• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.175-180
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• 2006

Total conceptual cost estimates and the assessment of the reliability of these estimates are critical in the early stages of a building construction project. The purpose of this study is to propose the method to clients or estimators for improving the reliability of conceptual cost estimates by evaluating the reliability of conceptual cost estimates. The data from 83 building construction projects were collected for this study. The results showed that the proposed method can propose alternatives to clients or estimators for improving the reliability of conceptual cost estimates with the process of evaluation, review and feedback. This process can assist clients in their evaluation of the reliability of the estimated cost and the probability of exceeding the target cost, and in their re-estimation after dealing with the elements that influence the conceptual cost estimate badly.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2000.09a
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• pp.171-181
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• 2000

Many Studies of tunnel and tunnelling safety have been developed continuously based on the increasing social interests in underground space since 1990's in Korea. Because the growth of population in metropolitan has been accelerated at a faster pace than the development of the cities, underground facilities have been created as a great extent in view of less land space available. In this study, a lot of types of tunnel failure were surveyed and the detail causes were studied after many cases of tunnel failure were collected. There were suggested brief countermeasure of tunnel failure through case study. An expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river. This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Tunnel and Underground Space
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• v.10 no.3
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• pp.418-429
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• 2000

Many Studies of tunnel and tunnelling safety have been developed continuously based on the increasing social interests in underground space since 1990's in Korea. Because the growth of population in metropolitan has been accelerated at a faster pace than the development of the cities, underground facilities have been created as a great extent in view of less land space available. In this study, a lot of types of tunnel failure were surveyed and the detail causes were studied after many cases of tunnel failure were collected. There were suggested brief countermeasure of tunnel failure through case study. An expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river. This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• The Journal of Information Systems
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• v.14 no.1
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• Journal of The Korean Association of Information Education
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• v.5 no.2
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• pp.279-287
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• 2001

In this paper, we have defined and designed the structure of standardized XML content for supplying efficient e-Learning contents. We have also implemented the prototype of XML contents generator to create the educational contents easily. In addition, we have suggested the contents searching method using Case Base Reasoning and Bayesian belief network to supply XML contents suitable to learners request. The existing e-Learning system based on HTML could not customize and standardize, but XML contents can be reused and made an intelligent learning by supplying an adaptive content according to learners level. For evaluating the efficiency of designed XML content, we make the standard XML content for learning JAVA program in e-Learning system as well as discussing about the integrity and expanding the educational content. Finally, we have shown the architecture and effectiveness of the knowledge-based XML contents retrieval manager.

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.3
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• pp.235-253
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• 2017

The purpose of this study is to compare by learner groups(K-12, geology-related majoring students, science teachers) how geologic fieldwork education cases in domestic and foreign papers for recent 20 years reflect the elements of inquiry in field geology. The total number of analyzed cases is 53(58 for double counting), and the analysis was conducted on the elements of inquiry in field geology and their sub-element. As a result, there was a clear difference between the cases of geologic fieldwork education for K-12 and college students majoring in geology-related disciplines, in the way of reflecting elements of inquiry in field geology. Because most of K-12 target fieldworks were designed based on the curriculum, it mainly included 2-3 elements of observations, representations, and/or abductive reasoning. On the other hand, because fieldworks for geology-related major students aim to train geologic experts, it diversely contained four or five of the elements of inquiry in field geology, including spatial thinking and diachronic thinking in a high proportion, and also frequently used activities that require specialized skills such as geological mapping. Fieldworks for science teachers were found to have mixed features of K-12 and geology-related major students. Considering the diversity of included inquiry elements, similarities with the activities performed by geologists, and the autonomy of learners, it was analyzed that geologic fieldwork for geology-related major students was more close to authentic geologic inquiry than fieldwork for others. Based on the results of this study, we suggested implications for improving geological fieldwork as authentic science inquiry.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2002.11a
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• pp.139-143
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• 2002

