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With the rapid development of China's high-speed railway (HSR), there are also many problems. For example, in the classification and calculation of HSR transportation costs, there is a lack of reasonable cost statistical methods. There are many types of high-speed train currently running on HSR (mainly G and D trains). There are differences in the speed, load and energy consumption of different types of trains. The resulting infrastructure usage costs also vary. However, all train costs are classified and calculated uniformly in practice. This paper proposes the expenditure rate method of the transport process allocating the infrastructure use costs among types of trains. Based on data from the Beijing-Shanghai high-speed railway, the cost of infrastructure is calculated, and the calculation results can reasonably reflect the cost allocation among types of trains. It makes the cost calculation of high-speed railway more accurate and lays a foundation for environmental cost calculation.

SIFT algorithm is the most efficient and powerful algorithm to describe the features of images and it has been applied in many fields. In this paper, we propose an optimized method to realize the hardware implementation of the SIFT algorithm. We mainly discuss the structure of Data Generation here. A pipeline architecture is introduced to accelerate this optimized system. Parameters’ setting and approximation’s controlling in different image qualities and hardware resources are the focus of this paper. The results of experiments fully prove that this structure is real-time and effective, and provide consultative opinion to meet the different situations.

Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV) method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA) is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.