Due to the intensity distribution of X-ray image of thick steel pipe is not uniform, the contrast is low, the noise is big, and the size, shape, position and contrast of the blowholes defects are different, it is difficult to detect various types of blowholes automatically. Aiming at the problems that the traditional defect detection algorithm has a large workload of manually marking defect data, and the edge of the weld is difficult to accurately extract and other issues, a new unsupervised learning algorithm was proposed for the detection of various blowholes defects. Firstly, fast Independent Component Analysis (ICA) was used to learn a set of independent base vectors from the steel pipe X-ray image set, and a linear combination of the base vectors was used to selectively reconstruct the test image with blowholes defect. Then, the test image was subtracted from its reconstructed image to obtain the difference image, and the various blowholes were separated from the difference image by global threshold. There were 320 images in the training set and 60 images in the test set. The average sensitivity and accuracy of the proposed algorithm were 90.5% and 99.7%. The experimental results show that the algorithm can accurately detect all kinds of blowholes defects without manual marking the data or extracting the edge of the weld.

To solve the problem of large amount of calculation and nonlinear programming in the process of service composition optimization, a Cost Benefit Coefficient (CBC) approach was proposed for Web services composition reliability optimization in the situation of a given cost investment. First, the structure patterns of service composition and related reliability function were analyzed. Furthermore, the Web service composition method of reliability calculation was proposed and a nonlinear optimization model was established accordingly. And then the cost benefit coefficient was computed through the relationship between the cost and the reliability of component services, and the optimization schemes of Web service composition were decided. According to the nonlinear optimization model, the results of optimization were computed. Finally, given cost investment, the higher reliability of the approach to optimize the reliability of Web service composition was verified through the comparison of this approach and the traditional method on the reliable data of component service. The experimental results show that the proposed algorithm is effective and reasonable for reliability optimization of Web services composition.

To distribute the score of composite service obtained from customer to each component service based on actual and historical performance of component services, Analytic Hierarchy Process (AHP) was used to calculate the distribution weight of each component service, in which a method was presented to convert Web service process into structure tree process, and the weight matrix was used to calculate the weight of each node in the tree structure. The relationship between actual Quality of Service (QoS) of component services and its advertised utility interval of QoS were taken into consideration, and through deviation function, the deviation proportion between actual QoS utility value of component service and actual QoS average utility value of all component services was calculated, meanwhile the influence on score distribution by history performance of each component service was considered. The experimental results show that actual QoS and history performance of component services have some influence on score which was distributed, and demonstrate that the proposed approach can achieve a reasonable and fair score distribution.