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IEEE International Symposium on Electromagnetic Compatibility (EMC-Beijing)

作者： XiaXia Zeng Fanyong Cheng Wenzhong Lin Haibo Luo Zhiqiang Ruan Fujian provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou China

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we adopt the descriptor of Locality-constrained Linear Coding (LLC) and Double Distribution Support Vector Machine (DDSVM) to address music symbol classification in OMR. LLC can obtain stronger feature representation ability compared with original music symbol. Recently, DDSVM is presented to obtain stronger robustness and generalization performance. Therefore, DDSVM is adopted to improve the classification performance in OMR. Moreover, One Versus Rest Double Distribution Support Vector Machine (OVR-DDSVM) is proposed for multi-class music symbol classification. OVR-DDSVM can obtain high accuracy only using linear kernel due to LLC descriptor, and this speeds the classification process. OVR-DDSVM is tested on more than 10000 music symbol images of 20 classes, and experimental results verify the superiority of LLC+DDSVM to other algorithms.

关键词： Support vector machines Feature extraction Robustness Music Encoding Histograms Information processing

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The Ball Mill Load Measuring algorithm though Grinding tone signal based on GA

The Ball Mill Load Measuring algorithm though Grinding tone ...

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2017 International Seminar on Artificial Intelligence, Networking and Information Technology

作者： Yingmin Yi Haichuan Yang Lu Sun Xiaoli Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

For a high energy loss and complex system of ball mill,this paper provide a ball mill load detection method based on genetic algorithm optimizing BP neural *** effective frequency range of mill sound signal is *** soft measurement model of mill load based on mill sound signal is *** order to solve the problem which converge slowly and easily reach minimal value,the global optimization of GA(genetic algorithm) local optimization of BP neural network will be combined to improve the BP neural *** with the detected mill load error generated from existing BP neural network and RBF neural network based on *** experiments results show that the proposed algorithm has better precision.

关键词： Ball mill load Grinding tone signal Genetic algorithm RBF neural network

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Nonlinear integral sliding mode variable structure control for two-tank system with input — Limitation

Nonlinear integral sliding mode variable structure control f...

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Annual Conference of Industrial Electronics Society

作者： Hai-Peng Ren Jiao Hu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an Shaanxi CN Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

To deal with the unsatisfactory transient response of the conventional integral sliding mode control (ISMC), A nonlinear integral sliding mode controller (NISMC) is proposed in this paper, which uses a smooth nonlinear function with “small error amplification and large error saturation” property, meanwhile, takes the limited input into account. The proposed NISMC improves the transient performance, i.e., less controller chattering and less steady error. The two-tank system is usually treated as a paradigm to test the control algorithms. Five kinds of sliding-mode control methods (SMC) are used to control the two-tank liquid level to test the performance. The simulation and experimental results show that the proposed NISMC has the superior performance as compared to the comparison methods.

关键词： Liquids Two dimensional displays Level control Robustness Valves Sliding mode control Transient analysis

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Complete stability of delayed recurrent neural networks with Gaussian activation functions

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Neural networks : the official journal of the International Neural Network Society 2017年 85卷 21-32页

作者： Peng Liu Zhigang Zeng Jun Wang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: liupengjz@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng@***. Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong. Electronic address: jwang.cs@cityu.edu.hk.

This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3 equilibrium points with 0≤k≤n, among which 2 and 3-2 equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results.

关键词： Complete stability Gaussian functions Recurrent neural networks Time-varying delays

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A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm

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Neurocomputing 2017年 221卷 24-31页

作者： Rui Hu Shiping Wen Zhigang Zeng Tingwen Huang School of Automation Huazhong University of Science and Technology Wuhan PR China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan PR China Texas A & M University at Qatar Doha 5825 Qatar

Short term power load forecasting plays an important role in the security of power system. In the past few years, application of artificial neural network (ANN) for short-term load forecasting (STLF) has become a research hotspots. Generalized regression neural network (GRNN) has been proved to be suitable for solving the non-linear problems. And according to the historical load curve, it can be known that STLF is a non-linear problem. Thus, the GRNN was used for STLF in this paper. However, the value of spread parameter σ determines the performance of the GRNN. The fruit fly optimization algorithm with decreasing step size (SFOA) is introduced to select an appropriate spread parameter σ . Combined with the weather factors and the periodicity of short-term load, an effective STLF model based on the GRNN with decreasing step FOA was proposed. Performance of the proposed SFOA-GRNN model is compared with other ANN on the basis of prediction error.

