This paper studies distributed filtering problem of homogeneous sensor *** part of sensors can get observations of the process and consensus based filters are *** the distributed detectability and the suboptimal filte...
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ISBN:
(纸本)9781479947249
This paper studies distributed filtering problem of homogeneous sensor *** part of sensors can get observations of the process and consensus based filters are *** the distributed detectability and the suboptimal filter design are *** converting the stability problem of estimation errors of sensors under time-varying topologies to the robust stability problem of some uncertain system,two sufficient distributed detectability conditions are *** is LMI-based and the other is given by the eigenvalues of the process matrix and Laplacian matrices.A suboptimal algorithm to design the consensus based filter gains is proposed and the convergence of the algorithm is *** examples are given to illustrate the results.
In this paper, the speed regulation problem for permanent magnet synchronous motor(PMSM) systems under vector control framework is studied. A modified composite control strategy combining model reference adaptive cont...
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ISBN:
(纸本)9781479947249
In this paper, the speed regulation problem for permanent magnet synchronous motor(PMSM) systems under vector control framework is studied. A modified composite control strategy combining model reference adaptive control(MRAC)method and extended state observer(ESO) technology, called MRAC+ESO method, is proposed. controller is designed for the speed loop of the permanent magnet synchronous motor(PMSM) to improve the performance of the system. The stability analysis, simulation and experimental results are presented to show the effectiveness of the proposed control method.
The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to ***,using the dynamic change of coordinates,the problem of global state feedback stabilization is solved for a...
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The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to ***,using the dynamic change of coordinates,the problem of global state feedback stabilization is solved for a class of time-delay systems under a type of nonhomogeneous growth *** the aid of an appropriate Lyapunov-Krasovskii functional and the adaptive strategy used in coordinates,the closed-loop system can be globally asymptotically stabilized by the dynamic linear state feedback *** growth condition in perturbations are more general than that in the existing *** correctness of the theoretical results are illustrated with an academic simulation example.
Reasonable timing design for traffic light can induce and maintain the transportation systems in good order. How to allocate the time are the keys. In the paper, the theory of time-varying universe is used to describe...
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Reasonable timing design for traffic light can induce and maintain the transportation systems in good order. How to allocate the time are the keys. In the paper, the theory of time-varying universe is used to describe the circle time, and corresponding fuzzy sets on the universe are also discussed to modeling the situation of traffic flow, then the parallel traffic management and control methods which are dynamic with the time change are presented. A simulation example are provided to analyze the linguistic dynamic evolution of timing design of traffic light when the traffic flow is change with time-varying for an intersection.
This paper presents an imaging method for 360-degree panoramic view based on four wide angle *** order to complete the image mosaic,all the parameters such as focal length,principal point and distortion coefficients,e...
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ISBN:
(纸本)9781479970186
This paper presents an imaging method for 360-degree panoramic view based on four wide angle *** order to complete the image mosaic,all the parameters such as focal length,principal point and distortion coefficients,etc are calibrated by our proposed calibration ***,our approach does not adopt the scheme which stitching all the images to the surrounding view after distortion *** proposed method directly calculates the mapping relationship between the wide-angle lens images and cylindrical projection images to generate lookup tables which can greatly simplifies the computation and reduces the loss of information in each ***,panoramic image is composed by image registration and image *** results show that this method is valid.
K-means clustering has been extremely popular in scene image classification. However, due to the random selection of initial cluster centers, the algorithm cannot always provide the most optimal results. In this paper...
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K-means clustering has been extremely popular in scene image classification. However, due to the random selection of initial cluster centers, the algorithm cannot always provide the most optimal results. In this paper, we develop a density-based k-means clustering. First, we calculate the density and distance for each feature vector. Then choose those features with high density and large distance as initial cluster centers. The remaining steps are the same with k-means. In order to evaluate our proposed algorithm, we have conducted several experiments on twoscene image datasets: Fifteen Scene Categories dataset and UIUC Sports Event dataset. The results show that our proposed method has good repeatability. Compared with the traditional k-means clustering, it can achieve higher classification accuracy when applied in multiclass scene image classification.
The problem of tracking control for stochastic nonlinear systems is investigated in this paper. Because of the randomness and nonlinearity of stochastic nonlinear systems, the existing methods are sometimes difficult ...
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ISBN:
(纸本)9781479999767
The problem of tracking control for stochastic nonlinear systems is investigated in this paper. Because of the randomness and nonlinearity of stochastic nonlinear systems, the existing methods are sometimes difficult to achieve the desired tracking performance. In this paper, a new network controller (multi-dimensional Taylor network) is proposed, which only relies on the output of system. Firstly we give the structure of multi-dimensional Taylor network (MTN), and then prove the MTN has a good approximation performance. Secondly, Design a MTN control strategy relying on the system output, which will guarantee the tracking of system output to desired output. An example is given to illustrate the effectiveness of the proposed design approach.
The control input item is added to constitute the nonlinear dynamic model, on the basis of the original multi-dimensional Taylor network in this paper. And this nonlinear dynamic model is used to optimally control MIS...
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ISBN:
(纸本)9781479999767
The control input item is added to constitute the nonlinear dynamic model, on the basis of the original multi-dimensional Taylor network in this paper. And this nonlinear dynamic model is used to optimally control MISO nonlinear system only by output feedback without the disturbance estimation of the system or needing the state observer. The back-propagation algorithm is used to train the parameters of the multi-dimensional network with the control input item. Through the simulation, it is demonstrated the multi-dimensional Taylor network used as the optimal MISO nonlinear system tracking controller is effective.
Multi-variable systems widely exist in the practical engineeringcontrolsystems whose performances are always severely interrupted by strong disturbances including unmodeled dynamics, parameter variations, couplings ...
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ISBN:
(纸本)9781479947249
Multi-variable systems widely exist in the practical engineeringcontrolsystems whose performances are always severely interrupted by strong disturbances including unmodeled dynamics, parameter variations, couplings and external disturbances. Disturbance observer(DOB) is known as an effective technique to estimate disturbances and has been extensively applied for feed-forward compensation design in the presence of disturbances. Yet many disturbance observer techniques in previous literature are just used for single-input-single-output(SISO) systems or the DOBs can be applied in the multi-variable systems, but the DOBs are still SISO DOBs. A decoupled robust multi-input-multi-output neural network disturbance observer(MNNDOB) is designed for the multi-input-multi-output(MIMO) systems. Simulation results on the mixing tank show that the proposed method has better disturbance estimation performance when there are severe model mismatches compared with the MIMO linear disturbance observer.
Convolutional neural network (CNN) has achieved great success in many vision tasks. A key to this success is its ability to powerful automatically learns both high-level and low-level features. In general, low-level f...
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ISBN:
(纸本)9781479958306
Convolutional neural network (CNN) has achieved great success in many vision tasks. A key to this success is its ability to powerful automatically learns both high-level and low-level features. In general, low-level features have a small size of receptive fields and appear multiple times in different locations of objects, while high-level semantic features have a relatively large size of receptive fields and only appear once in a specific location of objects. However, traditional CNN treats these two kinds of features in the same manner, i.e., learning them by the convolution operation, which can be approximately considered as cumulating the probabilities that a feature appears in different locations. This strategy is reasonable for low-level features but not for high-level semantic ones, especially in the case of pedestrian detection, where a local feature can be shared by different locations but a semantic part, e.g., a head, only appears once for a human. To jointly model the spatial structure and appearance of high-level semantic features, we propose a new module to learn spatially weighted max pooling in CNN. The proposed method is evaluated on several pedestrian detection databases and the experimental results show that it achieves much better performance than traditional CNN.
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