The network of Integrated Modular Avionics (IMA) platform has complex communication structure. When fault injection testing, it is difficult to select a appropriate test path, and there are much equivalent and invalid fault injection. According to the characteristics of the network communication structure of the IMA platform, a new fault injection strategy was proposed. Firstly, according to the requirements of real-time and certainty in the network of IMA platform, a test path optimization algorithm based on communication links was proposed, optimal test paths were generated to achieve orderly coverage of IMA platform network test tasks. Secondly, after determining the test path, a test case automatic generation model was constructed by using Colored Petri Net (CPN) modeling method, the equivalent and invalid faults were eliminated, and test cases required for each test task in the path were streamlined. The simulation results show that the proposed method is less than the traditional fault injection strategy in terms of test times and test time, so it can overcome the shortcomings of disorder and blindness in the traditional strategy and reduce the time cost of the test.

Aiming at the problems caused by intermittent faults of ground air conditioning, including low use efficiency, maintenance lag etc., a prediction method of intermittent faults which combined re-association Array Summation (AS)-Apriori with clustering K-means was raised, based on this method, delayed maintenance forecast was realized. The low efficiency problem of frequently scanning transaction database in Apriori was solved in AS-Apriori algorithm, by constructing intermittent fault arrays and giving a summation of corresponding items on them in real-time. The goal of delayed maintenance forecast is to estimate the critical region of permanent fault to arrange reasonable maintenance, which can be realized by using Gaussian distribution for the solution of maintenance wave of different intermittent fault variables and delay probability and then giving an accumulation in order. The results show that, the operational efficiency is improved, the support degree of re-association rules is upgraded by 20.656 percentage points, and more accurate prediction of intermittent failure is realized. Moreover, according to the analysis of data, the probability of forecasting maintenance-wave and delay-probability is shown as a linear distribution, which means that the high predictability of intermittent faults is more convenient to maintain and manage in advance and the formation of permanent fault is reduced.

Shadow removal is one of the most important parts of moving object detection in the field of intelligent video since the shadow definitely affects the recognition result. In terms of the disadvantage of shadow removal methods utilizing texture, a new algorithm based on Gaussian Mixture Model (GMM) and YCbCr color space was proposed. Firstly, moving regions were detected using GMM. Secondly, the Gaussian mixture shadow model was built through analyzing the color statistics of the difference between the foreground and background of the moving regions in YCbCr color space. Lastly, the threshold value of the shadow was obtained according to the Gaussian probability distribution in YCbCr color space. More than 70 percent of shadow pixels in sequence images of the experiments could be detected by the algorithm accurately. The experimental results show that the proposed algorithm is efficient and robust in object extraction and shadow detection under different scenes.