An online multiple target tracking method for the aerial infrared targets was proposed based on the hierarchical data association to solve the tracking difficulty caused by the high similarity, large number and large false detections of the targets in star background. Firstly, according to the characteristics of the infrared scene, the location features, gray features and scale features of the targets were extracted. Secondly, the above three features were combined to calculate the preliminary relationship between the targets and the trajectories in order to obtain the real targets. Thirdly, the obtained real targets were classified according to their scales. The large-scale target data association was calculated by adding three features of appearance, motion and scale. The small-scale target data association was calculated by multiplying the two features of appearance and motion. Finally, the target assignment and trajectory updating were performed to the two types of targets respectively according to the Hungarian algorithm. Experimental results in a variety of complex conditions show that:compared with the online tracking method only using motion features, the proposed method has the tracking accuracy improved by 12.6%; compared with the method using multi-feature fusion, the hierarchical data correlation of the proposed method not only improves the tracking speed, but also increases the tracking accuracy by 19.6%. In summary, this method not only has high tracking accuracy, but also has good real-time performance and anti-interference ability.

The deep learning model increases the number of hidden layers, which makes the model have a good effect on speech recognition, image video classification and so on. However, to establish a model suitable for a specific object, a large number of data sets are required to train it for a long time to get the appropriate weights and biases. To resolve the above problems, a sub-health diagnosis method for rolling bearing was proposed based on depth autoencoder-relevance vector machine network model. Firstly, the bearing vibration signal was collected and transformed by Fourier transform and normalization. Secondly, the improved automatic encoder, named sparse edge noise reduction autoencoder, was designed, which combined the features of sparse automatic encoder and edge noise reduction automatic encoder. Then the depth autoencoder-relevance vector machine network model was designed, in which the supervised function was used to finely tune the parameters of each hidden layer, and it was trained by Relevance Vector Machine (RVM). Finally, the final classification results were obtained according to D-S (Dempster-Shafer) evidence fusion theory. The experimental results show that the proposed algorithm can effectively improve the recognition precision of the "sub-health" state of the rolling bearing and correct the error classification.

On the private cloud platform, it cannot be flexible to monitor and distribute the virtual machine memory resources effectively using the existing methods. To solve this problem, a Memory Monitor and Scheduler (MMS) model was put forward. And the real-time monitoring and dynamic scheduling of the virtual machine memory shortage and memory free were realized by using the libvirt function library and libxc function library provided by Xen. A small private cloud platform was built using Eucalyptus with regarding one physical machine as master node and two physical machines as child nodes. In the experiments, when the state of host was in memory shortage, MMS system effectively released the memory space by starting the virtual machine migration strategy; when the memory of the virtual machine was approaching the initial maximum memory, MMS system assigned it with a new maximum memory; when the occupied memory decreased, MMS system recycled part of free memory resource, which has little effect on the performance of virtual machines if the release memory did not exceed 150MB (maximum memory is 512MB). The results show that the MMS model of private cloud platform is effective for real-time monitoring and dynamic scheduling of the memory.