• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.10-12
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• 2011

과거 충돌회피를 위한 알고리즘은 충돌위험을 결정하는데 항해사 대신 위험도를 판단하여 충돌회피를 하려고 한다. 그러나 경우에 따라서 국제해상충돌예방규칙에 맞지 않게 충돌 회피를 시행한다. 또한 타선과의 피항 관계를 항해사가 주시하고 기억해야 하는 것은 항해사에게 부담을 줄 수 있다. 따라서 국제해상충돌예방규칙에 맞게 피항 관계를 정의하여 항해사에게 알려줌으로써 피항 행동을 결정하는데 시간 및 인적 실수를 줄여줄 것으로 기대한다.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.21-23
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• 1998

본 연구에서는 속성중심 귀납법에서 사용하는 개념 계층의 상승 기법, 결정트리에 의한 귀납법에서 사용하는 정보 획득량의 측정 기법, 그리고 라프셋에 의한 지식감축 방법을 복합하여 저수준의 데이터를 고수준 정보로 일반화하고, 불필요한 속성들을 감축하여 간략화된 결정규칙을 도출하는 통합방법의 지식 발견 시스템을 시험적으로 구현했다. 여기서 추출한 최소화 결정 규칙은 대규모 데이터베이스에서 추출할수 있는 유용한 지식으로 의사결정에 사용하는 정보가 된다. 생성된 규칙지식은 각기 방법들보다 간결하다. 그리고 개념 일반화에 의해 유도된 지식이 고수준의 추상으로 표현된다.

• Korean Journal of Crystallography
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• v.12 no.2
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• pp.76-80
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• 2001

We have studied the effect of sintering conditions on the long-range order parameter of the 1 : 2 cation ordering in Ba(Mg/sub 1/3/Nb/sub 2/3/)O₃microwave dielectrics prepared through a columbite precursor method. It is found that the order parameter depends strongly on the sintering conditions. As the heat-treatment time increases at 1350℃, the long-range order parameter decreases. When sintered at 1500℃ for 4 hours, BMN shows a high long-range order parameter of 0.94.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.67-72
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• 2021

Association rule mining is a method of showing the relationship between patterns hidden in several tables. These days, granulation logic is used to add more detailed meaning to association rule mining. In addition, unlike the existing system that recommends using existing data, the granulation related rules can also recommend new subscribers or new products. Therefore, determining the qualitative size of the granulation of the association rule determines the performance of the recommendation system. In this paper, we propose a granulation method for subscribers and movie data using fuzzy logic and Shannon entropy concepts in order to understand the relationship to the movie evaluated by the viewers. The research is composed of two stages: 1) Identifying the size of granulation of data, which plays a decisive role in the implications of the association rules between viewers and movies; 2) Mining the association rules between viewers and movies using these granulations. We preprocessed Netflix's MovieLens data. The results of meanings of association rules and accuracy of recommendation are suggested with managerial implications in conclusion section.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.121-124
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• 2001

충돌회피 시스템은 선박의 안전 항해에 중요한 역할을 한다. 충돌회피 시스템은 선박이 장애물을 만났을 때 영역전문가인 항해사를 대신하여 피항 행위를 하도륵 지시하는 시스템으로 자선에서 이루어지는 해상 장애물들에 대한 피항 시 그 판단 기준을 각 장애물에 대한 충돌위험도에 둔다. 따라서 본 연구에서는 선박의 충돌회피 시스템의 보다 안전한 충돌회피를 도모하기 위해 충돌회피를 위한 충돌위험도 결정 시스템을 제안한다. 충돌위험도 결정 시스템은 장애물 모델링과 장애물의 충돌위험도 결정의 두 부분으로 구성된다. 장애물 모델링은 선박의 센서에서 나오는 저수준의 자료를 지능형 선박의 타 시스템에서 이용하기 쉽도록 구하는 과정이다. 충돌위험도 결정 시스템의 출력으로 산출되는 충돌위험도는 충돌회피 시스템의 피항 행위 결정에 정보로 사용된다. 본 연구에서는 DCPA와 TCPA를 이용한 기존의 기법에 VCD의 개념을 추가한 새로운 충돌위험도 결정 기법을 제안한다. 입력변수가 되는 DCPA, TCPA, VCD의 퍼지 소속함수를 산출하고 이를 기반으로 퍼지 추론을 이용하여 세부적인 충돌위험도를 결정한다. 본 연구에서 제안하는 기법은 기존의 DCPA와 TCPA만으로 충돌위험도를 결정한 경우보다 상세한 충돌위험도 결정이 가능하다는 장점과 국제해상충돌예방규칙의 내용이 적용되었다는 장점을 지닌다. 제안된 기법은 DCPA와 TCPA 만으로 충돌위험도를 결정한 기법과 비교.평가하여 성능을 검증한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.25-31
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• 2014

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules; these rules are formulated by language experts and consisted of lexical items. Such grammar checkers, unfortunately, show low recall which is detection ratio of errors in the document. In order to resolve this shortcoming, a new error-decision rule-generalization method that utilizes the existing KorLex thesaurus, the Korean version of Princeton WordNet, is proposed. The method extracts noun classes from KorLex and generalizes error-decision rules from them using the Tree Cut Model and information-theory-based MDL (minimum description length).

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.232-241
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• 2000

This study suggests an integrated method based on concept hierarchies, information gain, and rough set theory for efficient discovery rules from a large amount of data, and implements an optimal rules discovery system. Our approach applies attribute-oriented concept ascension technique to extract generalized knowledge from a database, knowledge reduction technique to remove superfluous attributes and attribute values, and significance of attributes to induce optimal rules. The system first reduces the size of database by removing the duplicate tuples through the condition attributes which have no influences on the decision attributes, and finally induces simplified optimal rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes. And we induce some decision rules from actual data by using the system and test rules to new data, and evaluate that the rules are well suited to them.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.397-405
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• 2010

Data mining is the method to find useful information for large amounts of data in database, and one of the important goals is to search and decide the association for several variables. The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function by decreased lift influence to generate association rule for items satisfying at least one of three criteria. We compared our function with the functions suggested by Park (2010), and Wu et al. (2004) using some numerical examples. As the result, we knew that our decision function was better than the function of Park's and Wu's functions because our function had a value between -1 and 1regardless of the range for three association thresholds. Our function had the value of 1 if all of three association measures were greater than their thresholds and had the value of -1 if all of three measures were smaller than the thresholds.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.877-884
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• 2011

The decision tree is a representative algorithm of data mining and used in many domains such as retail target marketing, fraud detection, data reduction, variable screening, category merging, etc. This method is most useful in classification problems, and to make predictions for a target group after dividing it into several small groups. When we create a model of decision tree with a large number of input variables, we suffer difficulties in exploration and analysis of the model because of complex trees. And we can often find some association exist between input variables by external variables despite of no intrinsic association. In this paper, we study on the removal method of unnecessary input variables using multiple external association rules. And then we apply the removal method to actual data for its efficiencies.