2024 IEEE.

关键词： Clean energy

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Brain-inspired Intelligent Robotics:Theoretical Analysis and systematic Application

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Machine Intelligence Research 2023年第1期20卷 1-18页

作者： Hong Qiao Ya-Xiong Wu Shan-Lin Zhong Pei-Jie Yin Jia-Hao Chen The State Key Laboratory of Management and Control for Complex System Institute of AutomationChineseAcademyof SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Chinese Academy of Science Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China

Traditional joint-link robots have been widely used in production lines because of their high precision for single *** the development of the manufacturing and service industries,the requirement for the comprehensive performance of robotics is *** types of bio-inspired robotics have been investigated to realize human-like motion control and manipulation.A study route from inner mechanisms to external structures is proposed to imitate humans and animals *** this idea,a brain-inspired intelligent robotic system is constructed that contains visual cognition,decision-making,motion control,and musculoskeletal *** paper reviews cutting-edge research in brain-inspired visual cognition,decision-making,motion control,and musculoskeletal *** software systems and a corresponding hardware system are established,aiming at the verification and applications of next-generationbrain-inspired musculoskeletal robots.

关键词： Brain-inspired intelligent robot software and hardware decision making muscle control cognitive intelligence.

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Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

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Journal of Field Robotics 2025年第0期

作者： Hai, Huang Tao, Jiang Xinyu, Bian Hao, Zhou Xu, Yang Gang, Wang Hongde, Qin Xinyue, Han National Key Laboratory of Science and Technology on Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Bei Jing China

Underwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross-domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region-Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real-world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments. © 2025 Wiley Periodicals LLC.

关键词： Health risks

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A Grey Wolf Optimizer Variant with Weighted-leader Strategy 24

A Grey Wolf Optimizer Variant with Weighted-leader Strategy

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liang, Yajun Yan, Su Ma, Qian Huang, Shunlun Liu, Xin China Aerodynamics Research & Development Center Sichuan Mianyang China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing China State Key Laboratory of Air Traffic Management System Beijing China Beijing Aviation Meteorological Institute Beijing China

ISBN: (纸本)9798400710834

To enhance the convergence capability of grey wolf optimizer (GWO), this research investigates an evolved GWO using weighted-leader strategy (WLS), namely WLSGWO. The key issue of WLS is realizing the adaptive adjustment of position weighting according to the control parameters, thus well overcoming the drawback of premature convergence in the basic GWO. The newly developed method’s performance is assessed through experiments conducted on a standardized test suite. Additionally, comparisons are made by utilizing GWO alongside other advanced optimization techniques. The results of non-parametric studies validate the outperformance of the WLSGWO, thus proving the efficiency of the proposed strategy. © 2024 Copyright held by the owner/author(s).

关键词： Swarm intelligence

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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