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IEEE International Workshop on Open-source Software for Scientific Computation (OSSC)

作者： Jing Gao Baoyong Zhao Information College Capital University of Economics and Business Beijing China School of Automation & Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education China

ISBN: (纸本)9781612844923

Nowadays, decision tree is widely used as one of the most powerful tools in data mining. However, to construct an optimization decision tree is a complete NP problem. So a new method about how to construct decision tree, which is based on association rule mining, is proposed in this paper. Firstly, approximate exact rule with high reliability is defined. Secondly new attributes are generated from the approximate exact rule. And then its evaluation method is discussed in detail. Thirdly, the decision tree is constructed with both the new generated attributes and its original data. Finally, after comprehensive analysis, experimental results show that this new method has higher accuracy than any other old method.

关键词： Association rules Decision trees Approximation algorithms Classification algorithms Itemsets Accuracy

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Dynamic Optimal Scheduling Method and Its Application for Converter Fault in Steelmaking and Continuous Casting Production process

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IFAC Proceedings Volumes 2011年第1期44卷 9506-9511页

作者： Shengping Yu Tianyou Chai Hong Wang Xinfu Pang Binglin Zheng State Key Laboratory of Integrated Automation for Process Industry (Northeastern University) Shenyang China 110004 (Tel: +86-24-83689665-8013) National Metallurgical Automation Engineering Technology Research Center Northeastern University Shenyang China 110004 Control Systems Centre School of Electrical and Electronic Engineering the University of Manchester P O Box 88 Manchester M60 1QD UK

Abstract In steelmaking and continuous casting (SMCC) production process, converter fault can lead to unexpected changes to the pre-specified converter-continuous caster production mode so that the original scheduling plan becomes unrealizable. In this paper, the dynamic scheduling problem in response to converter fault is firstly analyzed. This is then followed by the establishment of a novel multi-objective nonlinear programming model (MONPM) by introducing the production mode parameter α, production schedule parameter β and χ. The proposed method considers changes in production mode, production schedule of charge, the interval characteristics of processing time. In specific, a two-stage dynamic optimal scheduling method is proposed including the production path planning of charges (PPP) and the production time scheduling (PTS). As a result, a dynamic optimal scheduling software system (DOSSS) is developed and is successfully applied to the scheduling of the largest iron and steel company (BaoSteel) in China. The real-time application shows that the proposed method can efficiently reduce scheduling time, significantly increase the outputs of converters and dramatically shorten the redundant waiting time for molten steel.

关键词： steelmaking and continuous casting dynamic optimization production mode nonlinear programming scheduling system

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Optimization research of genetic neural network based on Scilab

Optimization research of genetic neural network based on Sci...

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IEEE International Workshop on Open-source Software for Scientific Computation (OSSC)

作者： Baoyong Zhao Yingjian Qi Xing Tao School of Automation & Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education China Science School Communication University of China Beijing China Beijing Research Center of Urban System Engineering Beijing China

ISBN: (纸本)9781612844923

Radial basis function (RBF) network is one of the significant neural networks. It has been used successfully in various fields. But in RBF network approximation algorithm, the initial value of the network weights, Gauss function center vector and broad-based vector is not easy to determine, and when these parameter choice is undeserved, RBF network approximation precision will decline and even the serious consequences of network spread will be produced. By using genetic algorithm in this paper, which can better realize RBF network parameter optimization, thereby increasing the accuracy of approximation. Scilab is open source software and has good simulation capabilities. Experiments using Scilab shows that the optimization method of genetic neural network is feasible and results are satisfied.

关键词： Genetic algorithms Radial basis function networks Optimization Approximation methods Vectors Biological cells Genetics

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A new double frequency exciting mode for the electromagnetic flowmeter

A new double frequency exciting mode for the electromagnetic...

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International Conference on Electric Information and control engineering (ICEICE)

作者： Shizhong Tang Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University TJU Tianjin China

ISBN: (纸本)9781424480364

A new double frequency and rectangular waveform exciting mode is proposed, as to overcome the disadvantage of the current mode. The designed waveform is made up of two kinds of frequencies, the high frequency of 75Hz and the low frequency of 6.25Hz. The high frequency is used to reduce the interference from flow noise. The low frequency is introduced to keep the stabilization of zero. The design is established by using the FPGA and DDS technology. A 10-bit D/A converter and WCS circuit are adopted to change the voltage signal from FPGA for a current one. The design consists of a power amplification circuit as well, which has a function of magnifying the output current and boosting the ability of load. The characteristics of the new exciting mode are analyzed in details, and the design of the hardware and software is introduces. The experimental result indicates that once the new exciting mode is established, the error of differential inference and orthogonal inference can be reduced greatly. Besides, the measurement accuracy is improved at the same time.

