• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.479-486
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• 2007

멘토링은 조직이나 사회 구성원들의 발전을 돕기 위한 프로그램으로서, 조언자, 상담자 및 후원자 역할을 하는 '멘토(mentor)'와 도움을 얻고자 하는 '멘티(mentee)'가 긴밀한 관계를 맺고 유지함으로써 상호 발전을 위해 수행된다. 현재 이루어지고 있는 대부분의 멘토링은 면대면 (face-to-face) 시스템이거나 웹 기반의 e-mentoring 시스템으로, 전자는 시간적 그리고 지역적 한계를 극복해야만 하고 후자는 멘토나 멘티가 멘토링 사이트에 접속하여 게시판을 확인하지 않으면 제대로 된 멘토링을 수행할 수 없다는 한계를 가지고 있다. 또한 멘토와 멘티의 매칭은 무작위로 이루어지거나 코디네이터라고 불리는 사람이 수행하기 때문에, 비용이 많이 소용될 뿐 아니라 개인적인 편견이나 오류가 개입될 여지가 상존한다. 이에 본 연구에서는 시간과 장소의 제약에 구애 받지 않는 u-Mentoring 시스템을 개발하고자 하며, 그 첫 단계로써 멘토와 멘티간의 매칭을 지원하는 새로운 알고리즘(M3 Algorithm, Mentor-Mentee Matching Algorithm)을 제안하고자 한다. 본 연구에서 제안하는 알고리즘은 매칭의 정확도와 멘토-멘티의 매칭 만족도를 높이기 위해 멘토-멘티 온톨로지(M-Ontology)와 사례기반추론 기법을 사용하였다. 즉, 멘토-멘티의 효과적인 매칭을 위해, 멘토-멘티간 매칭 사례가 없는 초기 단계에는 멘토와 멘티의 속성 비교를 통한 추천 방식을 사용하고, 멘토링이 종료되어 충분한 멘토-멘티간 매칭사례가 수집되면 그 결과를 재사용해 추후 매칭에 활용한다. 본 논문에서는 제안한 매칭 알고리즘이 내장된 u-Mentoring system의 포로토타입을 보여주고자 한다.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.929-936
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• 2004

A major limitation of available component-based platforms Is that they do not provide suitable means for describing and reasoning on the concurrent behaviour of interacting component-based system. Indeed while these platforms provide convenient ways to describe the typed signatures of components, e.g. like CORBA's IDL, they offer a quite low-level support to describe the concurrent behaviour of component. The ability to describe and verify the concurrent behaviour of interacting components is key aspect In the development of large component-based software system. This study propose a component interface specification using process algebra and configuration's role which allows one to prove correctness of software architecture generated at design level as well as to define compatibility relations by our evolution rule and $\pi$-graph. Also, we shown on an appropriateness of a specification techniques and definitions proposed in this paper by case-study.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.53-69
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• 2003

Many process variables in modem manufacturing processes have influence on quality of products with complicated relationships. Therefore, it is necessary to control multiple quality variables in order to monitor abnormal signals in the processes. This study proposes an integrated procedure of self-organizing map (SOM) neural network and case-based reasoning (CBR) for multivariate process control. SOM generates patterns of quality variables. The patterns are compared with the reference patterns in order to decide whether their states are normal or abnormal using the goodness-of-fitness test. For validation, it generates artificial datasets consisting of six patterns, normal and abnormal patterns. Experimental results show that the abnormal patterns can be detected effectively. This study also shows that the CBR procedure enables to keep Type 2 error at very low level and reduce Type 1 error gradually, and then the proposed method can be a solution fur multivariate process control.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.315-322
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• 2002

