It's an important need for a large chemical plant to roundly and deeply evaluate the design prototype of plant human machine interaction (HMI) using in the control room. To meet this need, we propose an evaluation...
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When Multi-DSP parallel architecture transfers to distributed memory way from shared memory way, its parallelism with fine-grained become weak, and it's difficult to offer SIFT's complex computing and satisfy ...
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With the fast development of the economy, the urban traffic demands increases rapidly, Bus rapid transit (BRT) system, a new type and high efficient bus operator system and a comprehensive mass transit system between ...
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Two humanoid robots are used to play table tennis with each other. For each humanoid robot, three cameras and a computer are equipped to form a stereovision system and a monocular vision system. The stereovision syste...
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This paper illustrates a compositional deformable model for detecting vehicle and recognizing vehicle-contours. To overcome the difficulties that vehicles in an image have various sizes, shapes, colors and poses, this...
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Learning control has been an active topic of research for several decades, and is of theoretical, as well as practical, significance. Current theories and developments in learning control are discussed. Following a br...
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This paper proposes a novel method to evaluate Traffic Signal control System(TSCS) based on Artificial Transportation systems(ATS). Using this method, we can generate travel demand based on individual's activities...
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A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF is an effective statistical framework to model prior knowledge of natural image...
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Wireless sensor networks are characterized by multihop network. Some nodes in network are required to forward a disproportionately high amount of traffic and die early, leaving the unmonitored areas in network and lea...
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This paper describes an Adaptive Backstepping Neural Network (ABNN) method used for a ship course tracking control. The ship model is described by a third order nonlinear model whose parameters are unknown. The contro...
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This paper describes an Adaptive Backstepping Neural Network (ABNN) method used for a ship course tracking control. The ship model is described by a third order nonlinear model whose parameters are unknown. The control design uses estimate values of unknown parameters of the system. Then, adaptive laws of the estimation of these values have been proposed. The stability of the controlled system has been ensured by the use of a Lyapunov function. Simulation results show the effectiveness of the proposed approach and the designed controller can be applied to the ship course tracking with good performances.
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