• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.37-41
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• 2010

본 논문에서는 Mathematical Markup Language(MathML) 형식으로 작성된 수학식 분류를 위해 필요한 자질과 성능 향상에 기여하는 자질 조합을 비교 평가한다. 이것은 MathML 형식의 수학식을 분석하기 위한 전처리 작업으로, 연산자의 모호성을 해소하기 위한 가장 기본적인 단계에 해당한다고 볼 수 있다. 실험에 사용되는 기본자질(Baseline)은 MathML 태그 정보와 연산자이고, 여기에 다른 자질들을 추가하며 가장 높은 분류 성능을 가지는 자질을 찾는 방식으로 진행하였다. 학습은 지지벡터기기(Support Vector Machine: SVM)를 사용하였고 분류하고자 하는 단원은 '수학의 정석' 책을 토대로 총 12개(집합, 명제, 미분, 적분 등)로 나누었다. 실험을 통해 MathML 문서 안에서 가장 유용한 자질이 '식별자&연산자 바이그램'인 것을 알 수 있었고, 여러 가지 자질들을 조합하여 수학식을 분류한 결과 92.5%의 성능으로 분류하는 것을 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.669-672
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• 2008

비즈니스 프로세스란 기업의 목표 달성을 위하여 다양한 비즈니스 규칙에 의해 정의된 상호 연관이 있는 비즈니스 기능들의 집합을 의미한다. 비즈니스 프로세스는 크게 모델링, 구현, 실행, 관리 이렇게 총 4가지의 단계로 구성이 되는데 모델링 단계에서 비즈니스 프로세스 다이어그램을 모델링 할 경우 예기치 못한 여러 가지 이상 현상을 포함 할 수 있다. 본 논문에서는 비즈니스 프로세스 정형화 기법을 이용하여 비즈니스 프로세스에서 모호성을 제거하고 이상 현상 검출에 불필요한 요소를 변환하여 기존의 워크플로우에 적용되었던 이상 현상 검출기법을 BPMN을 이용하여 모델링 되어진 비즈니스 프로세스에서도 사용할 수 있도록 하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.177-187
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• 1999

Up to date, in many application fields of GSIS, we usually have used vector-based spatial overlay or grid-based spatial algebra for extraction and analysis of spatial data. But, because these methods are based on traditional crisp set, concept which is used these methods. shows that many kinds of spatial data are partitioned with sharp boundary. That is not agree with spatial distribution pattern of data in the real world. Therefore, it has a error that a region or object is restricted within only one attribution (One-Entity-one-value). In this study, for improving previous methods that deal with spatial data based on crisp set, we are suggested to apply into spatial overlay process the concept of fuzzy set which is good for expressing the boundary vagueness or ambiguity of spatial data. two methods be given. First method is a fuzzy interval partition by fuzzy subsets in case of spatially continuous data, and second method is fuzzy boundary set applied on categorical data. with a case study to get a land suitability map for the development site selection of new town, we compared results between Boolean analysis method and fuzzy spatial overlay method. And as a result, we could find out that suitability map using fuzzy spatial overlay method provide more reasonable information about development site of new town, and is more adequate type in the aspect of presentation.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.396-405
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• 2005

Since sentences are the basic propositional units of text, their themes would be helpful for various tasks that require knowledge about the semantic content of text. Despite the importance of determining the theme of a sentence, however, few studies have investigated the problem of automatically assigning the theme to a sentence. Therefore, we propose a sentence theme allocation scheme based on the head-driven patterns of sentences in encyclopedia. In a serious of experiments using Dusan Dong-A encyclopedia, the proposed method outperformed the baseline of the theme allocation performance. The head-driven pattern 4, which is reconfigured based on the predicate, showed superior performance in the theme allocation with the average F-score of $98.96\%$ for the training data, and $88.57\%$ for the test data.

• Annual Conference on Human and Language Technology
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• 1995.10a
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• pp.169-174
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• 1995

