Shiru Qu

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On account of the random fluctuation of traffic demands or some special events, the signalized intersection system often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with some conventional means. A stochastic traffic signal control scheme, based on reinforcement learning, is introduced in the traffic(More)
The signalized intersection system often exhibits severe nonlinear and time-varying characteristic due to the random fluctuation of traffic demand or some special event, therefore, it cannot be adequately controlled with some traditional ways. The traditional reinforcement learning was extended to the fuzzy pattern with defining the fuzzy reinforcement(More)
A prediction model for short-time traffic flow series is proposed in this paper. At first, estimation of the largest Lyapunov exponent is implemented by applying small data sets method so as to validate that chaos exists in traffic flow series. Then, through properly choosing the delay time and the embedding dimension using mutual information and false(More)
In evolutionary multi-objective optimization, balancing convergence and diversity remains a challenge and especially for many-objective (three or more objectives) optimization problems (MaOPs). To improve convergence and diversity for MaOPs, we propose a new approach: clustering-ranking evolutionary algorithm (crEA), where the two procedures (clustering and(More)
A distributed approach to Reinforcement Learning in tasks of ramp metering and dynamic route guidance is presented. The problem domain, a freeway integration control application, is formulated as a distributed reinforcement learning problem. The DRL approach was implemented via a multi-agent control architecture where the decision agent was assigned to each(More)
Using the idea of digital image's information sharing, a new hiding approach for multi-image fusion based on NURBS curve is presented. The approach makes use of a k-order non-uniform rational B-spline curve with weights to hide information of one image into information of n images. These hiding schemes have more fusion parameters to act as the private keys,(More)
Motion object tracking is an important issue in computer vision. In this paper, a robust tracking algorithm based on multiple instance learning (MIL) is proposed. First, a coarse-to-fine search method is designed to reduce the computation load of cropping candidate samples for a new arriving frame. Then, a bag-level similarity metric is proposed to select(More)
Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore(More)
Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter(More)