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Nonlinear auto disturbance rejection control for vibration displacement system of continuous cast mold driven by servo motor

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Nonlinear auto disturbance rejection control for vibration d...

Chinese control Conference (CCC)

作者： Huicheng Zheng Le Liu Yiming Fang Qiang Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao Hebei CN

Aiming at the problem about the nonlinear function and the mechanical zero offset in the vibration system of continuous casting mold driven by servo motor, a discrete auto disturbance rejection control (ADRC) with nonlinear function processing algorithm is proposed. Firstly, for the nonlinear multi solutions problem existing between rotation angle and displacement of mold, a nonlinear algorithm is proposed to get the practical phase difference of eccentric shaft. The algorithm is achieved by calculating the time difference between ideal time of arrival and actual time of arrival at the same position. Secondly, the mathematical model of vibration system is discretized by using the Euler method, and an ADRC is designed to estimate the eccentric shaft mechanical zero offset and load disturbance, and then compensate and weaken the position error. Finally, the simulation results demonstrate the feasibility of the proposed method.

关键词： Vibrations Servomotors Casting Shafts Process control Algorithm design and analysis Synchronous motors

Hierarchical recurrent attention network for response generation

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arXiv 2017年

作者： Xing, Chen Wu, Wei Wu, Yu Zhou, Ming Huang, Yalou Ma, Wei-Ying College of Computer and Control Engineering Nankai University Tianjin China College of Software Nankai University Tianjin China Microsoft Research Beijing China State Key Lab of Software Development Environment Beihang University Beijing China

We study multi-turn response generation in chatbots where a response is generated according to a conversation context. Existing work has modeled the hierarchy of the context, but does not pay enough attention to the fact that words and utterances in the context are differentially important. As a result, they may lose important information in context and generate irrelevant responses. We propose a hierarchical recurrent attention network (HRAN) to model both aspects in a unified framework. In HRAN, a hierarchical attention mechanism attends to important parts within and among utterances with word level attention and utterance level attention respectively. With the word level attention, hidden vectors of a word level encoder are synthesized as utterance vectors and fed to an utterance level encoder to construct hidden representations of the context. The hidden vectors of the context are then processed by the utterance level attention and formed as context vectors for decoding the response. Empirical studies on both automatic evaluation and human judgment show that HRAN can significantly outperform state-of-the-art models for multi-turn response generation. Copyright © 2017, The Authors. All rights reserved.

关键词： Signal encoding

Solving almost all systems of random quadratic equations

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arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Saad, Yousef Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

This paper deals with finding an n-dimensional solution x to a system of quadratic equations of the form yi = jhai;xij2 for 1 ≤ i ≤ m, which is also known as phase retrieval and is NP-hard in general. We put forth a novel procedure for minimizing the amplitude-based least-squares empirical loss, that starts with a weighted maximal correlation initialization obtainable with a few power or Lanczos iterations, followed by successive refinements based upon a sequence of iteratively reweighted (generalized) gradient iterations. The two (both the initialization and gradient flow) stages distinguish themselves from prior contributions by the inclusion of a fresh (re)weighting regularization technique. The overall algorithm is conceptually simple, numerically scalable, and easy-to-implement. For certain random measurement models, the novel procedure is shown capable of finding the true solution x in time proportional to reading the data f(ai;yi)g1≤i≤m. This holds with high probability and without extra assumption on the signal x to be recovered, provided that the number m of equations is some constant c > 0 times the number n of unknowns in the signal vector, namely, m > cn. Empirically, the upshots of this contribution are: I) (almost) 100% perfect signal recovery in the high-dimensional (say e.g., n ≥2;000) regime given only an information-theoretic limit number of noiseless equations, namely, m = 2n - 1 in the real-valued Gaussian case;and, ii) (nearly) optimal statistical accuracy in the presence of additive noise of bounded support. Finally, substantial numerical tests using both synthetic data and real images corroborate markedly improved signal recovery performance and computational efficiency of our novel procedure relative to state-of-the-art approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational efficiency

PSSE Redux: Convex relaxation, decentralized, robust, and dynamic approaches

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arXiv 2017年

作者： Kekatos, Vassilis Wang, Gang Zhu, Hao Giannakis, Georgios B. Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States

Metric based on multi-order spaces for cross-modal retrieval

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Metric based on multi-order spaces for cross-modal retrieval

IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang Zhang Bingpeng Ma Guorong Li Qingming Huang Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Computer and Control Engineering University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China

