state recognition in disconnecting switches is important during substation automation. Here, an effective computer vision-based automatic detection and state recognition method for disconnecting switches is proposed. ...
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state recognition in disconnecting switches is important during substation automation. Here, an effective computer vision-based automatic detection and state recognition method for disconnecting switches is proposed. Taking advantage of some important prior knowledge about a disconnecting switch, the method is designed using two important features of the fixed-contact facet of such disconnecting switches. First, the Histograms of Oriented Gradients (HOG) of the fixed-contact are used to design a Linear Discriminant Analysis (LDA) target detector to position the disconnecting switches and distinguish their loci against a usual cluttered background. Then a discriminative Norm Gradient Field (NGF) feature is used to train the Support Vector Machine (SVM) state classifier to discriminate disconnecting switch states. Finally, experimental results, compared with other methods, demonstrate that the proposed method is effective and achieves a low miss rate while delivering high performance in both precision and recall rate. In addition, the adopted approach is efficient and has the potential to work in practical substation automation scenarios.
Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text reco...
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Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models. For each category of characters, we build a part-based tree-structured model (TSM) so as to make use of the character-specific structure information as well as the local appearance information. The TSM could detect each part of the character and recognize the unique structure as well, seamlessly combining character detection and recognition together. As the TSMs could accurately detect characters from complex background, for text localization, we apply TSMs for all the characters on the coarse text detection regions to eliminate the false positives and search the possible missing characters as well. While for word recognition, we propose a normalized pictorial structure (PS) framework to deal with the bias caused by words of different lengths. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods both for text localization and word recognition. (C) 2014 Elsevier Ltd. All rights reserved.
This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterativ...
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This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.
This paper is devoted to the mechatronic design and implementation of a dolphin-like robot capable of fast swimming. In the context of multiple coordinated control surfaces, a set of serially connected flapping module...
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This paper is devoted to the mechatronic design and implementation of a dolphin-like robot capable of fast swimming. In the context of multiple coordinated control surfaces, a set of serially connected flapping modules is responsible for dorsoventral oscillations, an internal moving slider for pitch control and a yaw joint for lateral turns. To improve the swimming speed, an updated modular slider-crank-based flapping mechanism that fully capitalizes on the continuous high-speed rotation of the DC motor is proposed and constructed. With the proposed mechanisms, the resulting dolphin robot achieved a high level of propulsive speed, largely illustrating the validity of the present design scheme.
In this paper, a direct adaptive state-feedback control approach is developed for a class of nonlinear systems in discrete-time (DT) domain. We study MIMO unknown nonaffine nonlinear DT systems and employ a two-layer ...
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In this paper, a direct adaptive state-feedback control approach is developed for a class of nonlinear systems in discrete-time (DT) domain. We study MIMO unknown nonaffine nonlinear DT systems and employ a two-layer NN to design the controller. By using the presented method, the NN approximation is able to cancel the nonlinearity of the unknown DT plant. Meanwhile, pretraining is not required, and the weights of NNs used in adaptive control are directly updated online. Moreover, unlike standard NN adaptive controllers yielding uniform ultimate boundedness results, the tracking error is guaranteed to be uniformly asymptotically stable by utilizing Lyapunov's direct method. Two illustrative examples are provided to demonstrate the effectiveness and the applicability of the theoretical results. Copyright (c) 2014 John Wiley & Sons, Ltd.
Three-dimensional displaytechnologies based on lenticular sheet overlaid onto spatial light modulator screen have been studied for decades. However, the quality of these displays still suffers from insufficient number...
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Three-dimensional displaytechnologies based on lenticular sheet overlaid onto spatial light modulator screen have been studied for decades. However, the quality of these displays still suffers from insufficient number of views and zone-jumping between views. We present herein a subpixel multiplexing method in this paper. We propose to split mapping and alignment into two separate tasks, processed in parallel threads. Alignment thread deals with the task of computing the geometrical relationship between lenticular sheet and Liquid Crystal Display (LCD) panel for multiplexing. Afterwards, we conduct the multiplexing procedure through a box-constrained integer least squares algorithm. After multiplexing, each subpixel aggregated on the lenticular sheet is a multiplexing one that mixes up a number of subpixels in local region on the LCD plane. As a result, we multiplex subpixels on the synthetic image up to 27 views with a resolution of 1080 x 1920 and the rendering speed is 73.34 frames per second (fps).
In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities w...
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In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.
In this paper, a novel strategy is established to design the robust controller for a class of continuous-time nonlinear systems with uncertainties based on the online policy iteration algorithm. The robust control pro...
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In this paper, a novel strategy is established to design the robust controller for a class of continuous-time nonlinear systems with uncertainties based on the online policy iteration algorithm. The robust control problem is transformed into the optimal control problem by properly choosing a cost function that reflects the uncertainties, regulation, and control. An online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman (HJB) equation by constructing a critic neural network. The approximate expression of the optimal control policy can be derived directly. The closed-loop system is proved to possess the uniform ultimate boundedness. The equivalence of the neural-network-based HJB solution of the optimal control problem and the solution of the robust control problem is established as well. Two simulation examples are provided to verify the effectiveness of the present robust control scheme.
In this paper, the robust decentralized stabilization of continuous-time uncertain nonlinear systems with multi control stations is developed using a neural network based online optimal control approach. The novelty l...
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In this paper, the robust decentralized stabilization of continuous-time uncertain nonlinear systems with multi control stations is developed using a neural network based online optimal control approach. The novelty lies in that the well-known adaptive dynamic programming method is extended to deal with the nonlinear feedback control problem under uncertain and large-scale environment. Through introducing an appropriate bounded function and defining a modified cost function, it can be observed that the decentralized optimal controller of the nominal system can achieve robust decentralized stabilization of original uncertain system. Then, a critic neural network is constructed for solving the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal system in an online fashion. The weights of the critic network are tuned based on the standard steepest descent algorithm with an additional term provided to guarantee the boundedness of system states. The stability analysis of the closed-loop system is carried out via the Lyapunov approach. At last, two simulation examples are given to verify the effectiveness of the present control approach.
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