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3rd CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2014

作者： Du, Jinhua Wang, Miaomiao Zhang, Meng School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an710048 China

ISBN: (纸本)9783662459232

This paper presents a simple but effective sentence-length informed method to select informative sentences for active learning (AL) based SMT. A length factor is introduced to penalize short sentences to balance the "exploration" and "exploitation" problem. The penalty is dynamically updated at each iteration of sentence selection by the ratio of the current candidate sentence length and the overall average sentence length of the monolingual corpus. Experimental results on NIST Chinese–English pair and WMT French-English pair show that the proposed sentence-length penalty based method performs best compared with the typical selection method and random selection strategy. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Iterative methods

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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A Pursuit Strategy for Multi-Agent Pursuit-Evasion Game via Multi-Agent Deep Deterministic Policy Gradient Algorithm

A Pursuit Strategy for Multi-Agent Pursuit-Evasion Game via ...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Ye, Jianfeng Wang, Qing Ma, Bei Wu, Yongbao Xue, Lei School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China SEU-Monash Joint Graduate School Southeast University Nanjing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

This paper studies a classical pursuit-evasion problem. The pursuer attempts to capture the faster evader in a bounded area. The velocity of evader is 1.2 times as fast as the pursuers'. All of them have adaptive strategies. We use game theory to model the multi-agent pursuit-evasion game and prove that the game model has Nash equilibrium. Then, we modify the multi-agent deep deterministic policy gradient (MADDPG) algorithm for seeking the Nash equilibrium. The simulation examples are given to illustrate the effectiveness of the designed method. © 2022 IEEE.

关键词： Game theory

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Robust control reconfiguration of resource allocation systems with Petri nets and integer programming

Robust control reconfiguration of resource allocation system...

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作者： Li, Jun Zhou, Mengchu Guo, Tao Gan, Yahui Dai, Xianzhong Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China School of Automation Southeast University Nanjing 210096 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

Supervisory control reconfiguration can handle the uncertainties including resource failures and task changes in discrete event systems. It was not addressed to exploit the robustness of closed-loop systems to accommodate some uncertainties in the prior studies. Such exploitation can cost-efficiently achieve reconfigurability and flexibility for real systems. This paper presents a robust reconfiguration method based on Petri nets (PNs) and integer programming for supervisory control of resource allocation systems (RASs) subject to varying resource allocation relationships. An allocation relationship is seen as a control specification while the execution processes requiring resources as an uncontrolled plant. First, a robust reconfiguration mechanism is proposed. It includes updating the P-invariant-based supervisor and evolving the state of the closed-loop system. The latter adapts to the control specification changes by the self-regulation of the closed-loop system's state. Next, two novel integer programming models for control reconfiguration are proposed, called a reconfiguration model with acceptability and reconfiguration one with specification correction. Since both models integrate the firability condition of transitions, no additional efforts are required for the state reachability analysis. Finally, a hospital emergency service system is used as an example to illustrate them. © 2014 Elsevier Ltd. All rights reserved.

关键词： Integer programming

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Prediction of Direct Normal Irradiation Based on CNN-LSTM Model 20

Prediction of Direct Normal Irradiation Based on CNN-LSTM Mo...

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5th International Conference on Multimedia systems and Signal Processing, ICMSSP 2020

作者： Zhang, Chengying Yan, Zhen Ma, Chao Xu, Xiaoli Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781450377485

Due to the intermittency and instability of power output of solar power stations (SPS), the safety and stationarity of the electric grid are facing considerable challenges. So, it is essential to predict the power output of SPS accurately. To date, as the most important meteorological factor determining the power output of SPS, the prediction of direct normal irradiation (DNI) has always been a hotspot, however at the same time a challenging problem. In recent years, with the improvement of hardware performance, deep learning methods have witnessed rapid development in the field of forecasting of renewable energies. In this paper, a hybrid method combining convolutional neural network (CNN) and longshort term memory (LSTM) neural network is proposed, the former can be effectively used for extraction of spatial feature, while the latter is used for extraction of temporal feature. As a case study, the proposed method is used for prediction of DNI of four typical days, i.e. spring equinox, summer solstice, autumn equinox, and winter solstice, based on the meteorological conditions of Zhangbei. The results show that deep learning methods can be effectively used for prediction of DNI, however, the proposed method in this paper achieves higher prediction accuracy than that of the compared methods. © 2020 ACM.

关键词： Convolutional neural networks

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 IEEE.

关键词： Electroencephalography

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Anthropogenic monoterpenes aggravating ozone pollution

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National Science Review 2022年第9期9卷 178-185页

作者： Haichao Wang Xuefei Ma Zhaofeng Tan Hongli Wang Xiaorui Chen Shiyi Chen Yaqin Gao Ying Liu Yuhan Liu Xinping Yang Bin Yuan Limin Zeng Cheng Huang Keding Lu Yuanhang Zhang State Key Joint Laboratory of Environmental Simulation and Pollution Control State Environmental Protection Key Laboratory of Atmospheric Ozone Pollution ControlCollege of Environmental Sciences and Engineering Peking University School of Atmospheric Sciences Sun Yat-sen University Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary Key Laboratory of Tropical Atmosphere–Ocean System Ministry of Education Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Institute of Energy and Climate Research IEK-8:TroposphereForschungszentrum Jülich Gmb H State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Complex Shanghai Academy of Environmental Sciences Institute for Environmental and Climate Research Jinan University

