To reduce the time complexity of situational element acquisition and cope with the low detection accuracy of small class samples caused by imbalanced class distribution of attack samples in large-scale networks, a situation element extraction mechanism based on deep auto-encoder network was proposed. In this mechanism, the improved deep auto-encoder network was introduced as basic classifier to identify data type. On the one hand, in the training of the auto-encoder network, the training rule based on Cross Entropy (CE) function and Back Propagation (BP) algorithm was adopted to overcome the shortcoming of slow weights updating by the traditional variance cost function. On the other hand, in the stage of fine-tuning and classification of the deep network, an Active Online Sampling (AOS) algorithm was applied in the classifier to select the samples online for updating the network weights, so as to eliminate redundancy of the total samples, balance the amounts of all sample types, improve the classification accuracy of small class samples. Simulation and analysis results show that the proposed scheme has a good accuracy of situation element extraction and small communication overhead of data transmission.

Focusing on the issue that the Kernelized Correlation Filter (KCF) tracking algorithm has poor performance in handling scale-variant target, a multi-scale tracking algorithm called Scale-KCF (SKCF) based on Correlation Filter (CF) and multi-scale image pyramid was proposed. Firstly, the occlusion status of the target was got through the response of the conventional KCF algorithm's classifier. The multi-scale image pyramid was built for the occluded target. Secondly, the scale information of the target was obtained by calculating the correlation filter's maximum response on the multi-scale image pyramid. Finally, the appearance model and the scale model of the target were updated with the fresh target. The experimental results on comparison with some state-of-the-art trackers such as Structured Output tracking with kernel (Struck), KCF, Tracking-Learning-Detection (TLD) and Multiple Instance Learning (MIL) demonstrate that the proposed tracker of SKCF achieves the best accuracy and overlap rate than other algorithms. Meanwhile, the proposed tracker can be widely used in target tracking and achieve high precise target tracking.

The space high-energy particle radiation has a serious influence on the reliability of the space computation. Effective radiation hardening measures must be taken to overcome this problem. Compared with the use of radiation hardening devices, using the soft reinforcement commercial devices can enjoy the advantages of high performance, low cost, fast development and so on. However, the present research on soft reinforcement is mainly suitable for the application level, while there is very little research on soft reinforcement methods for the operating system. A control flow checking method for the on-board operating system was proposed to solve this problem. Taking account of the characteristics of the on-board operating system, the proposed method regarded each thread as a sequence of function calls and monitored the thread execution through inserting test statements in the entry and exit of a function to achieve the control flow error detection. The experimental results indicate that the proposed method can increase the control flow fault coverage of the on-board operating system by 25%.