A new method is proposed, through combining the algorithm of orthogonal discriminant linear local tangent space alignment (ODLLTSA) and the support vector machine (SVM), to improve the accuracy of recognizing door pla...
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A new method is proposed, through combining the algorithm of orthogonal discriminant linear local tangent space alignment (ODLLTSA) and the support vector machine (SVM), to improve the accuracy of recognizing door plate numbers. The feature of door plate characters is first extracted by the ODLLTSA and then this extracted feature is used to train the SVM classifier. Finally, the new plate characters are classified by the trained SVM. Using the algorithm, a high recognition rate can be achieved. Experimental results show that this method is effective and robust in the real applications.
The fault diagnosis for a class of widely used digital parallel output optical encoder were focused. After definition of the optical encoder, the main features of the optical encoder's output data were analyzed. A...
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The fault diagnosis for a class of widely used digital parallel output optical encoder were focused. After definition of the optical encoder, the main features of the optical encoder's output data were analyzed. A fault diagnosis method which did not rely on the system model where optical encoder used was proposed. The changes of optical encoder's output data were analyzed. Then, the inherent characteristics were calculated. The fuzzy logic was utilized to determine the fault type and locate the fault location. Theoretical analysis and experimental results show that this method can diagnose and isolate optical encoder fault accurately without disassembly.
This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and p...
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This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.
The ACP (Artificial societies, Computational experiments and Parallel execution) approach has provided us an opportunity to look into new methods in addressing transportation problems from new perspectives. In this pa...
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The ACP (Artificial societies, Computational experiments and Parallel execution) approach has provided us an opportunity to look into new methods in addressing transportation problems from new perspectives. In this paper, we present our works and results of applying ACP approach in modeling and analyzing transportation system, especially carrying out computational experiments based on artificial transportation systems. Two aspects in the modeling process are analyzed. The first is growing artificial transportation system from bottom up using agent-based technologies. The second is modeling environment impacts in simple-is-consistent principle. Finally, two computational experiments are carried out on one specific ATS, Jinan ATS, and numerical results are presented to illustrate the applications of our method.
Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling....
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Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.
The interval models of uncertain plants are frequently used in the field of robust control. In this paper, a novel interval model identification method based on linear programming is proposed. By certain prepossessing...
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The paper presents a theorem to show the relationship between the parameters of the Moving Average (MA) process and those of its inversed process. The theorem can be used for the parameter identification of the MA pro...
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ISBN:
(纸本)9787894631046
The paper presents a theorem to show the relationship between the parameters of the Moving Average (MA) process and those of its inversed process. The theorem can be used for the parameter identification of the MA process. It is further shown in this paper that the parameter identification of autoregressive moving average with exogenous variable model (ARMAX), based on the identification of its MA part, can be easily achieved. The approach, at first, achieves the identification of the ARX part by directly using least-square estimations to find out a straightforward relationship between estimated parameters and observed data. Then, the inversed model of the MA part is identified in a similar way. Finally, the noise variance can be computed by using identified MA parameters. Numerical simulations validate the effectiveness and efficiency of the proposed approach.
This paper presents an adaptive robust dynamic surface control (ARDSC) algorithm for the position control of DC torque motors which are modeled as third-order nonlinear systems with parametric and nonlinear uncertaint...
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A novel algorithm to solve the target tracking and formation control problem in multi-agent systems is proposed in the present work, which combines centroidal Voronoi tessellations (CVT) with consensus strategy. The a...
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ISBN:
(纸本)9787894631046
A novel algorithm to solve the target tracking and formation control problem in multi-agent systems is proposed in the present work, which combines centroidal Voronoi tessellations (CVT) with consensus strategy. The algorithm utilizes the connection information among robots to further reduce the system cost function on the basis of CVT configuration. The work load among robots can be averaged by the consensus strategy thus the tracking and formation task can be achieved. The method configures the robots on to local optimal solution which minimize the sensing error. Simulations validated the proposed approach. Comparison is drawn between the pure CVT algorithm and the method with consensus strategy.
Abstract This paper deals with the control of arbitrarily topological interconnected systems where information communicated between subsystems may be lost due to unreliable links. First, the stochastic variable that i...
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Abstract This paper deals with the control of arbitrarily topological interconnected systems where information communicated between subsystems may be lost due to unreliable links. First, the stochastic variable that is responsible for the communication status of lossy network is regarded as a source of model uncertainty. The system is modeled in the framework of linear fractional transformation with a deterministic nominal system and a stochastic model uncertainty. Then, the robust control theory is employed for system analysis. The largest probability of communication failure, tolerated by the interconnected systems keeping mean square stable, can be obtained by solving a μ synthesis optimization problem. Decentralized state feedback controllers are designed to ensure that the whole system is mean square stable for a given communication failure rate, based on the technique of linear matrix inequalities. An illustrative example is presented finally to verify the effectiveness of the proposed model and method.
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