1990 년대 후반부터 다양한 디지털 통신망을 이용하여 멀티미디어 컨텐츠 서비스가 가능하게 되었다. 하지만, 멀티미디어 컨텐츠의 전달 및 이용을 위한 기반 구조들의 독자적 발전 및 다양한 통합적 관리 체계 시스템으로 인해, 멀티미디어 컨텐츠 표현 방식의 호환성 문제, 혼재하는 네트워크 전달 방식과 단말 방식의 호환성 문제 등의 잠재적인 문제점이 발생한다. 이런 문제의 대안으로 현재 존재하는 기술 및 기반 구조들 사이의 연동을 통한 큰 프레임워크인 MPEG-21이 진행 중이다. MPEG-21 의 목표는 표준화 목표를 구체화하는 것부터 진행하여, 최종적으로 “다양한 네트워크 환경과 단말기에 있어서, 투명하고 통합적으로 멀티미디어 자원의 이용을 가능하게 하는 것”이다. 본 논문에서는 현재 표준화 작업이 진행 중인 MPEG-21 을 기반으로 하는 Testbed를 제안한다. Testbed는 server, client, DIA(Digital Item Adaptation) 의 세 모듈로 구성된다. Server 의 역할은 멀티미디어 컨텐츠를 Digital Item(DI)으로 생성하고, client 가 DI를 요구할 경우 DIA 모듈을 통해서 변환된 DI를 client 에게 제공한다. DIA 모듈은 server 에서 동작되며 client로부터 요청된 DI를 분석하고 client로부터 전송된 환경 정보를 이용하여 client 환경에 적합하게 변환된 (adapted) DI를 생성하는 것이 주 기능이다. Client 는 server 에 저장되어 있는 DI를 선택하고 user preference, terminal capability 등의 필요한 정보를 server로 전송한다. Testbed 에서는 스포츠 경기의 동영상, 정지 영상, 경기 내용 역사를 기록한 파일 등의 DI를 이용한다. 표현 언어는 XML이며, HTTP 기반의 Web 환경에서 구동되도록 설계된다.스템 사이에 의미 있는 데이터 전송, 지식 획득을 위해 정보 기술 분야에서 활용해야 할 영역으로 XML Web Services, Multi-agent Systems, 전문가 컴뮤니티를 위한 그룹웨어 연구 개발에 관해 사례 중심으로 발표한다.다 신선한 공기를 넣어 주었을 때는 배의 발달이 많이 늦어져 배양 3주째에 다른 처리보다 배의 수가 훨씬 적었다. 체세포배가 발달하는 동안에는 산소를 많이 요구하지 않으나 성숙하는 동안에는 산소를 많이 요구하는 것으로 생각된다.적인 것으로 나타났다. 다만, 곡선형은 물론 직선형에서도 열교환 튜브의 배치밀도, 튜브 길이 및 두께 등의 변화에 따른 최적화 연구가 수반되어야 할 것으로 판단된다.에서 제공된 API는 객체기반 제작/편집 도구에 응용되어 다양한 멀티미디어 컨텐츠 제작에 사용되었다.x factorization (NMF), generative topographic mapping (GTM)의 구조와 학습 및 추론알고리즘을소개하고 이를 DNA칩 데이터 분석 평가 대회인 CAMDA-2000과 CAMDA-2001에서 사용된cancer diagnosis 문제와 gene-drug dependency analysis 문제에 적용한 결과를 살펴본다.0$\mu$M이 적당하며, 초기배발달을 유기할 때의 효과적인 cysteamine의 농도는 25~50$\mu$M인 것으로 판단된다.N)A(N)/N을 제시하였다(A(N)=N에 대한 A값). 위의 실험식을 사용하여 헝가리산 Zempleni 시료(15%$S_{XRD}$)의 기본입자분포로부터 %$S_{XRD}$를 계산한 결과, 16%$S_{XRD}$의 결과값을 얻을 수 있었다. 따라서, 본 연구에서 도출한 관계식들이 유효함을 확인할 수 있었다.계식들이 유효함을 확인할 수 있었다.할 때 약간의 증가

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.179-200
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• 2008

We develop a feature selection method that can improve both the efficiency and the effectiveness of classification technique. In this research, we employ case-based reasoning as a classification technique. Basically, this research integrates the two existing feature selection methods, i.e., the univariate analysis and the LVF algorithm. First, we sift some predictive features from the whole set of features using the univariate analysis. Then, we generate all possible subsets of features from these predictive features and measure the inconsistency rate of each subset using the LVF algorithm. Finally, the subset having the lowest inconsistency rate is selected as the best subset of features. We measure the performances of our feature selection method using the data obtained from UCI Machine Learning Repository, and compare them with those of existing methods. The number of selected features and the accuracy of our feature selection method are so satisfactory that the improvements both in efficiency and effectiveness are achieved.