An implementation of adaptive filtering, composed of an unsupervised adaptive filter (UAF), a multi-step forward linear predictor (FLP), and an unsupervised multi-step adaptive predictor (UMAP), is built for sup...
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An implementation of adaptive filtering, composed of an unsupervised adaptive filter (UAF), a multi-step forward linear predictor (FLP), and an unsupervised multi-step adaptive predictor (UMAP), is built for suppressing impulsive noise in unknown circumstances. This filtering scheme, called unsupervised robust adaptive filter (URAF), possesses a switching structure, which ensures the robustness against impulsive noise. The FLP is used to detect the possible impulsive noise added to the signal, if the signal is "impulse-free", the filter UAF can estimate the clean sig- nal. If there exists impulsive noise, the impulse corrupted samples are replaced by predicted ones from the FLP, and then the UMAP estimates the clean signal. Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter, and is effective to restrict large disturbance like impulsive noise when the universal filter fails.
In order to improve real-time performance of the fire controlsystem, a ballistic resolving method based on the improved particle swarm optimization (PSO) algorithm is proposed, which improves the response speed of th...
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In order to improve real-time performance of the fire controlsystem, a ballistic resolving method based on the improved particle swarm optimization (PSO) algorithm is proposed, which improves the response speed of the system and provides a convenient extension to parallel computing on multicore platforms. First, particles are generated and initialized around the pre-estimated aiming angle. Then each particle is evaluated by an objective function composed of the ballistic differential equation etc. Finally, the position and velocity of particle swarm are updated. In order to accelerate the convergence speed, the correction angle of the global best particle obtained by Zhou's iterative and correction formula is used to guide the update of particle swarm. Experimental results show that the calculation speed is twice that of the iterative and correction method, and the convergence speed of particle swarm is 1.5 times that of the conventional PSO algorithm. Moreover, the proposed method is fully compatible with parallel computing and can further shorten execution time on multicore platforms.
The content based image retrieval (CBIR) is aimed to find the most similar images from a collection of images or a database to the query image according to the visual or semantic similarity. Current image retrieval al...
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A new concept of word-cell is introduced. It evolves from the concepts of footprint of uncertainty (FOU) of interval type-2 fuzzy set and sets-as-points geometric view of fuzzy sets. The word-cell can be graphically i...
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Aiming at the problems of registration error and synthetic movement ghost which are caused by moving objects in image mosaicing, a mosaicing algorithm for dynamic scene using multi-scale pyramid histogram of oriented ...
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Aiming at the problems of registration error and synthetic movement ghost which are caused by moving objects in image mosaicing, a mosaicing algorithm for dynamic scene using multi-scale pyramid histogram of oriented gradients (PHOG) and optimal seam is proposed. Firstly, a new feature, multi-scaled PHOG, is generated by introducing PHOG to multi-scale space corner detections. The feature is used to align images for avoiding the local impact caused by moving objects in image registration. Then, an optimal seam, guaranteeing the minimum difference in geometry and gray value, is searched by graph cut algorithm through constructing an energy function to remove the movement ghost. The experimental results show that the proposed algorithm is efficient in dealing with the problems of image mosaicing with moving objects, and the mosaicing results are satisfactory with high precision.
This paper is concerned with the problem of stability of systems with time-varying delay in a given interval. A novel Lyapunov-Krasovskii functional is proposed to obtain new stability conditions. Some triple integral...
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In view of large range change of operating status of combustion process and difficulties in building global model and timely optimizing control parameters, this paper presents dynamic model based optimization control ...
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This paper proposes a two-stage identification approach for the parameter identification of autoregressive moving average with exogenous variable (ARMAX) model. First, a bias-eliminated least squares method is employe...
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In this paper, a two-stage approach is proposed for the parameter identification of autoregressive moving average with exogenous (ARMAX) variable model. The proposed approach identifies the autoregressive part with ex...
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In this paper, a two-stage approach is proposed for the parameter identification of autoregressive moving average with exogenous (ARMAX) variable model. The proposed approach identifies the autoregressive part with exogenous variable (ARX) by a bias-eliminated least squares method, and the moving average (MA) part by utilizing the parameter relationship between MA process and its inverse. Finally, the noise variance can be computed by using the identified MA parameters. Numerical simulations validate the effectiveness of the proposed approach.
This paper is concerned with the problem of stability of systems with time-varying delay in a given interval. A novel Lyapunov-Krasovskii functional is proposed to obtain new stability conditions. Some triple integral...
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This paper is concerned with the problem of stability of systems with time-varying delay in a given interval. A novel Lyapunov-Krasovskii functional is proposed to obtain new stability conditions. Some triple integral terms are introduced in the Lyapunov-Krasovskii functional and the information on the lower bound on the delay are sufficiently used. New delay-dependent stability criteria are derived using integral inequalities and formulated in terms of linear matrix inequality (LMI). Comparing numerical examples show that the proposed criteria yield a larger upper bound on the delay for a given lower bound on the delay than existing results.
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