Monoterpenes have been known to have a critical influence on air quality and climate change through their impact on the formation of fine *** we present field evidence that monoterpene oxidations largely enhanced local ozone production in a regional site in eastern *** observed monoterpene was most likely from biomass burning rather than biogenic emissions,as indicated by the high correlation with CO at night-time,and the observed ratio of these two species was consistent with previously determined values from biomass burning *** monoterpene oxidations were determined experimentally based on direct radical measurements,leading to a daily ozone enhancement of 4-18 parts per billion by volume（ppb）,which was 6%-16% of the total ozone production,depending on the speciation of *** demonstrates that the previously overlo oked anthropogenic monoterpenes make an important contribution to 03 production in eastern *** role could possibly be important at similar locations across China and other parts of the world that are characterized by massive emissions,especially where there are high *** results highlight that anthropogenic monoterpenes should be taken into account when proceeding with the coordinated mitigation of O3and particulate matter pollution.

关键词： monoterpenes ozone pollution biomass burning anthropogenic emissions radical chemistry

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H∞ robust control of direct-drive permanent magnetic synchronous generator considering the uncertainty of stator parameters

H∞ robust control of direct-drive permanent magnetic synchr...

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作者： Zu, Hui Zhang, Guo-Bao Fei, Shu-Min Wei, Zi-Cong Zhu, Hai-Rong School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Ministry of Education Nanjing 210096 China

ISBN: (纸本)9789881725592

The robust control issues of direct-drive permanent magnet synchronous generator (DD-PMSG) are discussed, in which the variation of stator resistances and inductance are considered. The speed loop of DD-PMSG is a linear system with several ways of disturbance inputs, so the H∞ mixed sensitivity control method is applied to eliminate the torque disturbance and measurement noise. The current loop is regarded as an uncertain DESCRIPTOR system and the output-feedback H∞ robust control algorithm for the uncertain DESCRIPTOR system is proposed. Linear matrix inequalities (LMI) is adopted to prove the sufficient conditions of descriptor system quadratic stability and satisfying the H∞ robust performances, and then an output feedback controller is presented. Simulation results show that comparing with PI controller and the standard H∞ method designed controller, the proposed controller has better response and control accuracy, the inhibition of disturbance input and parameter uncertainty has better robustness than the PI controller, and less conservative than the standard H∞ controller. © 2011 Chinese Assoc of Automati.

关键词： Linear matrix inequalities

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Image-Based Fast Finite-Time Target Tracking control of Onboard Inertially Stabilized Camera Platform

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IEEE Transactions on Industrial Electronics 2024年第12期71卷 16409-16418页

作者： Sun, Xunhong Zhu, Wenwu Du, Haibo Chen, Weile Chen, Chih-Chiang Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University School of Automation Nanjing210096 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

In this article, we investigate the problem of fast finite-time target tracking control of the inertially stabilized platform (ISP) with a camera that mounted on a mobile robot. At first, the kinematics of the onboard ISP target tracking system is established by the geographic coordinate and pinhole camera model, which includes the state-dependent uncertainties and external disturbances. Then, based on the image information captured by camera, a continuous image-based fast finite-time control algorithm is designed for target tracking. Rigorous analysis proves that the target can be tracked at the center of camera's view in finite time in the absence of disturbances. In the presence of disturbances, the tracking error can converge to a region around the origin in finite time. Simulation and experimental comparisons are presented to confirm the efficiency of the proposed method. © 1982-2012 IEEE.

关键词： Mobile robots

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A Two-layer Adaptive control Strategy for Photovoltaic Power in Different Scenarios 3

A Two-layer Adaptive Control Strategy for Photovoltaic Power...

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3rd International Conference on Energy engineering and Power systems, EEPS 2023

作者： Wan, Yanghui Yan, Jiahao Chu, Tianfeng Li, Yaping Yuan, Kun Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing China Power Automation Department Nanjing China State Grid Liaoning Electric Power Co. Ltd. Electric Power Research Institute Shenyang China

ISBN: (纸本)9798350313857

Currently, control strategies for distributed photovoltaic (PV) power generation are relatively single and rigid, and cannot flexibly adapt to the diverse demands of power grids in different scenarios. Firstly, this paper analyzes the adaptive relationship between different dispatching demands, the regulation ability of distributed PV, and the characteristics of the local voltage. Secondly, for different scenarios, the coordinated controller determines the reference values of active power and reactive power given to the inverter. Thus, distributed PV power control modes which can adapt to different scenarios have been established. Then, these control modes can be classified into single control modes and compound control modes according to the complexity of scenario demands. On this basis, a specific two-layer adaptive control strategy and process of distributed PV power generation in a low-voltage distribution network are further proposed, which takes into account both external demands of the distribution network and the regulation ability of distributed PV. Finally, a case study is given to verify the rationality and effectiveness of the two-layer adaptive scenarios control strategy. This strategy can effectively improve the comprehensive support ability of distributed PV power generation towards the power grid in multiple scenarios, and improve the stable operation level of the regional power grid. © 2023 IEEE.

关键词： Reactive power

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