关键词： Short term power load forecasting Generalized regression neural network Fruit fly optimization algorithm Decreasing step size

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Second-Order controllability of Multi-Agent Systems with Multiple Leaders

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Communications in Theoretical Physics 2016年第5期65卷 585-592页

作者：刘波史运涛苏厚胜韩潇 College of Science North China University of Technology Key Laboratory of Beijing for Field-Bus Technology & Automation North China University of Technology School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaHuazhong University of Science and Technology

This paper proposes a new second-order continuous-time multi-agent model and analyzes the controllability of second-order multi-agent system with multiple leaders based on the asymmetric *** paper considers the more general case:velocity coupling topology is different from location coupling *** sufficient and necessary conditions are presented for the controllability of the system with multiple *** addition,the paper studies the controllability of the system with velocity damping *** results are given to illustrate the correctness of theoretical results.

关键词： controllability second-order multi-agent systems multiple leaders asymmetric topology

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Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning

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Physical Review E 2017年第2期96卷 022411-022411页

作者： Duxin Chen Bowen Xu Tao Zhu Tao Zhou Hai-Tao Zhang Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan 430074 P.R. China Web Sciences Center University of Electronic Science and Technology of China Chengdu 610054 P.R. China

Coordination shall be deemed to the result of interindividual interaction among natural gregarious animal groups. However, revealing the underlying interaction rules and decision-making strategies governing highly coordinated motion in bird flocks is still a long-standing challenge. Based on analysis of high spatial-temporal resolution GPS data of three pigeon flocks, we extract the hidden interaction principle by using a newly emerging machine learning method, namely the sparse Bayesian learning. It is observed that the interaction probability has an inflection point at pairwise distance of 3–4 m closer than the average maximum interindividual distance, after which it decays strictly with rising pairwise metric distances. Significantly, the density of spatial neighbor distribution is strongly anisotropic, with an evident lack of interactions along individual velocity. Thus, it is found that in small-sized bird flocks, individuals reciprocally cooperate with a variational number of neighbors in metric space and tend to interact with closer time-varying neighbors, rather than interacting with a fixed number of topological ones. Finally, extensive numerical investigation is conducted to verify both the revealed interaction and decision-making principle during circular flights of pigeon flocks.

关键词： Collective behavior Self-organization Birds Flocks Organisms

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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A deep learning-based remaining useful life prediction approach for bearings

arXiv

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arXiv 2018年

作者： Cheng, Cheng Ma, Guijun Zhang, Yong Sun, Mingyang Teng, Fei Ding, Han Yuan, Ye The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The College of Control Science and Engineering Zhejiang University Hangzhou310007 China The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using a ǫ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

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Optimizing the Taxonomy and Hierarchy of a Chinese Lexical Database - Cilin

Optimizing the Taxonomy and Hierarchy of a Chinese Lexical D...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： Tingxin Wei Xiaohe Chen Shi Shengwang Junsheng Zhou Yanhui Gu Weiguang Qu International College for Chinese Studies Nanjing Normal University Nanjing China School of Chinese Language and Literature Nanjing Normal University Nanjing China School of Computer Science and Technology Nanjing Normal University Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9781728102085;9781728102078

Cilin is one of the most popular semantic knowledge bases in Chinese information processing. Due to its coding scheme and taxonomical arrangement, some semantic relations among words are not explicitly shown. Our work aims to characterize its semantic relations by adding tags and compound codes to optimize the taxonomy and hierarchy of Cilin. Experiment results show that using the tag-augmented Cilin as a knowledge base improves the performance in the task of semantic similarity measurement.

关键词： Semantics Compounds Taxonomy Encoding Knowledge based systems Wheels Task analysis

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