关键词： Field programmable gate arrays Electromagnetics Patents Instruments Frequency measurement Automatic frequency control Electromagnetic scattering

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Generating Li-Yorke chaos in a stable continuous-time T-S fuzzy model via time-delay feedback control

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Chinese Physics B 2010年第7期19卷 141-149页

作者：孙秋野张化光赵琰 College of Information Science and Engineering Northeastern University Key Laboratory of Integrated Automation of Process Industry Ministry of EducationNortheastern University Department of Automatic Control Engineering Shenyang Institute of Engineering

This paper investigates the chaotification problem of a stable continuous-time T S fuzzy system. A simple nonlinear state time-delay feedback controller is designed by parallel distributed compensation technique. Then, the asymptotically approximate relationship between the controlled continuous-time T-S fuzzy system with time-delay and a discrete-time T-S fuzzy system is established. Based on the discrete-time T-S fuzzy system, it proves that the chaos in the discrete- time T-S fuzzy system satisfies the Li-Yorke definition by choosing appropriate controller parameters via the revised Marotto theorem. Finally, the effectiveness of the proposed chaotic anticontrol method is verified by a practical example.

关键词： chaotification T-S fuzzy model time-delay feedback Li Yorke chaos 1

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Identification of the nonlinear, multivariable behavior of mechatronic combustion engines

Identification of the nonlinear, multivariable behavior of m...

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5th IFAC Symposium on Mechatronic Systems, MECHATRONICS 2010

作者： Isermann, R. Schreiber, A. Institute of Automatic Control Laboratory of Control Engineering and Process Automation TU Darmstadt Germany

ISBN: (纸本)9783902661760

The identification of the nonlinear dynamic behavior of combustion engines on test benches is described. A method, that is based on the combination of statistical stationary engine measurement (Design of Experiments - DoE) and dynamic identification techniques for nonlinear multi-input systems is shown. The dynamics are excited by several amplitude-modulated pseudorandom-binary- signals (APRBS), which act on the engine simultaneously, in order to allow a short measurement time period. To show the applicability of the described identification and modelling methods several models for a common-rail diesel engine with exhaust gas recirculation and variable geometry turbocharger (VGT) are shown, with five input variables and the outputs nitrogen-oxide emissions, opacity, air-fuel ratio (Lambda) and exhaust gas temperature. © 2010 IFAC.

关键词： Diesel engines

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Stationary global-local emission models of a CR-diesel engine with adaptive regressor selection for measurements of airpath and combustion

Stationary global-local emission models of a CR-diesel engin...

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作者： Sequenz, Heiko Mrosek, Matthias Isermann, Rolf Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University Darmstadt Germany

ISBN: (纸本)9783902661722

A model structure for stationary emission models is presented. The emissions NOx, soot and opacity are separately modeled. Model inputs are the air mass flow rate, charge air pressure, intake temperature, the location of mass fraction burned 50% and the engine operation point. Local polynomials are trained for each operation point, defined by engine speed and injection quantity. To increase accuracy a selection algorithm of the significant regressors is presented. The local models are composed by means of weighting functions, depending on the engine operation points, to global emission models. For a rasterized operation range, the weighting can be interpreted as a linear interpolation of local models. This enables an easy implementation to common electronic control units (ECUs). Measurements from a CR-Diesel engine show the quality of the models. © 2010 IFAC.

关键词： Diesel engines

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KPCA Based Multi-Spectral Segments Feature Extraction and GA Based Combinatorial Optimization for Frequency Spectrum Data Modeling

KPCA Based Multi-Spectral Segments Feature Extraction and GA...

