With the continuous development of target detection technology,the vehicle detection system for road scenes has been widely used in the field of automatic driving. Compared with the traditional target detector, though the target of vehicle detection is relatively simple, two major problems need to be solved. First, the characteristics which provided to the detector are usually incomplete in some complex road scenes, and other problems such as occlusion and deformation will occur. Second, it is necessary to estimate the distance of different vehicles to ensure the car can make timely evasive action in the process of automatic driving, which means it needs depth estimation of the target area of the image. Aiming at these two problems, a vehicle detection system based on anti⁃sample generation and depth map reconstruction was proposed. A confrontation network was designed for the pre⁃training target detection network called Faster⁃RCNN, which was used to generate a large number of samples during the training process, and train the vehicle detector with these samples. According to the detection results, the vehicle distance is estimated to inform the system to make evasive action in time through the reconstruction of 3D scene and camera pose recovery depth map. The experimental results show that this detection system can improve the detection effect and estimate the distance of the target vehicle without increasing the data training sample.

This paper studies the stability of complex network systems with nonlinear coupled nodes. For the network coupying matries,the switching points of sub⁃systems discrete points in complex networks by using method that the Nimensinal complex network system transformed into the form of kronecker product.Then based on the Lyapunov function method, sufficient conditions for the stability of complex network systems are obtained. Based on the Lyapunov stability determination method, sufficient conditions for satisfying the stability of complex network systems are obtained. Finally, numerical examples are given to verify the effectiveness of the design method.

 

Turbine rotor fault diagnosis is the key to ensuring the safe operation of the steam turbine. Vibration signal analysis is widely used in turbine rotor fault diagnosis. The wavelet packet analysis method was adopted to extract the vibration signal eigenvalue as the input of BP neural network, the nonlinear mapping relationship between signal features and fault type and realizing the fault diagnosis with BP neural network was established. The simulation results show that this method can effectively diagnosis turbine rotor fault.