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Journal of Materials 2017年第1期2017卷

作者： Jianlong Ji Chang Qiao Yali Liu Wendong Zhang Shengbo Sang Xing Yang Aoqun Jian Qianqian Duan Qiang Zhang Ying Liu Micro Nano System Research Center Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education College of Information Engineering Taiyuan University of Technology Taiyuan 030024 *** Advanced Coal Mine Machinery and Equipment Collaborative Innovation Center of Shanxi Province College of Information Engineering Taiyuan University of Technology Taiyuan 030024 *** MEMS Laboratory Department of Precision Instruments and Mechanology Tsinghua University Beijing 100084 *** College of Materials Science and Engineering Taiyuan University of Technology Taiyuan 030024 ***

Dispersions of individual carbon nanotubes (CNTs) are crucial for nanodevices and polymer/CNTs nanocomposites. In this paper, stable and homogenous dispersions of individual multiwalled carbon nanotubes (MWCNTs) have been synthesized. The factors influencing the dispersibility mechanism, including the surfactant concentration and the pH value, have been investigated. SEM images display the impurities sticking on MWCNTs which have been removed. The oxygen-containing groups on the surface of MWCNTs sample have been detected through FT-IR and Raman spectra. All experimental results illustrate that using fullerenols as surfactant can greatly improve the dispersibility of MWCNTs. Moreover, the prepared dispersions exhibit good stability that the sediment percentage of fullerenols-MWCNTs is only 5.2% after 5 days.

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Multi-dimensional scenario forecast for generation of multiple wind farms

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Journal of Modern Power systems and Clean Energy 2015年第3期3卷 361-370页

作者： Ming YANG You LIN Simeng ZHU Xueshan HAN Hongtao WANG Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Laboratory of Electric Vehicles Engineering of Shandong ProvinceShandong UniversityJinan 250061China Zaozhuang Power Supply Company Zaozhuang 277800China

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is *** the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power *** probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation *** copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the *** the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula *** results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

关键词： Wind power generation forecast Multidimensional scenario forecast Support vector machine(SVM) Sparse Bayesian learning(SBL) Gaussian copula Dynamic conditional correlation matrix

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Nonlinear Fermentation Process Fault Monitoring Based on JITL Strategy

Nonlinear Fermentation Process Fault Monitoring Based on JIT...

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第28届中国控制与决策会议

作者： Yakun Zhang Xuejin Gao YaFen Li XiChang Wang Pu Wang College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

PCA,as the representative of the traditional global fault monitoring algorithm,is a linear feature extraction *** global algorithm may cause a lot of false alarms in fault monitoring of fermentation process with nonlinear and multi-stage *** this paper,the Just In Time Learning(JITL) strategy is introduced into the Kernel Principal Component Analysis(KPCA) *** local model is established with the data similar to the current online *** the local model can represent the current state of the system,it is not necessary to identify the stages before *** the same time,the local KPCA model can be used to feature extract nonlinear *** data generated by the PenSimv2.0 simulation platform is used for verifying the *** results show that this method has a better effect than KPCA algorithms.

关键词： JITL KPCA Fermentation Fault Monitoring

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Fault Detection of Batch Process Based on MSICA-OCSVM

Fault Detection of Batch Process Based on MSICA-OCSVM

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第28届中国控制与决策会议

作者： Xuejin Gao Rong Ma College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

When we use the traditional multi-scale independent component analysis method to extract independent component ICA on each scale,and then,using the ICA decomposition on the reconstructed data to construct monitoring statistics,however,data of the reconstruction on the nature was already independent component,it is meaningless to extract them by *** on the shortcoming,this paper proposes a MSICA-OCSVM method that was combined with Multi-scale Independent Component Analysis(MSICA) and One-class Support Vector Machine(OCSVM) to monitor the ***,we can use the wavelet transform decomposition to monitor data at different *** then,the data was processing by threshold denoising,and was monitored on each scale extraction by using ICA independent principal ***,we can use the wavelet transform coefficients for each scale would scale back on the reconstruction of the new signal matrix(?) Finally,new OCSVM model was constructed by the reconstructed matrix *** can make the use of determined hyper-plane to construct a nonlinear statistic,and the appropriate control limits was determined by using kernel density *** is more,this method is applied to penicillin fermentation process simulation platform,the experimental results show that this method can effectively utilize the structure information data compared to traditional MSICA fault monitoring method,the failure rate of false positives,false negative rate was significantly reduced.