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IEEE Conference on Decision and control

作者： Jian Tang Tianyou Chai Wen Yu Lijie Zhao S. Joe Qin State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) Shenyang China Departamento de Control Automatico CINVESTAV-IPN Mexico City Mexico College of Information Engineering Shenyang University of Chemical Technology China The Mork Family Department of Chemical Engineering and Materials Science Ming Hsieh Department of Electrical Engineering University of Southern California Los Angeles CA 90089

ISBN: (纸本)9781612848006

Mill load (ML) estimation plays a major role in improving the grinding production rate (GPR) and the product quality of the grinding process. The ML parameters, such as mineral to ball volume ratio (MBVR), pulp density (PD) and charge volume ratio (CVR), reflect the load inside the ball mill accurately. The relative amplitudes of the high-dimensional frequency spectrum of shell vibration signals contain the information about the ML parameters. In this paper, a kernel principal component analysis (KPCA) based multi-spectral segments feature extraction and genetic algorithm (GA) based Combinatorial optimization method is proposed to estimate the ML parameters. Spectral peak clustering algorithm based knowledge is first used to partition the spectrum into several segments with their physical meaning. Then, the spectral principal components (PCs) of different segments are extracted using KPCA. The candidate input features are serial combinated with mill power. At last, GA with Akaike's information criteria (AIC) is used to select the input features and the parameters for the least square-support vector machine (LS-SVM) simultaneously. Experimental results show that the proposed approach has higher accuracy and better predictive performance than the other normal approaches.

关键词： Vibrations Feature extraction Sensors Acoustics Optimization Kernel Support vector machines

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An unsupervised grid-based approach for clustering analysis

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Science China(Information Sciences) 2010年第7期53卷 1345-1357页

作者： YUE ShiHong1, WANG JeenShing2, TAO Gao1 & WANG HuaXiang1 1School of Electrical engineering and automation, Tianjin Key laboratory of process Measurement and control, Tianjin University, Tianjin 300072, China 2Department of Electrical engineering, National Cheng Kung University, Tainan 701, China School of Electrical Engineering and Automation Tianjin Key Laboratory of Process Measurement and Control Tianjin University Tianjin China Department of Electrical Engineering National Cheng Kung University Tainan China

In recent years, the growing volume of data in numerous clustering tasks has greatly boosted the existing clustering algorithms in dealing with very large datasets. The K-means has been one of the most popular clustering algorithms because of its simplicity and easiness in application, but its effciency and effectiveness for large datasets are often unacceptable. In contrast to the K-means algorithm, most existing grid-clustering algorithms have linear time and space complexities and thus can perform well for large datasets. In this paper, we propose a grid-based partitional algorithm to overcome the drawbacks of the K-means clustering algorithm. This new algorithm is based on two major concepts: 1) maximizing the average density of a group of grids instead of minimizing the minimal square error which is applied in the K-means algorithm, and 2) using grid- clustering algorithms to thoroughly reformulate the object-driven assigning in the K-means algorithm into a new grid-driven assigning. Consequently, our proposed algorithm obtains an average speed-up about 10-100 times faster and produces better partitions than those by the K-means algorithm. Also, compared with the K-means algorithm, our proposed algorithm has ability to partition any dataset when the number of clusters is unknown. The effectiveness of our proposed algorithm has been demonstrated through successfully clustering datasets with different features in comparison with the other three typical clustering algorithms besides the K-means algorithm.

关键词： clustering grid-based algorithm K-means algorithm

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Regularizing the Gauss-Newton iterative procedure for the inverse boundary problem of ERT 6

Regularizing the Gauss-Newton iterative procedure for the in...

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6th World Congress in Industrial process Tomography

作者： Xu, Yaoyuan Dong, Feng Tan, Chao Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin300072 China

ISBN: (纸本)9780853163220

In this work, a Newton iterative algorithm is adopted to approximate the boundary of inclusions based on quadratic boundary element method. Two regularization methods are adopted, the first one is widely used Tikhonov regularization and the second one is based on Fourier decomposition in the polar coordinate. Although there are many criterions to determine the regularization parameter for the former, such as L-curve criterion, generalized cross validation (GCV) and so on, they are yet not always effective for regularizing this iterative procedure. A parameter selection is presented, which ensures the stability and also convergence performance. A skill for the selection of decomposition order for the latter method is *** resistance tomography (ERT) is a novel non-intrusive technique to image the electrical conductivity distribution. ERT image reconstruction is characterized as a severely nonlinear and ill-posed inverse problem with many unknowns. In recent years, there are a growing number of papers published which aim to determine the locations and shapes of inclusions by assuming their conductivities are piece-wise constant and isotropic. © International Society for Industrial process Tomography, 2010. All rights reserved.

关键词： Image reconstruction

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