In this paper, an improved and much stronger RNH-QL method based on RBF network and heuristic Q-learning was put forward for route searching in a larger state space. Firstly, it solves the problem of inefficiency of reinforcement learning if a given problem's state space is increased and there is a lack of prior information on the environment. Secondly, RBF network as weight updating rule, reward shaping can give an additional feedback to the agent in some intermediate states, which will help to guide the agent towards the goal state in a more controlled fashion. Meanwhile, with the process of Q-learning, it is accessible to the underlying dynamic knowledge, instead of the need of background knowledge of an upper level RBF network. Thirdly, it improves the learning efficiency by incorporating the greedy exploitation strategy to train the neural network, which has been testified by the experimental results.

Using Shanghai Tennis Masters as an example, this study seeks to explore the psychic income associated with major sports events hosting and whether the psychic income would predict the attitudes of local residents toward events hosting. In addition, the moderating effect of sport involvement on the relationship between psychic income and attitude is also tested. In this study, a questionnaire survey is adopted. The structured questionnaire was developed based on 4 parts, including the demographics of the residents, involvement in the sport event, psychic income from the sport event, and their attitudes toward the sports event, there were 47 items in total. Data were collected from the local residents of Shanghai (including 16 districts or counties), as a result, 1,302 valid questionnaires were collected. A series of statistical analyses were conducted by using SPSS25.0 and AMOS 24.0 to examine the reliability and validity of the scales and to test the hypotheses. The results showed that the event has brought a significant level of psychic income to the local community, and the perceived psychic income would predict the attitudes of the residents toward the event hosting. The moderating effect of sports involvement on the relationship between psychic income and attitude is also confirmed.

To preserve the edge, multiplicative noise removal models based on the total variation regularization have been widely studied, but they suffer from the staircase effect. In this paper, to preserve the edge and reduce the staircase effect, we develop a hybrid variational model based on the variable splitting method for multiplicative noise removal; the new model is a strictly convex objective function which contains the total variation regularization and a modified regularization term. We use the linear alternative direction method to find the minimal solution and also give the convergence proof of the proposed algorithm. Experimental results verify that the proposed model can obtain the better results for removing the multiplicative noise compared with the recent method.

Flexible structures have been widely used in many fields due to the advantages of light quality, small damping, and strong flexibility. However, flexible structures exhibit the vibration in the process of manipulation, which reduces the pointing precision of the system and causes fatigue of the machine. So, this paper focuses on the identification method for active vibration control of flexible structure. The modal parameters and transfer function of the system are identified from the step response signal based on Prony algorithm, while the vibration is attenuated by using the input shaping technique designed according to the parameters identified from the Prony algorithm. Eventually, the proposed approach is applied to the most common flexible structure, a piezoelectric cantilever beam actuated by Macro Fiber Composite (MFC). The experimental results demonstrate that the Prony algorithm is very effective and accurate on the dynamic modeling of flexible structure and input shaper could significantly reduce the vibration and improve the response speed of system.

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