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第三十四届中国控制会议

作者： XIE Guo ZHANG Dan HEI Xinhong QIAN Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781467374439

Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian noise, however, is still a challenge. For a class of nonlinear systems where all of the candidate parameters are contained in a definite parameter set, an online parameters and sates estimation method is proposed based on particle filter and Bayes theorem as the following steps. Firstly, regarding all of the candidates, the states are estimated by particle filter(PF) algorithm. Secondly the posterior probabilities of all of candidates are calculated according to the Bayes theorem;then the weights of all of the candidates are obtained through normalization. Lastly, the parameters and sates are estimated ultimately according to the weighted sum of all of the candidates and states. Numerical illustrations are presented to exhibit the application of the method proposed herein, and the performance of the method is examined.

关键词： Nonlinear system,Particle filter,Parameter set,Online parameter identification

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Modelling and Optimization of Heating Dispatch in Thermal Power Plant

Modelling and Optimization of Heating Dispatch in Thermal Po...

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第28届中国控制与决策会议

作者： Lixia Xie Yali Wu Xianyong Peng Lei Li Fan Yi School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Emerson Process Management Co.Ltd.

ISBN: (纸本)9781467397155

With the addition of heating in thermal power *** to lack of guidance for heating scheduling in traditional scheduling mode,it has been unable to satisfy the current costs for energy conservation and saving of thermal power *** load distribution(especially heat load dispatch) is an important subject in the operation optimization of a thermal power *** model of heat load dispatch on the condition of economic power dispatch is established and linear programming for optimized heat load distribution among units based on LINGO is *** average distribution and linear programming distribution are contrasted in a thermal power *** is obviously shown by simulation and calculation that linear programming distribution gets more optimal results than current average *** has important significance to reduce energy consumption and save costs.

关键词： Heating Dispatch Thermal Power Plant LINGO

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Circadian Rhythms Recovery Based on Slide Mode controller 34

Circadian Rhythms Recovery Based on Slide Mode Controller

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第三十四届中国控制会议

作者： ZHOU Ren REN Haipeng HUANG Xiaona Department of Information and Control Engineering Xi'an University of Technology Shaanxi Key Laboratory for Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781467374439

Disrupting the circadian rhythms will cause health problems such as sleep disorders, memory disorders and obesity. Finding the suitable external stimulation to synchronize a model with a desired phase is a biologically significant issue. The phase control of circadian rhythms for Drosophila is considered from control engineering viewpoint in this paper. If all parameters of the model are known, we can use the feedback linearization to design a tracking controller for the phase ***, in practice, parameters uncertainties always exist. To deal with this problem, a slide-mode controller is proposed for the phase tracking control of circadian rhythms. The simulation results show the effectiveness of the proposed method.

关键词： Circadian rhythms,Circadian phase,Parameter uncertainty,Slide mode control

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Stochastic resonance of asymmetric bistable system driven by binary signals under α stable noise

Stochastic resonance of asymmetric bistable system driven by...

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Chinese control Conference (CCC)

作者： Shang-Bin Jiao Jie Kou Ding Liu Qing Zhang National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Xi'an Shaanxi CN Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

In this paper the asymmetric bistable system excited by binary aperiodic signals is taken as a model and the average symbol error rate is regarded as an index to study stochastic resonance (SR) phenomenon. Firstly, the SR driven by binary signals under α stable noise is studied. Secondly, the interplay between the α stable noise parameters α, β, and the system parameters a, b, r on the resonant output effect is explored. The results show that weak binary signals detection can be realized by adjusting the system parameters a, b and r. The optimal values solved by these parameters can make the system produce the best SR effect. For a certain a or b, there is an optimal value under different α or β. For the parameter r, there is an optimal value under different α, and there are several optimal values under different β. Moreover, when α or β is given different values, the evolution laws in asymmetric bistable SR system excited by binary signals are same. The results lay a foundation for realizing the adaptive parameter adjustment in asymmetric bistable SR system with α stable noise.

关键词： Error analysis Signal detection Indexes Time-domain analysis Stochastic resonance Fluctuations Market research

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Research on the weak signal detection method based on adaptive vibrational resonance

Research on the weak signal detection method based on adapti...

