In this paper, a new algorithm that based on discrepancy of polygon intersection area for aircraft recognition is presented. The recognition algorithm process involves three parts: generating polygon of aircraft, placing overlapping plane polygons and computing the area of total intersecting polygons. For the purpose of getting the polygon of aircraft, the picture that was ready to be recognized has gone through a series of pre-processing and the smallest circumference polygon algorithm was used to get approximate polygon of the target contour. To make the two compared polygons have the approximate area, the similar principle was utilized. The matching procedure was divided into four steps including computing intersecting points, computing polygon intersecting sets, computing the intersecting area and getting the intersecting rate to recognize the aircraft. The data structure of algorithm is based on doubly liked list principle. A mass of simulations illustrate that the proposed algorithm is effective and reasonable.
Avionics equipment failure prediction by conventional GM (Grey Model) may yield large forecasting errors. Combining GM (1, 1) model with genetic programming algorithm, a kind of GP-GM (1, 1) forecast model was established to minimize such errors. Forecasting sequence was calculated by means of GM (1, 1) model, then genetic programming algorithm was used to modify them further, and the degradation trend prediction of characteristic parameters of avionics equipment was realized. The validity of GP-GM (1, 1) prediction model was testified by tracking and forecasting the experiment data of avionics equipment in real environment.
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