In this paper, an approach to design on-line robust model predictive control (RMPC) for time-delayed systems with structured uncertainties is proposed, where an uncertain time-varying input delay and multiple fixed st...
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ISBN:
(纸本)9781612844879
In this paper, an approach to design on-line robust model predictive control (RMPC) for time-delayed systems with structured uncertainties is proposed, where an uncertain time-varying input delay and multiple fixed state delays are included. By invoking the augmented state, a new system description with no input delay is introduced. For the resulting augmented systems with state delays, multi-step feedback control laws are utilized which guarantee both the satisfaction of input and state constraints and closed-loop stability. Finally a numerical example is given to illustrate the effectiveness of the proposed approach.
Almost all existing social learning models assume that there is only one type of agents in the society in order to avoid identification problem. In this work, we assume that there are various types of agents according...
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Almost all existing social learning models assume that there is only one type of agents in the society in order to avoid identification problem. In this work, we assume that there are various types of agents according to the communities they locate in. We design the rule of weight adjustment and testify that the updating rule with weight adjustment ensures learning on the whole social network. Furthermore, we show that how convergence speed is influenced by two updating-relevant parameters, and present instruction on how to attain the optimal social learning efficiency.
This paper we propose a multi-objective optimization model to deal with the capacity planning in semiconductor manufacturing system, which is a typical multi-objective problem. Unlike traditional optimization methods,...
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ISBN:
(纸本)9781424494408
This paper we propose a multi-objective optimization model to deal with the capacity planning in semiconductor manufacturing system, which is a typical multi-objective problem. Unlike traditional optimization methods, our model combines case-based reasoning with adaptive resonance theory and multi-objective genetic algorithm, in which some constraints such as maximizing profits, utilization of capacities, minimizing unmet demands etc are considered. An illustration shows how these techniques work effectively is also presented.
In this paper we propose an image-based eyeglasses try-on method, which can be used in e-commerce. The proposed method combines an improved active shape model feature extration algorithm with an image composition algo...
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ISBN:
(纸本)9781612847719
In this paper we propose an image-based eyeglasses try-on method, which can be used in e-commerce. The proposed method combines an improved active shape model feature extration algorithm with an image composition algorithm, producing a natural facial image with eyeglasses specified by users. To preserve the color fidelity of the eyeglasses on the resulting image, a new variational model was proposed. The output image was obtained by solving the modified Poisson equations. Experimental results show that the eyeglasses image was blended into the facial image seamlessly, producing natural eyeglasses try-on effects.
作者：
Ruimin BaoLanjuan ZhuDepartment of Automation
and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China
In parallel with the quick expansion of embedded devices come the rapid growth of embedded software. The commonly used software developing methods can't meet the fast growing demands of variable software when conc...
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In parallel with the quick expansion of embedded devices come the rapid growth of embedded software. The commonly used software developing methods can't meet the fast growing demands of variable software when concerning user experience, cross-platforms, upgrade, hardware resources, etc. This paper proposes a new embedded software developing architecture based on the neural network organization and component principles. With the advantages of the network's dynamic weight adjusting and flexible self-learning structure, the approach can not only enable the software better adapt to the users' attributes, but also make the software be easily to extend and upgrade.
In this paper, We have developed a rolling and non-rolling subtitle detection algorithm with temporal and spatial analysis for news video. This paper mainly includes rolling subtitle region detection, character segmen...
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In this paper, We have developed a rolling and non-rolling subtitle detection algorithm with temporal and spatial analysis for news video. This paper mainly includes rolling subtitle region detection, character segmentation for rolling subtitle, and non-rolling subtitle detection and location. While, most previous video text detection methods aimed at non-rolling subtitles, this paper proposed a detailed detection algorithm for both rolling and non-rolling subtitles in currently popular news video. The proposed algorithm makes good use of typical features of rolling subtitles, and is efficient for rolling character segmentation. For non-rolling subtitles, it utilizes edge features and connected component analysis of subtitle regions. In the experiments, we have tested the proposed algorithm with a series of news video and achieved good detection and segmentation results.
For a category of linear parameter varying (LPV) systems, i.e. LPV systems with both bounded rates of parameter variations and parameter measurement errors, the approach to design the feedback robust model predictive ...
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For a category of linear parameter varying (LPV) systems, i.e. LPV systems with both bounded rates of parameter variations and parameter measurement errors, the approach to design the feedback robust model predictive control (RMPC) is studied. The proposed controller utilizes the information on system parameters so as to improve the control performance, where the LPV system model is transferred into a sequence of future models with parameter-incremental uncertainty to include both the parameter variations and the parameter measurement. Then, a sequence of feedback control laws is designed to correspond to the sequence of future models. Since the information on system parameters is utilized and the control actions will vary corresponding to the future variations of system parameters, the better control performance can be achieved. The recursive feasibility and closed-loop stability of the proposed RMPC are also proven.
In this paper, a modified interval type-2 fuzzy T-S modeling method is applied to a heat exchange process on the equipment CE117 process Trainer. First, subtractive clustering method combined with least square method ...
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In this paper, a modified interval type-2 fuzzy T-S modeling method is applied to a heat exchange process on the equipment CE117 process Trainer. First, subtractive clustering method combined with least square method is employed to build the type-1 fuzzy T-S model. Then the type-2 fuzzy T-S model is obtained from the type-1 model through unconstrained optimization where the Nelder-Mead Simplex method is utilized. Finally, the results of the experiment prove the efficiency of the proposed algorithm.
This paper presents a method of point feature tracking and online identification using SIFT(Scale Invariant Feature Transform). The proposed approach uses the probabilistic voting method with appearance model to estim...
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This paper presents a method of point feature tracking and online identification using SIFT(Scale Invariant Feature Transform). The proposed approach uses the probabilistic voting method with appearance model to estimate the object's optimal center and apply hierarchical vocabulary tree to recognize the object. Since SIFT feature is invariant to changes caused by rotation, scaling and illumination, we can obtain higher tracking performance than the conventional approach and the probabilistic voting approach enables the track to search object efficiently. Online identification is also a challenge in video surveillance system, we use bag of words method based on hierarchical vocabulary tree to represent and match tracked objects by sampling SIFT feature online. Experimental results illustrate that the proposed approach works robustly for multi-persons tracking and identification.
During the last two decades, performance assessment of controlsystems has been receiving wide attention. However, estimation of the benchmark performance of nonlinear controlsystems still remains open. In this work,...
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During the last two decades, performance assessment of controlsystems has been receiving wide attention. However, estimation of the benchmark performance of nonlinear controlsystems still remains open. In this work, we consider an estimation problem of the benchmark performance when control loops are intervened by the complex non-differentiable nonlinearity. The considered nonlinearity includes control valve stiction of the controlsystems, as well as switching action involved by protection valves widely used in the safety-related controlsystems. Based on the idea of Lebesgue sampling, the paper proposes a novel threshold autoregressive model for unbiased estimation of the benchmark performance. Basically, the proposed method is based on the partitioning of the closed-loop routine operating data when it reaches certain thresholds. Modeling each partitioned regime as an autoregressive model, the prediction error variance as the benchmark performance can be obtained by the principle of pooled variance. A numerical example well shows the effectiveness of the proposed method.
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