ISBN: (纸本)9781509060689

This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We assume that compared with vectors, matrices can directly represent images and characterize the structure of feature space. Furthermore, we propose a Metric based on Multi-order spaces (MMs). Multi-order statistic features are used to represent images for enriching the semantic information, and metrics among the multi-spaces are jointly learned to measure the similarity between two different modalities. Specifically, there are three steps for MMs. First, we jointly use the bags of visual features (zero-order), mean (first-order) and covariance (second-order) to characterize each image. Second, considering that covariance matrices and vectors lie on a Riemannian manifold and an Euclidean space respectively, we embed multi-order spaces into their corresponding Hilbert spaces to reduce the heterogeneity among the original spaces. Finally, the similarity between two different modalities can be measured by learning multiple transformations from the different Hilbert spaces to a common subspace. The performance of the proposed method over the state-of-the-art has been demonstrated through the experiments on two public datasets.

关键词： Hilbert space Manifolds Correlation Covariance matrices Feature extraction Linear programming

Distributed kalman filter in a network of linear dynamical systems

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arXiv 2017年

作者： Marelli, Damián Zamani, Mohsen Fu, Minyue Department of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University 388 Yuhangtang Road Hangzhou Zhejiang Province310058 French-Argentinean International Center for Information and Systems Sciences National Scientific and Technical Research Council Ocampo Esmeralda Rosario2000 Argentina School of Electrical Engineering and Computer Science University of Newcastle University Drive CallaghanNSW2308 Australia

This paper is concerned with the problem of distributed Kalman filtering in a network of interconnected subsystems with distributed control protocols. We consider networks, which can be either homogeneous or heterogeneous, of linear time-invariant subsystems, given in the state-space form. We propose a distributed Kalman filtering scheme for this setup. The proposed method provides, at each node, an estimation of the state parameter, only based on locally available measurements and those from the neighbor nodes. The special feature of this method is that it exploits the particular structure of the considered network to obtain an estimate using only one prediction/update step at each time step. We show that the estimate produced by the proposed method asymptotically approaches that of the centralized Kalman filter, i.e., the optimal one with global knowledge of all network parameters, and we are able to bound the convergence rate. Moreover, if the initial states of all subsystems are mutually uncorrelated, the estimates of these two schemes are identical at each time step. Copyright © 2017, The Authors. All rights reserved.

关键词： Kalman filters

Learning label-Specific Features and Class-Dependent labels for Multi-label Classification

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IEEE Transactions on Knowledge and Data engineering 2016年第99期PP卷 3309-3323页

作者： Huang, Jun Li, Guorong Huang, Qingming Wu, Xindong School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101480 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer Science University of Vermont 33 Colchester Avenue BurlingtonVT05405 United States

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent labels in a sparse stacking way, denoted as LLSFDL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods. © 2016 IEEE.

关键词： Classification (of information)

Maximizing Network Utility of Rechargeable Sensor Networks with Spatiotemporally Coupled Constraints

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Maximizing Network Utility of Rechargeable Sensor Networks w...

作者： Deng, Ruilong Zhang, Yongmin He, Shibo Chen, Jiming Shen, Xuemin State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated. © 2016 IEEE.

关键词： Optimization

Distributed containment control for multi-robot system based on binocular vision and RBF neural network

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International Journal of Control and Automation

International Journal of control and Automation 2015年第9期8卷 139-152页

作者： Shao, Nuan Li, Huiguang Liu, Le Li, Guoyou Key Lab of Industrial Computer Control Engineering of Hebei Province College of Electrical Engineering Yanshan University Qinhuangdao China

Aiming at the distributed containment control problem for Multi-Robot system with dynamic leaders, we use binocular vision as the sensing device, so firstly, a binocular visual model for dynamic target is established by introducing the pseudo depth variable, and it is used to provide Real-Time status information for the followers. Then, in the case that each follower can only get information from part of the leaders, even individual follower can not get any information from the leaders directly, the topological relationship among robots is built by using the graph theory. Once more, RBF neural networks are employed to approximate the uncertainties in the system model of followers, and L2robust control laws are used to restrain the influences of the approximation errors. Finally, a simulation is carried out on multiple 2 Degrees-Of-Freedom (DOF) robots, and results validate the designed controllers can drive the followers converge to the convex hull spanned by the leaders asymptotically and realize synchronization motion ultimately. © 2015 SERSC.

关键词： Multipurpose robots