2005, Vol.10, No.5

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

Multiple sequence alignment(MSA) is a fundamental technique of DNA and protein sequence analysis. Biological sequences are aligned vertically in order to show the similarities and differences among them. In this paper, we propose an efficient group alignment method, which is based on clustering divergency, to perform the alignment between two groups of sequences. The proposed algorithm is a clustering divergence(CDMS)-based multiple sequence alignment and a top-down approach. The algorithm builds the tree topology for merging. It is also based on the concept that two sequences having the longest distance should be spilt into two clusters. We expect that our sequence alignment algorithm improves its quality and speeds up better than traditional algorithm Clustal-W.

Recently, there are a lot of endeavor to implement a fuzzy theory to artificial neural network. Goh proposed the fuzzy single layer perceptron algorithm and advanced fuzzy perceptron based on the generalized delta rule to solve the XOR problem and the classical problem. However, it causes an increased amount of computation and some difficulties in application of the complicated image recognition. In this paper, we propose an enhanced fuzzy single layer perceptron using the dynamic adjustment of threshold. This method is applied to the XOR problem, which used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for image application. In a result of experiment, it does not always guarantee the convergence. However, the network show improved the learning time and has the high convergence rate.

With the rapidly growing information, the research on the efficient information retrieval is increasing. Most of the retrieval systems for domestic cultural assets on the web have adopted a keyword-based search method. Those systems have required users to know the exact information about cultural assets such as name, keyword, etc. However, it is not easy to search the cultural assets with little information or only a remembrance of the shape. In this paper, we propose the retrieval system for cultural assets using both ontology-based and sketch-based search method to solve the problems of existing systems. Our retrieval system allows users to use both text and sketch for a query regardless of the type of information about cultural assets and to search in results using the ontology.