This paper presents a sliding-mode-based diagonal recurrent cerebellar model articulation controller (SDRCMAC) for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. Sliding mode technology is used to ...
This paper presents a sliding-mode-based diagonal recurrent cerebellar model articulation controller (SDRCMAC) for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. Sliding mode technology is used to reduce the dimension of the control system. Two learning stages are adopted to train the SDRCMAC and to improve the stability of the control system. Lyapunov stability theorem and Barbalat's lemma are adopted to guarantee the asymptotical stability of the system. Performance is illustrated on a two-link robotic control and motor control of the human arm in the sagittal plane.
A resource-constrained transport task scheduling problem (RCTTSP) with two optimal objectives was considered, and a multi-objective hybrid genetic algorithm (HGA) was proposed. The proposed algorithm used the serial s...
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A resource-constrained transport task scheduling problem (RCTTSP) with two optimal objectives was considered, and a multi-objective hybrid genetic algorithm (HGA) was proposed. The proposed algorithm used the serial scheduling method to initialize the population and evaluated the individual. It used the weighted sum method and the rank-based fitness assignment method to assign the individual fitness. Firstly, this paper described the multi-objective RCTTSP and presented the principle of the HGA, and then developed the algorithm to implement several experimental cases with different problem size;lastly the effectiveness and efficiency of the algorithm were compared. The numerical result indicated that the proposed multi-objective HGA can resolve the proposed multi-objective RCTTSP efficiently.
Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clusterin...
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Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm to optimize two complementary clustering objectives: one based on compactness in the same cluster, and the other based on connectedness of different clusters. The output of the first phase is a set of Pareto solutions, which correspond to different tradeoffs between two clustering objectives, and different numbers of clusters. In the second phase, we make use of the meta-clustering algorithm (MCLA) to combine all the Pareto solutions to get the final segmentation. The segmentation results are evaluated by comparing with three known algorithms: K-means, fuzzy K-means (FCM), and evolutionary clustering algorithm (ECA). It is shown that MECEA is an adaptive clustering algorithm, which outperforms the three algorithms in the experiments we carried out.
An efficient image denoising algorithm is introduced. Firstly, image pixels are classified into noisy pixels and noise-free pixels by four directional operators. Then an adaptive weighted median filter is designed to ...
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An efficient image denoising algorithm is introduced. Firstly, image pixels are classified into noisy pixels and noise-free pixels by four directional operators. Then an adaptive weighted median filter is designed to remove and restore the detected noisy pixels and keep the noise-free ones unchanged. Experimental results indicate that the proposed algorithm preserves image details well while removing impulsive noise efficiently, and its filtering performance is significantly superior to the classical median filter and some other typical and recently developed improved median filters.
We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems ...
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We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems are used in two versions: as devices computing functions and as devices generating sets of numbers, with two ways of encoding the result of a computation. As devices of computing functions, if we associate the result with the distance between the first two spikes emitted by the output neuron, we produce a universal computing spiking neural P system with exhaustive use of rules (without delay) having 125 neurons; if we introduce the usual way of defining the result of a computation in membrane systems to encode the result, namely, the number of spikes emitted during a computation, then a universal computing system (without delay) with 126 neurons is also obtained in the sense of the exhaustive use of rules. For spiking neural P systems used as generators of sets of numbers, we construct a universal system (without delay) by using 128 neurons under the first way of defining the computation result, and a system (without delay) by using 127 neurons under the second way of defining the computation result.
In this paper,a new lifting scheme of directionlet transform(LDT) is presented,the corresponding multidirectional and anisotropic transform has latticebased separable filtering and subsampling along any two directions...
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In this paper,a new lifting scheme of directionlet transform(LDT) is presented,the corresponding multidirectional and anisotropic transform has latticebased separable filtering and subsampling along any two directions with rational *** design an adaptive compression algorithm based on LDT,using the quad-tree segmentation resulting optimized *** results show that our proposed compression algorithm for image coding outperforms the standard wavelet-based SPIHT and JPEG2000 both in terms of PSNR and visual quality,especially at the low-rate.
A novel way achieving geometrical reconstruction of actual human face through projecting two types of texture on face in short time is advanced. The first type texture is stripe which is used to establish parallax gri...
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A novel way achieving geometrical reconstruction of actual human face through projecting two types of texture on face in short time is advanced. The first type texture is stripe which is used to establish parallax grid between images. Taking into account of its results, the second type projecting texture is used to match by virtue of its abundant traits. After realizing geometrical reconstruction, the paper provides a general way about achieving actual texture reconstruction by the outer spherical surface surrounding object. In order to uniform color, it deals with parts of images in conjunct region and makes the color change meeting a certain function on condition of keeping their original information mostly. Results show this way can improve reconstruction quality and decrease complicacy of algorithm.
A transient, six-cylinder diesel engine model for cold test has been developed for analyzing the engine fault through the engine torque curve. The model is based on physically working cycle, thermodynamic theory and d...
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A transient, six-cylinder diesel engine model for cold test has been developed for analyzing the engine fault through the engine torque curve. The model is based on physically working cycle, thermodynamic theory and dynamics mechanism. The simulation of this model, implemented on Matlab/Simulink, can not only achieve engine faults detection before hot test, but also indicate different causes of engine faults, such as initial phase change, intake valve closing-time delay, and so on. It is shown that the diesel engine model for cold test proves its significance to improving cold test technology.
To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrö...
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To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.
In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filt...
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In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filter. The experimental results validate the proposed method perform well over a wide range of restoration image ringing levels assessment. And the proposed model has given good agreement with observer ratings obtained in subjective experiments.
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