최근에 경험적 지식을 체계화하는 방법으로 사례기반추론(CBR: Case Based Reasoning) 및 규칙기반추론(RBR: Rule Based Reasoning)이 여러 분야에서 이용되고 있다. CBR과 RBR이 각각 활용되기도 하지만 문제 해결의 정확성을 높이기 위해 복합된 형태로 사용되기도 하고, 흑은 효과적으로 문제를 해결하기 위해 문제 해결 단계별로 각각 사용되기도 한다 또한 데이터에서 지식을 추출하기 위한 세부 알고리즘으로는 인공지능과 통계적 분석기법 등이 활발하게 연구 및 적용되고 있다. 본 연구는 모니터의 핵심 부품인 섀도우마스크(Shadow Mask)를 개발하는데 있어 도면 협의부터 설계가지의 과정에 CBR과 RBR을 활용하고 발생되는 데이터를 이용하여 진화(Evolution)하는 지식기반시스템(Knowledge Based System)으로 구축하는 것을 목적으로 하고 있다. 특히 도면 협의시 인터넷상에 웹서버 시스템을 통하여 규격 (User Spec.)을 생성하고 이를 이용하여 자동으로 도면이 설계되도록 하고 저장된 사례들을 공유할 수 있도록 하여 도면 검토 시간이 단축되고 검토의 정확성을 기할 수 있어 실패비용을 감소시켰다. 그리고 실제 설계시 CBR과 RBR을 활용하여 자동설계를 할 수 있게 하였고 현장에서 발생되는 데이터를 지식화하여 유사사례 설계가 가능하도록 하였다. 지식기반시스템은 신속한 도면 검토가 가능하므로 인원 활용이 극대화되고, 섀도우 마스크 설계자와 마스터 패턴 설계자 사이의 원활한 의사소통을 통해 고객과의 신뢰성 확보와 신인도 향상을 기대할 수 있는 효과가 있다. 그리고 고급설계자에게만 의지되어온 것을 어느 정도 해결할 수 있고, 신입설계자에게는 훌륭한 교육시스템이 될 수 있다.한 도구임을 입증하였다는 점에서 큰 의의를 갖는다고 하겠다.운 선용품 판매 및 관련 정보 제공 등 해운 거래를 위한 종합적인 서비스가 제공되어야 한다. 이를 위해, 본문에서는 e-Marketplace의 효율적인 연계 방안에 대해 해운 관련 업종별로 제시하고 있다. 리스트 제공형, 중개형, 협력형, 보완형, 정보 연계형 등이 있는데, 이는 해운 분야에서 사이버 해운 거래가 가지는 문제점들을 보완하고 업종간 협업체제를 이루어 원활한 거래를 유도할 것이다. 그리하여 우리나라가 동북아 지역뿐만 아니라 세계적인 해운 국가 및 물류 ·정보 중심지로 성장할 수 있는 여건을 구축하는데 기여할 것이다. 나타내었다.약 1주일간의 포르말린 고정이 끝난 소장 및 대장을 부위별, 별 종양개수 및 분포를 자동영상분석기(Kontron Co. Ltd., Germany)로 분석하였다. 체의 변화, 장기무게, 사료소비량 및 마리당 종양의 개수에 대한 통계학적 유의성 검증을 위하여 Duncan's t-test로 통계처리 하였고, 종양 발생빈도에 대하여는 Likelihood ration Chi-square test로 유의성을 검증하였다. C57BL/6J-Apc$^{min/+}$계 수컷 이형접합체 형질전환 마우스에 AIN-76A 정제사료만을 투여한 대조군의 대장선종의 발생률은 84%(Group 3; 21/25례)로써 I3C 100ppm 및 300ppm을 투여한 경우에 있어서는 각군 모두 60%(Group 1; 12/20 례, Group 2; 15/25 례)로 감소하는 경향을 나타내었다. 대장선종의 마리당 발생개수에 있어서는 C57BL/6J-Apc$^{min/+}$계 수컷 이형접합체 형질전환 마우스에 AIN-76A 정제사료만을 투여한

• Journal of KIISE
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• v.44 no.7
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• pp.680-685
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• 2017

The emergence of online media and their data has enabled data-driven methods to solve challenging and complex tasks such as rumor classification problems. Recently, deep learning based models have been shown as one of the fastest and the most accurate algorithms to solve such problems. These new models, however, either rely on complete data or several days-worth of data, limiting their applicability in real time. In this study, we go beyond this limit and test the possibility of super early rumor detection via recurrent neural networks (RNNs). Our model takes in social media streams as time series input, along with basic meta-information about the rumongers including the follower count and the psycholinguistic traits of rumor content itself. Based on analyzing millions of social media posts on 498 real rumors and 494 non-rumor events, our RNN-based model detected rumors with only 30 initial posts (i.e., within a few hours of rumor circulation) with remarkable F1 score of 0.74. This finding widens the scope of new possibilities for building a fast and efficient rumor detection system.

• Journal of the Korean Geographical Society
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• v.48 no.1
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• pp.112-127
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• 2013

It is unsuitable for Korean coastal information to work specific tasks because the coastal information of the current provides simple information, and thus the coastal information is required to reprocess. Therefore, this paper intends to present the ontology model for managing the coastal information using Geo-Ontology and seek application of ontology. The contents of this paper follow as; First of all, I considered the base theories for ontology and related researches. Second, I built Geo-Ontology which defines taxonomy of geographical features and their relationships. Third, I designed and implemented the coastal information ontology about basin of coast, Masan, using Geo-Ontology. Fourth, I carried out semantic queries and reasoning, assessment of the coastal information ontology. This paper will be a base study for many projects which are currently being conducted to integrate spatial information for more effective administrative works and easier maintenance and management of data. Also, this paper is significant in the sense that it is the study preparing for linked data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.283-286
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• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.93-108
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• 2004

The purpose of this research is to summarize the methodological issues in internet survey and to suggest personalized internet survey system using data mining technique for enhancing the survey quality of internet survey as well as utilizing the benefit of interactive multimedia factors of internet survey. The data mining technique used in this paper is Case Based Reasoning for adopting individual design preference affecting survey quality. For achieving the research purpose, two surveys, pre & post survey, were performed. Pre survey was done for implementing CBR database to find individual index affecting survey quality and post survey was used for measuring the peformance of personalized internet survey system. The result shows that the survey quality of personalized web survey system is better than generalized web survey system.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.