Crosstalk is a primary defect in affecting the image quality of stereoscopic three-dimensional (3-D) displays. Until now, the crosstalk reduction methods either require extra devices or need tedious calibration proced...
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Crosstalk is a primary defect in affecting the image quality of stereoscopic three-dimensional (3-D) displays. Until now, the crosstalk reduction methods either require extra devices or need tedious calibration procedures, which require precise measurement on each display device. We propose herein a new method of synthesizing lenticular 3-D display based on the light field decomposition and optimization to minimize the crosstalk. The light field concept is introduced into lenticular 3-D display. Rays of multiview light field are back-projected to the LCD plane to form a synthetic image, with subpixel resolution. A weighted value considering all arriving rays is assigned for the subpixel to reduce crosstalk. We developed a new algorithm of ray's mergence and assignment for a smooth fusion of different views and crosstalk reduction. We also performed validation experiments which convincingly demonstrated that our new method is capable of reducing the crosstalk on synthetic graph. Compared with existing methods, our proposed new method is simple and effective, and implementation cost is low.
The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations ...
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The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.
Field data are important for convenient daily travel of urban residents, reducing traffic congestion and accidents, pursuing a low-carbon environment-friendly sustainable development strategy, and meeting the extra pe...
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Field data are important for convenient daily travel of urban residents, reducing traffic congestion and accidents, pursuing a low-carbon environment-friendly sustainable development strategy, and meeting the extra peak traffic demand of large sporting events or large business activities, etc. To meet the field data demand during the 2010 Asian (Para) Games held in Guangzhou, China, based on the novel Artificial systems, Computational experiments, and Parallel execution (ACP) approach, the Parallel Traffic management System (PtMS) was developed. It successfully helps to achieve smoothness, safety, efficiency, and reliability of public transport management during the two games, supports public traffic management and decision making, and helps enhance the public traffic management level from experience-based policy formulation and manual implementation to scientific computing-based policy formulation and implementation. The PtMS represents another new milestone in solving the management difficulty of real-world complexsystems.
Visual classification has long been a major challenge for computer vision. In recent years, biologically inspired visual models have raised great interests. However, most of the related studies mainly focus on learnin...
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Visual classification has long been a major challenge for computer vision. In recent years, biologically inspired visual models have raised great interests. However, most of the related studies mainly focus on learning features and representations from very large scale dataset relying on deep network architecture, which is doomed to fail with limited training samples due to its high complexity. In this paper, it is found that aside from the deep architecture, two other biologically inspired mechanisms, the pooling and nonlinear operations, also contribute to the improvement of classification performance. Based on this perspective, a new classifier of shallow architecture is proposed, in which the both mechanisms are implemented with max operation. Moreover, the architecture is derived in a probabilistic perspective to further explain the underlying rationale thereof. To train the classifier, a supervised learning algorithm is devised to minimize the hinge loss function under the new architecture. Based on the manifold assumption of continuously transforming features, an unsupervised learning algorithm is also presented to learn the features used by the classifier. Finally, the method is compared against other classifiers on several image classification benchmarks. The results demonstrate the strength of the proposed method when the training data source is limited. (c) 2014 Elsevier B.V. All rights reserved.
In this study, a neural-network-based online learning algorithm is established to solve the finite horizon linear quadratic tracking (FHLQT) problem for partially unknown continuous-time systems. An augmented problem ...
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In this study, a neural-network-based online learning algorithm is established to solve the finite horizon linear quadratic tracking (FHLQT) problem for partially unknown continuous-time systems. An augmented problem is constructed with an augmented state which consists of the system state and the reference trajectory. The authors obtain a solution for the augmented problem which is equivalent to the standard solution of the FHLQT problem. To solve the augmented problem with partially unknown system dynamics, they develop a time-varying Riccati equation. A critic neural network is used to approximate the value function and an online learning algorithm is established using the policy iteration technique to solve the time-varying Riccati equation. An integral policy iteration method and an online tuning law are used when the algorithm is implemented without the knowledge of the system drift dynamics and the command generator dynamics. A simulation example is given to show the effectiveness of the established algorithm.
Parallel-jaw gripper finds wide applications in various industrial sectors. In this paper, we mainly focus on the problem of form closure caging grasps of polygons with a parallel-jaw gripper equipped with four finger...
