2009, Vol.14, No.9

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

The field of the web service orchestration introduced to generate a valuable service by reusing single services. Recently, it suggests rule-based searching and composition by the AI(Artificial Intelligence) instead of simple searching or orchestration based on the IOPE(Input, Output, Precondition, Effect) to implement the Semantic web as the web service of the next generation. It introduce a AOP programming paradigm from existing object-oriented programming paradigm for more efficient modularization of software. In this paper, we design a dynamic web service orchestration and invocation scheme applying Aspect-Oriented Programming (AOP) and Reflection for Semantic web. The proposed scheme makes use of the Reflection technique to gather dynamically meta data and generates byte code by AOP to compose dynamically web services. As well as, our scheme shows how to execute composed web services through dynamic proxy objects generated by the Reflection. For performance evaluation of the proposed scheme, we experiment on search performance of composed web services with respect to business logic layer and user view layer.

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System (3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using MNIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

Color polarity classification is a process to determine whether the color of text is bright or dark and it is prerequisite task for text extraction. In this paper, we propose a color polarity method to extract text region. Based on the observation for the text and background regions, the proposed method uses the ratios of sizes and standard deviations of bright and dark regions. At first, we employ Otsu's method for binarization for gray scale input region. The two largest segments among the bright and the dark regions are selected and the ratio of their sizes is defined as the first measure for color polarity classification. Again, we select the segments that have the smallest standard deviation of the distance from the center among two groups of regions and evaluate the ratio of their standard deviation as the second measure. We use these two ratio features to determine the text color polarity. The proposed method robustly classify color polarity of the text, which has shown by experimental result for the various font and size.