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2013 International Conference for Smart Health, ICSH 2013

作者： Zhu, Zhaonan Cao, Zhidong Zeng, Daniel Dajun State Key Lab. of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642398438

Previous epidemiological researches have studied HFMD transmission pattern, but the study on the patients mobility pattern while seeking treatment is absent. In this paper, we present a statistical analysis of the spatialtemporal pattern of the Beijing HFMD patients mobility based on a complete works dataset containing over 40,000 cases in 2010. The main findings are as follows: (1) the patents incline to take their sick children to hospitals on weekdays, especially on Monday;(2) the patients living far from city center are more likely to get to hospitals in the afternoon, while the patients in the downtown in the morning;(3) patients (or their parents) either select the nearby hospitals, or the Class-A hospitals in the downtown. Furthermore, we employ a gravity model to describe the spatial mobility pattern of patients. The experiment results show a good fit. © 2013 Springer-Verlag.

关键词： Hospitals

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A new search strategy of radio fingerprint matching method in wireless sensor network

A new search strategy of radio fingerprint matching method i...

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1st International Conference on Robot, Vision and Signal Processing, RVSP 2011

作者： Li, Hongchun Zhao, Xiaoguang Tan, Min Institute of Automation Chinese Academy of Sciences State Key Lab. of Intelligent Control and Management of Complex Systems Beijing China

ISBN: (纸本)9780769545813

Radio fingerprint matching localization method promises high localization accuracy but requires extensive infrastructural effort and search operations. This paper proposes a new search strategy for radio fingerprint matching method, which sharply reduces search operations and shows little effect on localization accuracy. Indoor experiment is conducted to evaluate our method. © 2011 IEEE.

关键词： Wireless sensor networks

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LLM Fine-Tuning: Concepts, Opportunities, and Challenges

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BIG DATA AND COGNITIVE COMPUTING 2025年第4期9卷 87-87页

作者： Wu, Xiao-Kun Chen, Min Li, Wanyi Wang, Rui Lu, Limeng Liu, Jia Hwang, Kai Hao, Yixue Pan, Yanru Meng, Qingguo Huang, Kaibin Hu, Long Guizani, Mohsen Chao, Naipeng Fortino, Giancarlo Lin, Fei Tian, Yonglin Niyato, Dusit Wang, Fei-Yue Renmin Univ China Sch Journalism & Commun Beijing 100872 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Journalism & Commun Guangzhou 510006 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China Tsinghua Univ Sch Publ Policy & Management Beijing 100084 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong 999077 Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Machine Learning Dept POB 131818 Abu Dhabi U Arab Emirates Shenzhen Univ Sch Media & Commun Shenzhen 518060 Peoples R China Univ Calabria Dept Informat Modeling Elect & Syst I-87036 Arcavacata Di Rende Italy Macao Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

As a foundation of large language models, fine-tuning drives rapid progress, broad applicability, and profound impacts on human-AI collaboration, surpassing earlier technological advancements. This paper provides a comprehensive overview of large language model (LLM) fine-tuning by integrating hermeneutic theories of human comprehension, with a focus on the essential cognitive conditions that underpin this process. Drawing on Gadamer's concepts of Vorverst & auml;ndnis, Distanciation, and the Hermeneutic Circle, the paper explores how LLM fine-tuning evolves from initial learning to deeper comprehension, ultimately advancing toward self-awareness. It examines the core principles, development, and applications of fine-tuning techniques, emphasizing its growing significance across diverse field and industries. The paper introduces a new term, "Tutorial Fine-Tuning (TFT)", which annotates a process of intensive tuition given by a "tutor" to a small number of "students", to define the latest round of LLM fine-tuning advancements. By addressing key challenges associated with fine-tuning, including ensuring adaptability, precision, credibility and reliability, this paper explores potential future directions for the co-evolution of humans and AI. By bridging theoretical perspectives with practical implications, this work provides valuable insights into the ongoing development of LLMs, emphasizing their potential to achieve higher levels of cognitive and operational intelligence.

关键词： large language models (LLM) fine-tuning hermeneutics comprehension tutorial fine-tuning human-AI co-evolution

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of Automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs

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INFORMATION SCIENCES 2013年 220卷 331-342页

作者： Liu, Derong Wang, Ding Yang, Xiong Chinese Acad Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, the adaptive dynamic programming (ADP) approach is employed for designing an optimal controller of unknown discrete-time nonlinear systems with control constraints. A neural network is constructed for identifying the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Two other neural networks are introduced for approximating the cost function and its derivatives and the control law, under the framework of globalized dual heuristic programming technique. Furthermore, two simulation examples are included to verify the theoretical results. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Adaptive dynamic programming Approximate dynamic programming control constraints Globalized dual heuristic programming Neural networks Optimal control

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Budget-constrained bidding in the sponsored search

Budget-constrained bidding in the sponsored search

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21st Workshop on Information Technologies and systems, WITS 2011

作者： Li, Xiarong Zeng, Daniel Dajun Yang, Yanwu State Key Lab. of Intelligent Control and Management of Complex Systems Chinese Academy of Sciences China Management Information Systems Department University of Arizona United States

In this paper, we study the budget-constrained bidding problems in sponsored search. Our findings illustrate that, compared to budget-irrespective bidding, advertisers can obtain equal even better return-on-investment... 详细信息

关键词： Budget control

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Relaying Strategy Based on Estimated Information for Multi-Antenna Cooperative Networks

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China Communications 2017年第8期14卷 157-165页

