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Generalization of integral inequalities and (c_1,c_1) stability of neutral differential equations with time-varying delays

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Journal of systems Engineering and Electronics 2017年第2期28卷 347-360页

作者： Shuli Guo Lina Han Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Mathematics Institute Oxford University Department of Cardiovascular Internal Medicine Nanlou Branch of Chinese PLA General Hospital

A uniform stability analysis is developed for a type of neutral delays differential equations which depend on more general nonlinear integral inequalities. Many original investigations and results are obtained. Firstly, generations of two integral nonlinear inequalities are presented, which are very effective in dealing with the complicated integro-differential inequalities whose variable exponents are greater than zero. Compared with existed integral inequalities, those proposed here can be applied to more complicated differential equations, such as time-varying delay neutral differential equations. Secondly, the notions of (ω, Ω) uniform stable and (ω, Ω) uniform asymptotically stable, especially (c1, c1) uniform stable and (c1, c1) uniform asymptotically stable, are presented. Sufficient conditions on about (c1, c1) uniform stable and (c1, c1) uniform asymptotically stable of time-varying delay neutral differential equations are established by the proposed integral inequalities. Finally, a complex numerical example is presented to illustrate the main results effectively. The above work allows to provide further applications on the proposed stability analysis and control system design for different nonlinear systems. © 2017 Beijing Institute of Aerospace Information.

关键词： Control system stability Differential equations Nonlinear equations Time delay Time varying control systems

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Magnetic Modulation of Terahertz Waves via Spin-Polarized Electron Tunneling Based on Magnetic Tunnel Junctions

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Physical Review Applied 2020年第1期14卷 014032-014032页

作者： Zuanming Jin Jugeng Li Wenjie Zhang Chenyang Guo Caihua Wan Xiufeng Han Zhenxiang Cheng Chao Zhang Alexey V. Balakin Alexander P. Shkurinov Yan Peng Guohong Ma Yiming Zhu Jianquan Yao Songlin Zhuang Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology 516 JunGong Road Shanghai 200093 China Department of Physics Shanghai University 99 Shangda Road Shanghai 200444 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing 100190 China Institute for Superconducting and Electronic Materials University of Wollongong Wollongong New South Wales 2500 Australia School of Physics University of Wollongong Wollongong New South Wales 2522 Australia Department of Physics and International Laser Center Lomonosov Moscow State University Leninskie Gory 1 Moscow 19991 Russia STU & SIOM Joint Laboratory for Superintense Lasers and Applications Shanghai 201210 China College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin 300110 China

Magnetic tunnel junctions (MTJs) are a key technology in modern spintronics because they are the basis of read-heads of modern hard disk drives, nonvolatile magnetic random access memories, and sensor applications. In this paper, we demonstrate that tunneling magnetoresistance can influence terahertz (THz) wave propagation through a MTJ. In particular, various magnetic configurations between parallel state and antiparallel state of the magnetizations of the ferromagnetic layers in the MTJ have the effect of changing the conductivity, making a functional modulation of the propagating THz electromagnetic fields. Operating in the THz frequency range, a maximal modulation depth of 60% is reached for the parallel state of the MTJ with a thickness of 77.45 nm, using a magnetic field as low as 30 mT. The THz conductivity spectrum of the MTJ is governed by spin-dependent electron tunneling. It is anticipated that the MTJ device and its tunability scheme will have many potential applications in THz magnetic modulators, filtering, and sensing.

关键词： Magnetoresistance Photoconductivity Ultrafast magnetic effects Magnetic tunnel junctions Terahertz time-domain spectroscopy

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Social Media Based Transportation Research: the state of the Work and the Networking

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 19-26页

作者： Yisheng Lv Yuanyuan Chen Xiqiao Zhang Yanjie Duan Naiqiang Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis *** summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified. © 2014 Chinese Association of automation.

关键词： Traffic control Transportation

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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Auto-LEE: A novel autonomous lower extremity exoskeleton for walking assistance

Auto-LEE: A novel autonomous lower extremity exoskeleton for...