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Chinese control Conference (CCC)

作者： Shang-Bin Jiao Di Sun Ding Liu Wei Chao Huang National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Xi'an Shaanxi CN Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

In this paper, the weak signal detection under α stable noise is investigated based on bistable vibrational resonance (VR) which is driven by a high frequency signal. On the one hand, the energy of the high frequency drive signal is transferred to the low frequency weak signal when VR occurs; on the other hand, the control of stochastic resonance (SR) is achieved based on VR, which transfers more noise energy into useful signal energy. In addition, considering the requirements of real-time detection, the amplitude and frequency of the high frequency drive signal are optimized by the knowledge-based particle swarm optimization (KPSO), which takes the mean signal-noise-ratio (MSNR) of output as the fitness function, and the property that VR system produces the best resonance effect just when the valid system parameter â(B,Ω) is greater than zero as knowledge. Finally, the parameter compensation is combined to achieve multi-high frequency weak signals detection with a stable noise. Furthermore, the method is applied to the vibration fault diagnosis of a mono-crystalline silicon furnace, and the experiment results show the effectiveness and practicability of the method.

关键词： Decision support systems

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Data-Based Health State Analysis for the Axle of High Speed Train

Data-Based Health State Analysis for the Axle of High Speed ...

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International Conference on Computational Intelligence and Security

作者： Guo Xie Minying Ye Xinhong Hei Jinwei Zhao Fucai Qian Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

Taking into account that the present popular methods, such as the judgement of the axle failure based on temperature threshold, and the early warning of axle based on real-time temperature analysis, cannot analyze the changing of performance trends, a health state analysis method for the axle of high-speed train based on long-term temperature monitoring data is proposed in this paper, which including the following main steps: (1) Preprocessing of the original data to correct the singular zero value and complement the missing values, (2) Smoothing of the processed data in order to automatically extract the beginning and end points of every temperature rising stage of axles, (3) Establishment of the calculation method of temperature rising rate, and evaluating the health sate of axles based on the temperature rising rate. Finally, the proposed method is validated based on the data from a test line, the results demonstrate the effectiveness and practicability of the method.

关键词： Axles Temperature distribution Aging Safety Data mining Fault diagnosis Monitoring

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Research on speed and tension multivariable system of reversible cold strip mill based on ILQ theory

Research on speed and tension multivariable system of revers...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Gang Zheng Xiao-Li Chao Zhe Yang Fu-Caiqian Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Xi'an University of Technology Xi'an Shaanxi CN

ISBN: (纸本)9781467372220

For the speed and tension system of reversing cold strip mill with multivariable, uncertainties and strong coupling, a new method of inverse linear quadratic (ILQ) is proposed. Firstly, a relatively complete mathematical model for the speed and tension multivariable coupling system is established by using the mechanism modeling method. Then according to the state-space expression of the controlled system and aspirations closed-loop poles, optimal feedback matrix is obtained by ILQ method. Instead of choosing the weighted matrix simplifies the design process, and the asymptotic behavior of the system is robust. Simulation results show the effectiveness of this method.

关键词： Reversible cold strip mill Speed and tension multivariable control system inverse linear quadratic (ILQ) pole-assignment cold strip Cold strip mills pole assignment design process multiple input multiple output systems tension speed Aspirations (Psychology) system behavior asymptotic behavior

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Cover Feature: Ultrasensitive, Sustainable, and Selective Electrochemical Hydrazine Detection by ALD-Developed Two-Dimensional WO3(ChemElectroChem 2/2018)

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ChemElectroChem 2017年第2期5卷

作者： Zihan Wei Zhenyin Hai Mohammad Karbalaei Akbari Prof. Jie Hu Lachlan Hyde Prof. Stephen Depuydt Prof. Francis Verpoort Prof. Serge Zhuiykov Ghent University Global Campus Department of Applied Analytical & Physical Chemistry 119 Songdomunhwa-ro Yeonsu-gu Incheon South Korea Micro and Nano System Research Center Key Lab of Advanced Transducers and Intelligent Control System (Ministry of Education) & College of Information Engineering Taiyuan University of Technology Taiyuan 030024 Shanxi PR China Factory of the Future Swinburne University of Technology Hawthorn Australia National Research Tomsk Polytechnic University Lenin Avenue 30 634050 Tomsk Russian Federation Laboratory of Organometallics Catalysis and Ordered Materials State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Chemical and Material Engineering Wuhan University of Technology Wuhan P.R. China

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Sentence-length informed method for active learning based resource-poor statistical machine translation 3

Sentence-length informed method for active learning based re...

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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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Large scale online kernel learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Jing Lu Steven C. H. Hoi Jialei Wang Peilin Zhao Zhi-Yong Liu Google MPI for Intelligent Systems School of Information Systems Singapore Management University Singapore Department of Computer Science University of Chicago Chicago IL Institute for Infocomm Research A*STAR Connexis Singapore State Key Lab of Management and Control for Complex System Chinese Academy of Sciences Beijing China

In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nyström Online Gradient Descent (NOGD) algorithm that applies the Nyström method to approximate large kernel matrices. We explore these two approaches to tackle three online learning tasks: binary classification, multi-class classification, and regression. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches.

关键词： kernel approximation large scale machine learning online learning

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