In recent years, the development of short-term traffic flow prediction technology has been the focus of many scholars. Although the existing traffic flow prediction methods perform well, they still fail to reach the level of accurate prediction. This is mainly because the model structure they adopted is simple, the factors considered are not enough, and the data processing methods they adopted are single. In this paper, a queue hybrid neural network (QHNN) model based on long short-term memory (LSTM) and gated recurrent unit (GRU), with weather weighted factor, is proposed to predict traffic flow. Queue hybrid neural network is proposed to extract the characteristics of traffic flow. The calculation formula of weather weighted factor is constructed to take more weather factors into consideration. The experimental results show that the method proposed in this paper is superior to the existing advanced models. The experimental process is more scientific because it is carried out in a step-by-step optimization way.

The deformation must be controlled during machining, especially for the thin-walled workpiece. Fixture layout and clamping force are the major two aspects that influence the degree and distribution of machining deformation. In this paper, a dual optimization model of fixture layout and dynamic clamping force has been established for machining the thin-walled workpiece. First, an optimal fixture layout is generated by considering the deformation degree and distribution. Thereafter, dynamic clamping force are optimized based on the optimal fixture layout. The finite element method is used to analyze the workpiece deformation. A genetic algorithm is developed to solve the optimization model. Finally, an example is used to illustrate that a satisfactory result has been obtained, which is far superior to the experiential one. This optimization method can reduce the machining deformation effectively and improve the distribution condition.