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2018 IEEE International Conference on Information and automation, ICIA 2018

作者： He, Yong Wang, Chao Li, Nan Fu, Ruiqing Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781538680698

Wearable exoskeletons have been proven to be efficacious in aiding walking for individuals suffering from lower limb mobility disorder. However, the application of most existing devices is limited to inconvenience of usage, e.g., complicated training and unnatural gait. This paper presents a novel autonomous lower extremity exoskeleton, Auto-LEE, for the purpose of improving the practicality of walking assistive devices as well as simplifying their application process. The developed exoskeleton consists of two robotic legs, and each of them has 5 active degrees-of-freedom (DOFs) to independently control the rotations of hip, knee and ankle joints in the sagittal and coronal planes, which enables the device to possess self-balancing ability and flexible gait. The modular design concept is introduced into the structure and hardware development of Auto-LEE, making it more convenient to be assembled and maintained. In order to validate the self-balancing walking ability, virtual prototype simulation and preliminary experiment on flat terrain are implemented. © 2018 IEEE.

关键词： Exoskeleton (Robotics)

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Small-scale intersection scan model for UGV in urban environment

Small-scale intersection scan model for UGV in urban environ...

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2017 American Control Conference, ACC 2017

作者： Yang, Yi Hao, Li Zhu, Hao Ningyi, Lyu Wenjie, Song School of Automation State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781509059928

Intersection detection is a critical capacity for an Unmanned Ground Vehicle (UGV) to drive safely in structured urban environment. Large-scale intersections stamped on maps have plenty of features for detection while some unmapped small-scale urban intersections are hard to be identified. In this paper, we propose a novel intersection detection method conducted on the basis of Hidden Markov Model (HMM). This method is based on the intersection scan model to obtain the traversable directions to classify the intersection. The scan model is effective in dealing with both LIDAR and visual data. Combination of the scan model and the HMM can accurately estimate the traversable directions in consideration of both real-time and historic data. Results from simulations and real-world experiments have shown the functionality of the presented approach. © 2017 American Automatic Control Council (AACC).

关键词： Hidden Markov models

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UAV-aided Large-scale Map Building and Road Extraction for UGV 7

UAV-aided Large-scale Map Building and Road Extraction for U...

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7th IEEE Annual International Conference on CYBER technology in automation, Control, and intelligent systems, CYBER 2017

作者： Meiling, Wang Huachao, Yu Guoqiang, Feng Yi, Yang Yafeng, Li Tong, Liu School of Automation Beijing Institute of Technology State Key Laboratory of Complex System Intelligent Control and Decision Beijing100081 China

ISBN: (纸本)9781538604892

Due to the limited visual field of UGV, it is difficult to detect the accessible zone in a wide range of area in real time. This paper proposes a UAV-aided autonomous road network construction method where roads are previously unknown for UGV navigation. First, SIFT and RANSAC algorithms are applied to obtain the homography of UAV sequential images, through which these images are stitched to a large-scale map. Next, least square fitting algorithm and RDP algorithm are applied to extract initial road area and then obtain road network suitable for UGV navigation by topological processing and skeleton extraction. The experiments show that this UAV-aided method is effective in generating a large-scale road network map for UGV driving through unknown environment without human intervention. © 2017 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Shape-Memory Polymers: Mechanically Robust and UV-Curable Shape-Memory Polymers for Digital Light Processing Based 4D Printing (Adv. Mater. 27/2021)

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Advanced Materials 2021年第27期33卷

作者： Biao Zhang Honggeng Li Jianxiang Cheng Haitao Ye Amir Hosein Sakhaei Chao Yuan Ping Rao Yuan-Fang Zhang Zhe Chen Rong Wang Xiangnan He Ji Liu Rui Xiao Shaoxing Qu Qi Ge Frontiers Science Center for Flexible Electronics (FSCFE) Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials and Engineering (IBME) Northwestern Polytechnical University 127 West Youyi Road Xi'an 710072 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen 518055 China School of Engineering and Digital Arts University of Kent Canterbury Kent CT2 7NT UK State Key Laboratory for Strength and Vibration of Mechanical Structures Department of Engineering Mechanics Xi'an Jiaotong University Xi'an 710049 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 China Digital Manufacturing and Design Centre Singapore University of Technology and Design Singapore 487372 Singapore State Key Laboratory of Fluid Power and Mechatronic System Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Zhejiang University Hangzhou 310027 China

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Generative Adversarial Networks:Introduction and Outlook

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： Kunfeng Wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game

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