关键词： batch process one-class support vector machines(OCSVM) fault detection Multi-scale independent component analysis(MSICA)

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Predictive control of wind power ramping based on wind turbine load optimization

Predictive control of wind power ramping based on wind turbi...

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International Conference on Renewable Power Generation, RPG 2015

作者： Liu, Weipeng Cui, Yang Liu, Yutian Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan250061 China

ISBN: (纸本)9781785610400

A predictive control strategy considering ramping rate and power deviation is proposed, which is based on wind turbine load optimization. The wind turbine load model is established according to blade element momentum (BEM) theory, and the influence factors of wind turbine load are analysed based on this model. The control strategy is divided into 3 modules. The prediction module is based on ultra-short-term neural network wind speed prediction. The online optimization model is established with the objectives of minimizing the ramping rate, wind turbine load and power deviation. The feedback module compensates the deviation of optimal control under practical operation conditions. Simulation results show that the proposed strategy can improve the security and economy of wind turbines when dealing with ramping events.

关键词： Wind power

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An associative classification method for the operation rule extracting based on decision tree

An associative classification method for the operation rule ...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xiongwei Hu Fang Shi Zhihong Yu Yanhao Huang Guangming Lu Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan P.R. China China Electric Power Research Institute Beijing P.R. China

ISBN: (纸本)9781509054190

Along with continuous accumulation of power grid data, some big data analysis methods are used to make a judgment on the stability of power grid. However, most of them only concern about whether it is stability or not, lacking of the judgement on stability margin. And some proposed rules are difficult to understand. An improved decision tree (DT) algorithm is used in this paper to find correlative relationship between power flow state and the stability margin of system when specified faults occur and to get some comprehensible rules. Some unrelated features are eliminated through the operation experience, the principal component analysis (PCA) is used to reduce the dimension of features to get the key features which can describe the state of system. All numerical simulations are based on the changes of IEEE 39-bus node test system. The results show the rationality and feasibility of the proposed method.

关键词： Power system stability Numerical stability Stability criteria Decision trees Principal component analysis Power system transients

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Improved target tracking based on spatio-temporal learning

Improved target tracking based on spatio-temporal learning

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International Conference on Information and Automation (ICIA)

作者： Songmin Jia Dishi Zeng Tao Xu Hui Zhang Xiuzhi Li College of Electronic and Control Engineering Beijing University of Technology Beijing China Ministry of Education Engineering Research Center of Digital Community Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781509041039

Object tracking is always in the core status with the develop of robotics. In this paper, we proposed a novel tracking framework based on Spatio-Temporal Context tracking (STC) and Template-Matching algorithm(TM). STC tracking method is a fast and simplified tracking method that strongly dependent on the context region (the surrounding background of the target). Therefore, it cannot correct the tracking error by itself and even lost the target during tracking. Under this circumstance, we make a closed-loop judgment whether the movement of the target between two neighboring image sequence larger than a constant or not. If the movement larger than the constant, the template matching is employed as sample detector to correct the error or re-track the target on line. The template-matching algorithm is an efficient and fast detection algorithm that find the maximum probability point in the image that similar to the template. In order to decrease the calculating time, the search region is not the whole image but the context region of STC tracking method. What's more, after tracking and detecting we update the scale parameter to adapt the change of the target's appearance. Finally, experimental result demonstrate that our tracking method have improve the robustness of tracking.

