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arXiv 2020年

作者： Li, Qi Zhao, Xiaoxu Deng, Longjiang Shi, Zhongtai Liu, Sheng Wei, Qilin Zhang, Linbo Cheng, Yingchun Zhang, Li Lu, Haipeng Gao, Weibo Huang, Wei Qiu, Cheng-Wei Xiang, Gang Pennycook, Stephen John Xiong, Qihua Loh, Kian Ping Peng, Bo National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore Department of Chemistry Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore 117549 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Key Laboratory of Flexible Electronics Institute of Advanced Materials Nanjing Tech University Nanjing211816 China College of Physical Science and Technology Sichuan University Chengdu610064 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The "Zeeman effect" offers unique opportunities for magnetic manipulation of the spin degree of freedom (DOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMDs) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley DOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMDs can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (geff = -6.4) and 10 K (geff = -11) in a CVD-grown Fe-doped MoS2 monolayer;the effective Landé geff factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization. Copyright © 2020, The Authors. All rights reserved.

关键词： Landforms

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A Review of Collaborative Innovation among University,Industry,and Government from the Perspective of Coupling

A Review of Collaborative Innovation among University,Indust...

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作者： Genping Yu Di Chen Zuoyong Li Zhongyan Lin Internet Innovation Research Center-the College's Research Base of Humanities and Social Science in Fujian Province Minjiang University Scientific Research Administrative Department of Minjiang University College of Computer and Control Engineering Minjiang University

University is not only the primary unit of training talents but also an essential part of the national innovation *** paper will research the university industry-academia-research collaboration literature review,through further statement and explanation of the industry-academia-research collaboration *** the coupling relationship between the three major innovations of universities,industries,and governments,enhance the contribution of university science and technology innovation application ability and service society *** promote the construction of regional science and technology cooperation platform and the coordinated development of multilateral technology systems.

关键词： University Industry Government Collaborative innovation

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Text emotion distribution learning via multi-task convolutional neural network 27

Text emotion distribution learning via multi-task convolutio...

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27th International Joint Conference on Artificial Intelligence, IJCAI 2018

作者： Zhang, Yuxiang Fu, Jiamei Zhang, Ying Yang, Jufeng College of Computer Science and Technology Civil Aviation University of China Tianjin China College of Computer and Control Engineering Nankai University Tianjin China College of Comp. Sci. and Tech. Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9780999241127

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users' emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexicon-based strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches. © 2018 International Joint Conferences on Artificial *** right reserved.

关键词： Classification (of information)

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Distributed parameter estimation under event-triggered communications

arXiv

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arXiv 2019年

作者： He, Xingkang Liu, Qian Wu, Junfeng Johansson, Karl Henrik ACCESS Linnaeus Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden LSC Academy of Mathematics and Systems Science Chinese Academy of Sciences China University of Chinese Academy of Sciences China College of Control Science and Engineering Zhejiang University China

In this paper, we study a distributed parameter estimation problem with an asynchronous communication protocol over multi-agent systems. Different from traditional time-driven communication schemes, in this work, data can be transmitted between agents intermittently rather than in a steady stream. First, we propose a recursive distributed estimator based on an event-triggered communication scheme, through which each agent can decide whether the current estimate is sent out to its neighbors or not. With this scheme, considerable communications between agents can be effectively reduced. Then, under mild conditions including a collective observability, we provide a design principle of triggering thresholds to guarantee the asymptotic unbiasedness and strong consistency. Furthermore, under certain conditions, we prove that, with probability one, for every agent the time interval between two successive triggered instants goes to infinity as time goes to infinity. Finally, we provide a numerical simulation to validate the theoretical results of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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SigNet: A novel deep learning framework for radio signal classification

arXiv

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arXiv 2020年

作者： Chen, Zhuangzhi Cui, Hui Xiang, Jingyang Qiu, Kunfeng Huang, Liang Zheng, Shilian Chen, Shichuan Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Utron Technology Co. Ltd. [as Hangzhou Qianjiang Distinguished Expert Hangzhou310056 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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Network Resilience

arXiv

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arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Guidance Vector Field Encoding based on Contraction Analysis 16

Guidance Vector Field Encoding based on Contraction Analysis

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16th European control Conference, ECC 2018

作者： Wu, Chengshuai Chen, Jian Jeltsema, DImitri Dai, And Chenxi State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Section Control Systems Engineering HAN University of Applied Science P.O. Box 2217 Arnhem6802 CE Netherlands Department of Electrical and Computer Engineering Ohio State University Columbus43210 United States

ISBN: (纸本)9783952426982

The guidance vector field encoding problem is revisited via exploiting the recently developed differential Lyapunov framework for contraction analysis. In control applications, a designed guidance vector field is utilized to guide a particle (such as a robot, or an end actuator) to converge to and circulate along a target curve, such as robot navigation, or motion planing. This paper proposes some new methods for guidance vector filed encoding. The methods analyze the guidance vector field encoding problem from a theoretical perspective providing insights into the problem, and it shows that the contraction analysis is a natural theoretical tool for guidance vector encoding. Furthermore, the differences and relations between the proposed result and the previous methods are discussed. Simulation examples are presented to illustrate the effectiveness of the proposed methods. © 2018 European control Association (EUCA).

