Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extin...
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(纸本)9781612844879
Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extinction which regulate DE's population size in an adaptive manner. The population size is adjusted according to the online progress of fitness improvement. Two schemes of inserting new individuals are proposed to match different mechanisms respectively. The performance of these innovations is examined through the optimization of benchmark problems. The results show that the proposed adaptive population sizing strategy is efficient for improving the convergence and efficiency of the DE.
This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image...
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This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.
A plant-friendly proportional-integral-derivative （PID） controller optimization framework is proposed to make tradeoffs among set-point response,controller output variations and *** objective function is chosen as t...
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A plant-friendly proportional-integral-derivative （PID） controller optimization framework is proposed to make tradeoffs among set-point response,controller output variations and *** objective function is chosen as the weighted sum of the integral of squared time-weighted error and the integral of squared timeweighted derivative of the control variable with respect to set-point response,while the robustness of the system is guaranteed by constraints on gain and phase *** to the complex structure of the constraints,the problem is solved by genetic *** analysis show the proposed method could efficiently reduce the controller output variations while maintaining a short settling *** on the simulation results,iterative tuning rules for the weighting factor in the objective function are obtained,which allows efficient simple proportional-integral（PI） tuning formulae to be derived.
An improved real-time target detection and tracking method was proposed based on moving foreground object in the servo monitoring system. This method extracts moving object based on adaptive mixture of Gaussian when t...
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An improved real-time target detection and tracking method was proposed based on moving foreground object in the servo monitoring system. This method extracts moving object based on adaptive mixture of Gaussian when the object comes into the video scene, then tracks the moving object using improved MeanShift algorithm, and makes it in the center of the scene. The algorithm not only ensures the real-timing of the detection and tracking, but also enlarges the sight of the camera when the object is tracked. The experiment results show that this method can automatically detect moving object and do servo tracking.
Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algo...
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Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algorithm for moving object and region detection in video using a compressive sampling is *** algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background *** algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated *** leads to a computationally efficient method and a system compared with the existing motion estimation *** experimental results show that the sampling rate can reduce to 25% without sacrificing performance.
The positioning accuracy of a short-haul target-locating system,the inverse-GPS(IGPS) ,was analyzed in detail. The relationship between IGPS and the positioning error was discussed. The multiplicative error minimal bo...
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The positioning accuracy of a short-haul target-locating system,the inverse-GPS(IGPS) ,was analyzed in detail. The relationship between IGPS and the positioning error was discussed. The multiplicative error minimal bound of the geometric dilution of precision (GDOP) about the four-base-station IGPS was also investigated. In order to clarify the practical implementation of IGPS,the multiplicative and additive error factors which affect the positioning accuracy and theoretical estimation of positioning accuracy were presented. By analyzing the experiments of locating a target's position in virtual three-dimensional areas,the positioning performance of IGPS was illustrated. The results show that the multiplicative and additive error factors should be eliminated in IGPS to improve the positioning accuracy.
New synchronization algorithm and analysis of its convergence rate for clock oscillators in dynamical network with time-delays are presented.A network of nodes equipped with hardware clock oscillators with bounded dri...
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New synchronization algorithm and analysis of its convergence rate for clock oscillators in dynamical network with time-delays are presented.A network of nodes equipped with hardware clock oscillators with bounded drift is ***,a dynamic synchronization algorithm based on consensus control strategy,namely fast averaging synchronization algorithm （FASA）,is presented to find the solutions to the synchronization *** FASA,each node computes the logical clock value based on its value of hardware clock and message *** goal is to synchronize all the nodes＇ logical clocks as closely as ***,the convergence rate of FASA is analyzed that proves it is related to the bound by a nondecreasing function of the uncertainty in message delay and network ***,FASA＇s convergence rate is proven by means of the robust optimal ***,several practical applications for FASA,especially the application to inverse global positioning system （IGPS） base station network are ***,numerical simulation results demonstrate the correctness and efficiency of the proposed *** FASA with traditional clock synchronization algorithms （CSAs）,the convergence rate of the proposed algorithm converges faster than that of the CSAs evidently.
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.
An adaptive fuzzy observer for nonlinear magnetic levitation system with uncertain friction coefficient is proposed. First, a T-S fuzzy model of the nonlinear magnetic levitation system is proposed. An observer is giv...
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An adaptive fuzzy observer for nonlinear magnetic levitation system with uncertain friction coefficient is proposed. First, a T-S fuzzy model of the nonlinear magnetic levitation system is proposed. An observer is given by LMI method and the Lyapunov method. Through adding an auxiliary variable, it relaxes the constraint of the design of observer. The proposed approach is a more relaxed condition than others. Simulation results show the effectiveness of this approach. The results show that the position of magnetic levitation system is estimated effectively with unknown friction coefficient. There is some reference value for a kind of nonlinear systems with unknown parameter.
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.
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