2006, Vol.11, No.2

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

In this paper, a RTL binding technique and low power technology mapping consider CPLD is proposed. Allocation processing selected module consider the module calculation after scheduling process for circuit by HDL. Select CPLD for constrain after allocation. A Boolean equation is partitioned for CLB by allocated modules. The proposed binding algorithm is description using optimum CLB within a CPLD consider low power. The proposed algorithm is examined by using 16 bit FIR filter. In the case that applicate the algorithm, the experiments results show reduction in the power consumption by 43% comparing with that of non application algorithm.攀제1저자 : 김재진교신저자 : 이관형접수일 : 2006.2.3, 심사완료일 : 2006.5.18*극동정보대학 컴퓨터정보과 교수, **청주대학교 전자정보공학부 전임강사攀攀

The advent of Semantic Web has generated several methods that can access the data on the web. Thus, it is necessary to handle the data by accessing the current web ontology as well as the existing knowledge base system. Web ontology languages are RDF(Resource Description Framework), DAML-OIL, OWL(Web Ontology Language), and so on. This paper presents the creation and the method of the ontology object model that can access, represent, and process the web ontology and the existing knowledge base. Unlike the existing access approach of web ontology using the model on memory constructed by each parser, we divide the model of web ontology into three layers such as frame-based ontology layer, generic ontology layer, and functional ontology layer. Generic ontology layer represents the common vocabulary among several domains and functional ontology layer contains the dependent vocabulary to each ontology respectively. Our model gets rid of the redundancy of the representation and enhances the reusability. Moreover, it can provide the easy representation of knowledge and the fast access of the model in the application.

Millions of documents are already on the Internet, and new documents are being formed all the time. This poses a very important problem in the management and querying of documents to classify them on the Internet by the most suitable means. However, most users have been using the document classification method based on a keyword. This method does not classify documents efficiently, and there is a weakness in the category of document that includes meaning. Document classification by a person can be very correct sometimes and often times is required. Therefore, in this paper, We wish to classify documents by using a neural network algorithm and C4.5 algorithms. We used resume data forming by XML for a document classification experiment. The result showed excellent possibilities in the document category. Therefore, We expect an applicable solution for various document classification problems.