품사 태깅은 형태소 분석 이후 발생한 모호성을 제거하는 것으로, 통계적 방법과 규칙에 기 반한 방법이 널리 사용되고 있다. 하지만, 이들 방법론에는 각기 한계점을 지니고 있다. 통계적인 방법인 은닉 마코프 모델(Hidden Markov Model)은 유연성(flexibility)을 지니지만, 교착어(agglutinative language)인 한국어에 있어서 제한된 윈도우로 인하여, 중의성 해결의 실마리가 되는 어휘나 품사별 제대로 참조하지 못하는 경우가 있다. 반면, 규칙에 기반한 방법은 차체가 품사에 영향을 받으므로 인하여, 새로운 태그집합(tagset)이나 언어에 대하여 유연성이나 정확성을 제공해 주지 못한다. 이러한 각기 서로 다른 방법론의 한계를 극복하기 위하여, 본 논문에서는 통계와 규칙을 통합한 한국어 태깅 모델을 제안한다. 즉 통계적 학습을 통한 통계 모델이후에 2차적으로 규칙을 자동학습 하게 하여, 통계모델이 다루지 못하는 범위의 규칙을 생성하게 된다. 이처럼 2단계의 통계와 규칙의 자동 학습단계를 거치게 됨으로써, 두개 모델의 단점을 보강한 높은 정확도를 가지는 한국어 태거를 개발할 수 있게 하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.439-440
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• 2011

최근 의료 장비들이 발전하고 진단 및 연구에 다양하게 이용되면서 이로부터 얻은 3차원 의료 영상들을 자동으로 처리해주는 기술의 수요가 늘고 있다. 자동 뼈 영역화 기법은 이러한 기술들 중 하나로써 골다공증이나 뼈 골절, 골격질환 등의 진단의 효율성을 크게 높여줄 것으로 기대되고 있다. 그러나 현재까지 이를 위한 다양한 연구들이 진행되었음에도 2차원 영상과는 달리 높은 데이터양과 주변 조직과의 모호한 경계들이 많다는 어려움 때문에 실제 진단에는 사용되지 못하고 있다. 이에 따라 본 논문에서는 다중 해상도를 기반으로 하여 영역화와 정합기법을 반복적으로 수행함으로써 3차원 의료 영상 내에서 자동으로 뼈를 영역화 해내는 기법을 제안한다. 낮은 해상도 단계에서 학습된 집합의 뼈 정보들을 이용하여 대략적인 뼈 위치를 검출하고, 이후 해상도를 높여가면서 정합 과정과 영역화 과정을 반복적으로 수행한다. 성능을 확인하기 위해 무릎 자기공명영상(magnetic resonance image)내에서 대퇴골(femur)과 경골(tibia)을 영역화 하는 실험을 진행하였으며 60개의 학습 데이터들을 바탕으로 40개 영상에서의 뼈들을 영역화 하였다.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.10a
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• pp.3-8
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• 1998

The present work proposes the potential fuzzy framework, based on fuzzy set theory, for supporting decision-making problems, especially, selection problems of a best design in the area of nuclear energy system. The framework proposed is composed of the hierarchical structure module, the assignment module, the fuzzification module, and the defuzzification module. In the structure module, the relationship among decision objectives, decision criteria, decision sub-criteria, and decision alternatives is hierarchically structured. In the assignment module, linguistic or rank scoring approach can be used to assign subjective and/or vague values to the decision analyst's judgment on decision variables. In the fuzzification module, fuzzy numbers are assigned to these values of decision variables. Using fuzzy arithmetic operations, for each alternative, fuzzy preference index as a fuzzy synthesis measure is obtained. In the defuzzification module, using one of methods ranking fuzzy numbers, these indices are defuzzified to overall utility values as a cardinality measure determining final scores. According these values, alternatives of interest are ranked and an optimal alternative is chosen. To illustrate the applicability of the framework proposed to selection problem, as a case example, the best option choice of four design options under five decision criteria for primary containment wall thickening around large penetrations in an advanced nuclear energy system is studied.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.11a
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• pp.119-124
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• 1999

A selection methodology of liquid levelmeters, especially, level sensors in non-nuclear category, to be installed in nuclear energy facilities is developed using linguistic fuzzy approaches such as fully-linguistic and semi-linguistic methods. Depending on defuzzification techniques, the linguistic fuzzy methodology leads to either linguistic (exactly, fully-linguistic) or cardinal (i.e., semi-linguistic) evaluation. For the linguistic method, for each alternative, fuzzy preference index is converted to linguistic utility value by means of a similarity measure determining the degree of similarity between fuzzy index and linguistic ratings. For the cardinal method, the index is translated to cardinal overall utility value. According to these values, alternatives of interest are linguistically or numerically evaluated and a suitable alternative can be selected. Under given selection criteria, the suitable selections out of some liquid levelmeters for nuclear facilities are dealt with using the linguistic fuzzy methodology proposed. Then, linguistic fuzzy evaluation results are compared with qualitative result available in the literature. It is found that as to a suitable option the linguistic fuzzy selection is in agreement with the qualitative selection. Additionally, the comparative study shows that the fully-linguistic method using adequate scale system facilitates linguistic interpretation regarding evaluation results.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.307-316
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• 2016

A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.