In deep learning， as the depth of Convolutional Neural Network （CNN） increases， more and more data is required for neural network training， but gene structure variation is a small sample event in large-scale genetic data， resulting in a very shortage of image data of variant genes， which seriously affects the training effect of CNN and causes the problems of poor gene structure variation detection precision and high false positive rate. In order to increase the number of gene structure variation samples and improve the precision of CNN to identify gene structure variation， a gene image data augmentation method was proposed based on GAN （Generative Adversarial Network）， namely GeneGAN. Firstly， initial genetic image data was generated by using the Reads stacking method and it was divided into two datasets including variant gene images and non-variant gene images. Secondly， GeneGAN was used to augment the variant image samples to balance the positive and negative datasets. Finally， CNN was used to detect the datasets before and after augmentation， and precision， recall and F1 score were used as measurement indicators. Experimental results show that compared with tradional augmentation method， GAN based augmentation method and feature extraction method， the F1 score of GeneGAN is improved by 1.94 to 17.46 percentage points， verifying that GeneGAN method can improve the precision of CNN to identify gene structure variation.

A new autonomous vehicle navigation model was proposed based on multi-sensor system for vehicle navigation and Global Positioning System (GPS) under complex road conditions. And the Unscented Kalman Filter (UKF) was used to overcome some security issues due to the sudden error produced by the Kalman filters with extensions, which belonged to Sigma point based sensor fusion algorithm. It is more suitable than the Kalman filters with extensions that the UKF can calculate the evaluation satisfied the requirement in vehicle navigation. Comparison experiments with the Kalman filter based on polynomial expansion were given in terms of estimation accuracy and computational speed. The experimental results show that the Sigma-point Kalman filter is a reliable and computationally efficient approach to state estimation-based control. Moreover, it is faster to evaluate the motion state of the vehicle according to the current direction situations and the feedback information of vehicle sensor, and can calculate the control input of vehicle adaptively in real time.

In view of the shortcomings of the existing pressor strategies in load testing, this paper proposed the index and linear combination pressor strategy, and the recording value detection pressor strategy. The effects of pressor strategies through the experiments were analyzed and compared. The experimental results show that the proposed method can improve the efficiency of the load testing.