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This paper focuses on the optimization of bus signal priority with the consideration of the stochastic traffic demand. Based on a situation of the variability of traffic composition, the phase clearance reliability (PCR) value of each phase and traffic composition ratio is introduced to reflect the traffic condition at intersection. Then, a bus signal priority optimization model is proposed with the purpose of the maximum of the total vehicular departure as the optimization goal. In order to obtain the optimal solution, an improved algorithm is designed by introducing the PCR value search strategy. Finally, two cases’ study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the model can not only clear the queue under the condition of continuous dynamic traffic flow but also reduce the vehicle queuing and passenger delay.

In Ad hoc On-Demand Distance Vector Routing (AODV) protocol, numerous control messages consume a large quantity of the network resources, which deteriorates the effectiveness of AODV. In this paper, Fuzzy Velocity-based AODV (FV-AODV) is proposed for improving the reliability and effectiveness of AODV in wireless mobile ad hoc network (MANET). According to the velocities of network nodes, FV-AODV adaptively adjusts the HELLO_INTERVAL, which is the transmission time interval of two successive HELLO messages. The simulation results demonstrate that the reliability and effectiveness of FV-AODV are improved comparing with AODV.

We first generalize the wavelet-based iterative regularization method and the wavelet-based inverse scale space to shift invariant wavelet-based cases for image restoration. Then, a method to estimate the scale parameter is proposed from wavelet-based iterative regularization; different parameters with different iterations are obtained. The wavelet-based iterative regularization with the new parameter, which controls the extent of denoising more precisely in the wavelet domain, leads to iterative global wavelet shrinkage. We also obtain a time adaptive wavelet-based inverse scale space from the iterative procedure with the proposed parameter. We provide a proof of the convergence and obtain a stopping criterion for the iterative procedure with the new scale parameter based on wavelet transform. The proposed iterative regularized method obtains quite accurate results on a variety of images. Numerical experiments show that the proposed methods can efficiently remove noise and well preserve the details of images.

In this paper, we present a new class of iterative regularization methods in the setting of Besov spaces, which can be seen as generalizations of J. Xu's method. By incorporating translation invariant wavelet transform, minimizers of the new methods can be understood as the alternative to translation invariant wavelet shrinkage with weight that is dependent on the wavelet decomposition scale and the Besov smooth order. And we generalize the iterative regularization methods to a new class of nonlinear inverse scale spaces with scale and Besov smooth order dependent weight. The numerical results show an excellent denoising effect and improvement over J. Xu's method.

The liquid silane sample, prepared by liquifying pure silane gas at 88.5 K, is multiply shock-compressed to 106 GPa by means of a two-stage light-gas gun and a coolant target system. Electrical resistivity is measured for fluid silane during the period of multi-shock compression in the pressure range from 63.5 GPa to 106 GPa. It is shown that the electrical resistivity reduces to the order of 10−3–10−4 ohmm after the second shock arrived, which is two orders higher than those of typical melt metals. Though the metallization transition could not be confirmed under the loading condition of our shock experiments, its resistivity drops sharply along with the pressure rise. The phenomenon might be caused by silane decomposed during the pressure loading, due to the fact that, above 100 GPa, we find that its resistivity is close to hydrogen under the same pressure.