User association is the problem that a wireless terminal chooses to access one serving base station. User association can be seen as the first step in wireless resource management, which has an important impact on network performance, and plays a very important role in achieving load balance, interference control, improvement of spectrum and energy efficiency. Aiming at the characteristics of multi-layer heterogeneous network including macro base stations and full-duplex small base stations, a separate multiple access mechanism was considered, which means allowing a terminal access different and multiple base stations in the uplink and downlink, so as to realize the performance improvement. On this basis, the user association problem with separation of uplink and downlink multi-access in heterogeneous network was modeled into an evolutionary game problem, in which the users act as the players to perform the resource competition with each other, the access choices of base stations are strategies in the game, and every user wants to obtain the maximum of own effectiveness by the choice of strategy. Besides, a low-complex self-organized user association algorithm was designed based on evolutionary game and reinforcement learning. In the algorithm, the user was able to adjust the strategy according to the revenue of current strategy choice, and reached an equilibrium state in the end, realizing user fairness. Finally, extensive simulations were performed to verify the effectiveness of the proposed method.
To realize automatic crack detection for aircraft skin, skin image processing and parameter estimation methods were studied based on scanning images obtained by pan-and-tilt long-focus camera. Firstly, considering the characteristics of aircraft skin images, light compensation, adaptive grayscale stretching, and local OTSU segmentation were carried out to obtain the binary images of cracks. Then, the characteristics like area and rectangularity of the connected domains were calculated to remove block noises in the images. After that, thinning and deburring were operated on cracks presented in the denoised binary images, and all branches of crack were separated by deleting the nodes of cracks. Finally, using the branch pixels as indexes, information of each crack branch such as the length, average width, maximum width, starting point, end point, midpoint, orientation, and number of branches were calculated by tracing pixels and the report was output by the crack detection software. The experimental results demonstrate that cracks wider than 1 mm can be detected effectively by the proposed method, which provides a feasible means for automatic detection of aircraft skin cracks in fuselage and wings.
To solve the problem of classification of unbalanced data sets and the problem that the general cost-sensitive learning algorithm can not be applied to multi-classification condition, an integration method of cost-sensitive algorithm based on average distance of K-Nearest Neighbor (KNN) samples was proposed. Firstly, according to the idea of maximizing the minimum interval, a resampling method for reducing the density of decision boundary samples was proposed. Then, the average distance of each type of samples was used as the basis of judgment of classification results, and a learning algorithm based on Bayesian decision-making theory was proposed, which made the improved algorithm cost sensitive. Finally, the improved cost-sensitive algorithm was integrated according to the K value. The weight of each base learner was adjusted according to the principle of minimum cost, obtaining the cost-sensitive AdaBoost algorithm aiming at the minimum total misclassification cost. The experimental results show that compared with traditional KNN algorithm, the improved algorithm reduces the average misclassification cost by 31.4 percentage points and has better cost sensitivity.