作者： Shuangshuang Han Peng Zhang Feijin Shi Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijingChina and Qingdao Academy of Intelligent Industries School of Computer Engineering Weifang University Communications Headquarters Ministry of Foreign Affairs of the People's Republic of China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina and Research Center of Computational Experiments and Parallel SystemsThe National University of Defense Technology

A sphere-based list forwarding scheme for multiple-input multiple-output(MIMO) relay networks is proposed and analyzed. Firstly, an estimate forwarding(EF) method is proposed, which forwards the minimum mean squared error(MMSE) estimate of the source data to the destination. Since it performs like amplify-and-forward(AF) and decode-and-forward(DF) for the low and high signal-to-noise ratio(SNR) regions, respectively, the EF relay thus outperforms conventional AF and DF across all SNRs without the need for switching algorithms for different SNRs. Because computational complexity is however high for relays with a large number of antennas(large MIMO) and/or high order constellations, list EF for large MIMO relay networks is proposed. It computes a list sphere decoder based MMSE estimate and retains the advantages of the exact EF relay at a negligible performance loss. The proposed list EF could offer a flexible trade-off between the performance and computational complexity.

关键词： MIMO system cooperative network soft information

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Seeking Consensus in Networks of Linear Agents: Communication Noises and Markovian Switching Topologies

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IEEE TRANSACTIONS ON AUTOMATIC control 2015年第5期60卷 1374-1379页

作者： Wang, Yunpeng Cheng, Long Ren, Wei Hou, Zeng-Guang Tan, Min Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

The stochastic consensus problem of linear multi-input multi-output (MIMO) multi-agent systems (MASs) with communication noises and Markovian switching topologies is studied in this technical note. The agent's full state is first estimated by the state observer, and then the estimated state is exchanged with neighbor agents through a noisy communication environment. The communication topology is randomly switching and the switching law is described by a continuous-time Markovian chain. Then a consensus protocol is proposed for this MAS, and some sufficient conditions are obtained for ensuring the mean square and almost sure consensus. In addition, if the communication topology is fixed, some necessary and sufficient conditions for the mean square consensus can be obtained according to whether or not each agent in the system has parents.

关键词： Communication noise linear multi-agent system Markovian switching topology stochastic consensus

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Parallel Training: An ACP-Based Training Framework for Iterative Learning in Uncertain Driving Spaces

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2832-2841页

作者： Wang, Jiangong Wang, Xiao Tian, Yonglin Wang, Yutong Niu, Jinglong Kwan, Oliver Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Anhui Univ Sch Artificial Intelligence State Key Lab Management & Control Complex Syst Hefei 230031 Peoples R China Chinese Acad Sci Qingdao Acad Intelligent Ind State Key Lab Management & Control Complex Syst Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Acad Intelligent Ind Qingdao 266109 Peoples R China China North Ind North Automatic Control Technol Re Taiyuan 030006 Peoples R China Motion G Inc Singapore 068898 Singapore

The traffic environment and driving behaviors are of great complexity and uncertainty in our physical world. Therefore, training in the digital world with low cost and diverse complexities become popular for autonomous driving in recent years. However, the current training methods tend to be limited to static data sets and deterministic models that do not sufficiently take into account the uncertainty and diversity prevalent in real traffic scenarios. These approaches also limit more possibilities for the comprehensive development and optimization of vision systems. In this paper, we develop a parallel training method based on artificial systems, computational experiments, and parallel execution (ACP) for the intelligent optimization and learning of the aforementioned agents in uncertain driving spaces. Parallel training creates a virtual driving space following the instruction of the ACP approach and conducts large-scale rehearsal experiments for possible scenarios. By enhancing the diversity of virtual scenarios, intelligent vehicles are trained to respond and adapt to the diverse uncertainties in the physical real-world driving space. Specifically, parallel training first proposes a standard operating procedure for intelligent driving systems, namely the projection-emergence-convergence-operation (PECO) loop. Digital quadruplets for parallel training, i.e., physical, descriptive, predictive, and prescriptive coaches, are also proposed. With the guidance of parallel training, virtual and real-world driving spaces are set up in parallel and interact frequently. They are closely linked and unified in opposition to each other, ultimately building a parallel driving system that fulfills safety, security, sustainability, sensitivity, service, and smartness (6S).

关键词： Training Digital twins Testing Task analysis Intelligent agents complex systems Uncertainty Parallel intelligence parallel driving digital quadruplets parallel learning digital twins metaverses automated driving knowledge transfer

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Separate control Strategy for a Biomimetic Gliding Robotic Fish

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 2535-2544页

作者： Dong, Huijie Wu, Zhengxing Zhang, Pengfei Wang, Jian Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Dept Adv Mfg & Robot Coll Engn State Key Lab Turbulence & Complex SystBIC ESAT Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this article, we present a novel separate strategy to control the pitch attitude and the gliding direction for a biomimetic gliding robotic fish by the coordination of the external and internal control surfaces. First, we derive the gliding dynamics and hydrodynamics within the framework of the extended angle of attack (AOA), based on which the gliding model is decomposed into pitch and velocity terms. Next, the backstepping and model predictive controllers are designed to regulate pitch angle using a movable mass and an AOA using pectoral fins based on their control features, respectively. Further, extensive simulations encompassing separate control and path following with desired pitch attitude are conducted to verify the feasibility and superiority of the proposed control strategy. More importantly, to capture the real-time gliding states in practical environment, we develop a gliding measurement and control system. Through the system, the aquatic experiments are carried out to further verify the effectiveness of the proposed separate control strategy. The obtained results offer valuable insight into the development of complex motion control of the gliding robots, laying a solid foundation for diversified underwater missions besides visual perception and autonomous docking.

关键词： Biomimetic robot gliding robotic fish motion control pitch control underwater robotics

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