• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.965-967
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• 2013

The paper proposed the DRCS(Disease Rule Creation Scheme) which generates the disease rules for efficient disease management in Healthcare system. The DRCS uses basically Rough Set Theory and computes support between each attributes and decision attributes. It creates the disease rules that judges disease after it removes the attribute which is the lowest support. Therefore, it reduces the number of disease rules and improves the exactness, compared with C4.5 algorithm.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.103-115
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• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.219-228
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• 2003

This paper presents an efficient construction method of determination rules of thematic roles from syntactic relations in Korean language processing. This process is one of the main core of semantic analysis and an important issue to be solved in natural language processing. It is problematic to describe rules for determining thematic roles by only using general linguistic knowledge and experience, since the final result may be different according to the subjective views of researchers, and it is impossible to construct rules to cover all cases. However, our method is objective and efficient by considering large corpora, which contain practical osages of Korean language, and case frames in the Sejong Electronic Lexicon of Korean, which is being developed by dozens of Korean linguistic researchers. To determine thematic roles more correctly, our system uses syntactic relations, semantic classes, morpheme information, position of double subject. Especially by using semantic classes, we can increase the applicability of the rules.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.642-650
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• 2010

Collaborative spectrum sensing allows secondary users scattered in location to work together to detect the activity of primary users and has been shown to significantly reduce the performance degradation due to fading phenomenon. Most previous works on collaborative spectrum sensing are based on the assumption that local spectrum sensing decisions of secondary users are statistically independent. However, it may not hold in some practical situations with shadowing effect. In this paper, we consider the case that the secondary users are evenly spaced in the form of a linear array and only adjacent secondary users are statistically correlated, and analyze the effect of the statistical correlation on the performance of collaborative spectrum sensing and the throughput of a cognitive radio network. Here we assumed the AND and OR fusion rules for combining the local decisions of secondary users. The analysis showed that the AND fusion rule achieves higher throughput than the OR fusion rule.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.358-360
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• 2001

본 논문에서는 의미분석을 위해서 구문관계와 의미관계를 자동으로 사상하는 규칙을 구축한다. 5 만개의 패턴을 수작업으로 사상해서 학습데이터로 만들고 이의 분석을 통해 규칙을 구축했다. 규칙에서는 의미역 결정을 위해서 가도까와 시소러스를 이용하는데, 본 논문에서는 한일 기계번역사전을 이용하여 추출한 구문 패턴을 대상으로 실험한 결과, 정확률 90%, 적용율 93.5%를 얻었다.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.330-332
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• 1998

모듈형 신경망은 인간의 정보처리 시스템이 고유한 목적이나 기능을 가진 모듈로 되어있다는 신경과학의 연구에 기반하여 제안된 모델이다. 하지만 모듈의 크기와 기능모듈간의 연결구조를 결정하는데 큰 어려움이 있다. 본 논문에서는 간단한 규칙으로 복잡한 구조를 생성해 낼 수 있는 린덴마이어-시스템을 이용하여 모듈형 신경망의 크기 및 연결구조를 만들어내는 과정에 대하여 고찰해본다. 또한, 신경망의 생성규칙을 유전자형으로 표현하고 진화 알고리즘을 적용하여 주어진 문제를 해결할 수 있는 최적의 규칙을 찾아내는 방법을 제안한다. 본 논문에서 제안한 유전자형과 진화연산은 최적화된 문법규칙 및 신경망의 구조를 만들어 낼 수 있는 가능성을 보여준다.

• Journal of KIISE
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• v.42 no.11
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• pp.1410-1422
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• 2015

In order to design a system, software developers use an object model such as the UML class diagram. Object-Relational Transformation Methodology (ORTM) is a methodology to transform the relationships that are expressed in the object model into relational database tables, and it is applied for the implementation of the designed system. Previous ORTM studies have suggested a number of transformation methods to represent one relationship. However, there is an implementation problem that is difficult to apply because the usage criteria for each transformation method do not exist. Therefore, this paper proposes a binary decision diagram-based modeling rule for each relationship. Hence, we define the conditions for distinguishing the transformation methods. By measuring the query execution time, we also evaluate the modeling rules that are required for the verification. After evaluation, we re-define the final modeling rules which are represented by propositional logic, and show that our proposed modeling rules are useful for the implementation of the designed system through a case study.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.519-529
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• 2007

In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.145-154
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• 2016

Because of the recent interest in the u-Health and development of IT technology, a need of utilizing a medical information data has been increased. Among previous studies that utilize various data mining algorithms for processing medical information data, there are studies of association rule analysis. In the studies, an association between the symptoms with specified diseases is the target to discover, however, infrequent terms which can be important information for a disease diagnosis are not considered in most cases. In this paper, we proposed a new association rule mining system considering the importance of each term using TF-IDF weight to consider infrequent but important items. In addition, the proposed system can predict candidate diagnoses from medical text records using term similarity analysis based on medical ontology.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.1
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• pp.61-69
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• 2001

This study concerns designing effective fuzzy inference rules, which can be used to evaluate other experiment sets for sensory tests. The number of fuzzy inference rules might be determined by the fuzzy division of variables. For the more the number of fuzzy division does not mean the more effectiveness, the number of inference rules should be reduced to improve efficiency of inference engine of expert system. This study verified that its suggested method and inference rules are effective in comparison with the existing studies.