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Parallel-jaw gripper finds wide applications in various industrial sectors. In this paper, we mainly focus on the problem of form closure caging grasps of polygons with a parallel-jaw gripper equipped with four fingers. The form closure caging grasp is helpful for the fingers placements and contact region selections of a pneumatic gripper, as it is less sensitive to fingers misplacements. We firstly prove that there is always a path from a cage to a form closure grasp of the object that never breaks the cage, as long as the attractive region constructed in the configuration space has a local minimum. If such a minimum cannot be found, we further adjust the fingers arrangements to produce the form closure grasp. Meanwhile, we also develop an algorithm to compute the initial cage of the form closure grasp. Simulations of the grasping process witness the effectiveness of the above analysis results.
In this paper, a type of fuzzy system structure is applied to heuristic dynamic programming (HDP) algorithm to solve nonlinear discrete-time Hamilton-Jacobi-Bellman (DT-HJB) problems. The fuzzy system here is adopted ...
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In this paper, a type of fuzzy system structure is applied to heuristic dynamic programming (HDP) algorithm to solve nonlinear discrete-time Hamilton-Jacobi-Bellman (DT-HJB) problems. The fuzzy system here is adopted as a 0-order T-S fuzzy system using triangle membership functions (MFs). The convergence of HDP and approximability of the multivariate 0-order T-S fuzzy system is analyzed in this paper. It is derived that the cost function and control policy of HDP can be iterated to the DT-1-1113 solution and optimal policy. The multivariate 0-order T-S (Tanaka-Sugeno) fuzzy system using triangle MFs is proven as a universal approximator, to guarantee the convergence of the Fuzzy-HDP mechanism. Some simulations are implemented to observe the performance of the proposed method both in mathematical solution and practical issue. It is concluded that Fuzzy-HDP outperforms traditional optimal control in more complexsystems. (C) 2014 Elsevier B.V. All rights reserved.
In this paper, the neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming approach is investigated. First, the robust controller of the original ...
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In this paper, the neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming approach is investigated. First, the robust controller of the original uncertain system is derived by adding a feedback gain to the optimal controller of the nominal system. It is also shown that this robust controller can achieve optimality under a specified cost function, which serves as the basic idea of the robust optimal control design. Then, a critic network is constructed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system, where an additional stabilizing term is introduced to verify the stability. The uniform ultimate boundedness of the closed-loop system is also proved by using the Lyapunov approach. Moreover, the obtained results are extended to solve decentralized optimal control problem of continuous-time nonlinear interconnected large-scale systems. Finally, two simulation examples are presented to illustrate the effectiveness of the established control scheme. (C) 2014 Elsevier Inc. All rights reserved.
In this paper, an optimal tracking control scheme is proposed for a class of unknown discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) algorithm. First, in order to obtain the dynamics...
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In this paper, an optimal tracking control scheme is proposed for a class of unknown discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) algorithm. First, in order to obtain the dynamics of the system, an identifier is constructed by a three-layer feedforward neural network (NN). Second, a feedforward neuro-controller is designed to get the desired control input of the system. Third, via system transformation, the original tracking problem is transformed into a regulation problem with respect to the state tracking error. Then, the iterative ADP algorithm based on heuristic dynamic programming is introduced to deal with the regulation problem with convergence analysis. In this scheme, feedforward NNs are used as parametric structures for facilitating the implementation of the iterative algorithm. Finally, simulation results are also presented to demonstrate the effectiveness of the proposed scheme. (C) 2013 Elsevier B.V. All rights reserved.
In this paper, a novel iterative Q-learning method called "dual iterative Q-learning algorithm" is developed to solve the optimal battery management and control problem in smart residential environments. In ...
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In this paper, a novel iterative Q-learning method called "dual iterative Q-learning algorithm" is developed to solve the optimal battery management and control problem in smart residential environments. In the developed algorithm, two iterations are introduced, which are internal and external iterations, where internal iteration minimizes the total cost of power loads in each period, and the external iteration makes the iterative Q-function converge to the optimum. Based on the dual iterative Q-learning algorithm, the convergence property of the iterative Q-learning method for the optimal battery management and control problem is proven for the first time, which guarantees that both the iterative Q-function and the iterative control law reach the optimum. Implementing the algorithm by neural networks, numerical results and comparisons are given to illustrate the performance of the developed algorithm.
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