关键词： Target tracking Context Context modeling Computational modeling Robustness Mathematical model

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Mechanical arm grasping and target recognition strategy based on the coarse-to-fine algorithm

Mechanical arm grasping and target recognition strategy base...

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International Conference on Information and Automation (ICIA)

作者： Songmin Jia Zengyue Ju Tao Xu Hui Zhang Xiuzhi Li College of Electronic and Control Engineering Beijing University of Technology Beijing China Ministry of Education Engineering Research Center of Digital Community Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781509041039

Target recognition is a fundamental research topic in the process of robot grasping. In this paper, we proposed an algorithm framework for object recognition based on local naive Bayes nearest neighbor with Kinect. With the emergence of local invariant feature detection, the method based on local invariant features gradually becomes the mainstream. Object recognition is realized through the feature matching of the model in the current scene and the models in the library based on local invariant property. Considering the number of models in the library may be as many as dozens or even hundreds, I divide the recognition process into coarse and fine recognition, the part of coarse recognition adopts the local naive Bayes nearest neighbor algorithm, just search for a number of the nearest neighbors of the object to be identified, it does not need to compare all models in the model library one by one, the computational complexity of the model increases with the number of models in the logarithmic growth, So we can effectively deal with the situation of large data in library. The process of fine recognition is a process of layers of verification, it mainly includes geometric verification, pose verification, projection verification, the model with the most matching points will be used as the final recognition result. In the end, a variety of performances were tested on the garage willow database and the grasping experiments of the robot arm demonstrate the superiority of my proposed method.

关键词： Feature extraction Object recognition Grasping Three-dimensional displays Robot sensing systems Libraries

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Evaluation method of module granularity partition for intelligent service robot based on D-S evidence theory

Evaluation method of module granularity partition for intell...

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International Conference on Information and Automation (ICIA)

作者： Songmin Jia Guoliang Zhang Boyang Li Mingchao Ding College of Electronic and Control Engineering Beijing University of Technology Beijing China Ministry of Education Engineering Research Center of Digital Community Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781509041039

The granularity partition for functional modules is a fundamental research topic in robot distributed control technology. How to evaluate the module partition scheme with different granularity, and then obtain the optimum scheme is the urgent problem. In this paper, we proposed a novel evaluation strategy for the granularity partition of functional modules in robotic system using RTM as control platform based on D-S evidence theory. The fuzzy clustering algorithm is primarily used to get the collection of granularity partition schemes for RT Components encapsulated by the platform of OpenRTM. As the two source of evidence, the indices of cohesion and coupling for the robotic system are achieved to measure the degree of module independence by analyzing the correlation matrix of RT Components. Then the Dempster's combination rule and the priority method for utility intervals are applied to obtain the optimal partition granularity. In the end, the effectiveness and progressiveness of the novel evaluation strategy are verified by applying it to the robotic 3D mapping system.

关键词： Robots Correlation Couplings Partitioning algorithms Clustering algorithms Decentralized control Middleware

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Distributed fiber temperature sensor using a laser diode modulated by a pseudo-random bit sequence

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Chinese Optics Letters 2015年第8期13卷 23-26页

作者：柴晶张明江刘毅李岚许卫鹏王云才 Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan University of Technology Institute of Optoelectronic Engineering College of Physics and OptoelectronicsTaiyuan University of Technology

We propose and demonstrate a scheme to measure the distribution of temperature along an optical fiber based on pseudo-random sequence modulation. In our experimental work, current modulation with a pseudo-random bit sequence （PRBS） is applied to a laser diode that serves as the Brillouin pump light and reference light. Because of the independence of the spatial resolution on the PRBS length~ the measurement range can be extended while maintaining high spatial resolution using a long PRBS length. Temperature-induced changes in a Brillouin frequency shift of 250 m fiber sections are clearly observed with 54 cm spatial resolution by this method.

关键词： Image resolution Modulation Optical fibers

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