关键词： Robots

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Efficient Multi-robot Exploration via Multi-head Attention-based Cooperation Strategy

arXiv

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arXiv 2019年

作者： Liu, Shuqi Wu, Zhaoxia National Key Laboratory of Big Data Management and Analysis College of Computer and Science Northeastern University Shenyang110004 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

The goal of coordinated multi-robot exploration tasks is to employ a team of autonomous robots to explore an unknown environment as quickly as possible. Compared with "human-designed" methods, which began with heuristic and rule-based approaches, "learning-based" methods enable individual robots to learn sophisticated and hard-to-design cooperation strategies through deep reinforcement learning technologies. However, in decentralized multi-robot exploration tasks, learning-based algorithms are still far from being universally applicable to the continuous space due to the difficulties associated with area calculation and reward function designing;moreover, existing "learning-based" methods encounter problems when attempting to balance the "historical trajectory" issue and target area conflict problem. Furthermore, the scalability of these methods to a large number of agents is poor because of the exponential explosion problem of state space. Accordingly, this paper proposes a novel approach Multi-head Attention-based Multi-robot Exploration in Continuous Space (MAMECS) aimed at reducing the state space and automatically "learning" the cooperation strategies required for decentralized multi-robot exploration tasks in continuous space. Computational geometry knowledge is applied to describe the environment in continuous space and to design an improved reward function to ensure a superior exploration rate. Moreover, the multi-head attention mechanism employed helps to solve the "historical trajectory" issue in the decentralized multi-robot exploration task, as well as to reduce the quadratic increase of action space. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Reliability analysis of web server cluster systems based on proportional hazards model

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Journal of Southeast University(English Edition) 2018年第2期34卷 187-190页

作者： Hou Chunyan Wang Jinsong Chen Chen School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China College of Computer and Control Engineering Nankai UniversityTianjin 300071China

An approach for web server cluster(WSC)reliability and degradation process analysis is *** reliability process is modeled as a non-homogeneous Markov process(NHMH)composed of several non-homogeneous Poisson processes(NHPPs).The arrival rate of each NHPP corresponds to the system software failure rate which is expressed using Cox s proportional hazards model(PHM)in terms of the cumulative and instantaneous load of the *** cumulative load refers to software cumulative execution time,and the instantaneous load denotes the rate that the users requests arrive at a *** result of reliability analysis is a time-varying reliability and degradation process over the WSC ***,the evaluation experiment shows the effectiveness of the proposed approach.

关键词： web server cluster load-sharing proportional hazards model reliability software aging

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Event-triggered add-on safety for connected and automated vehicles using road-side network infrastructure

arXiv

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arXiv 2019年

作者： Mamduhi, Mohammad H. Hashemi, Ehsan Baras, John S. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science Royal Institute of Technology Stockholm10044 Sweden Mechanical and Mechatronics Engineering Department University of Waterloo ON Canada Institute of Systems Research Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

This paper proposes an event-triggered add-on safety mechanism to adjust the control parameters for timely braking in a networked vehicular system while maintaining maneuverability. Passenger vehicle maneuverability is significantly affected by the combined-slip friction effect, in which larger longitudinal tire slips result in considerable drop in lateral tire forces. This is of higher importance when unexpected dangerous situations occur on the road and immediate actions, such as braking, need to be taken to avoid collision. Harsh braking can lead to high-slip and loss of maneuverability;hence, timely braking is essential to reduce high-slip scenarios. In addition to the vehicles own active safety systems, the proposed event-triggered add-on safety is activated upon being informed about dangers by the road-side infrastructure. The aim is to incorporate the add-on safety feature to adjust the automatic control parameters for smooth and timely braking such that a collision is avoided while vehicle's maneuverability is maintained. We study two different wireless technologies for communication between the infrastructure and the vehicles, the Long-Term Evolution (LTE) and the fifth generation (5G) schemes. The framework is validated through high-fidelity software simulations and the advantages of including the add-on feature to augment the safety margins for each communication technology is evaluated. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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