• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.48-49
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• 2020

Recently the diversification of construction market and the continuous reduction of construction amount are raising the need of alternative delivery method in the construction industry. The foreign advanced companies actively adopted the CM at Risk delivery method where they perform the service of agent CM in the design phase, and agree GMP(Guaranteed Maximum Price) with the client at the time of 50~80% completion of design. Even in Korea they began to apply that method to pilot projects. In CM at Risk, through the early participation of builder, the level of design completion can be improved and the change order and construction period delay can be minimized. On the other hand, GMP is usually calculated when the design is about 80% complete, so there is uncertainty in the construction cost. Therefore, in this research, the increased amounts of construction cost are analyzed in a number of public construction projects, and GMP calculation process is proposed using the analysis results and CBR(Case-Based Reasoning) technique to reduce the construction cost increase in the construction phase.

• Korean Journal of Construction Engineering and Management
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• v.12 no.1
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• pp.53-61
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• 2011

For construction projects, the importance of early cost estimates is highly recognized by the project team and sponsoring organization because early cost estimates are frequently a foundation of business decisions as well as a basis for identifying any changes as the project progresses from design to construction. However, it is difficult to accurately estimate construction cost in the early stage of a project due to various uncertainties in construction. To deal with these uncertainties, cost estimates should be made several times over the course of the project. In particular, early cost estimates are essential process for successful project management. For accurate construction cost estimates, it is necessary to compare cost estimates with actual costs based on historical project data. In this context, case-based reasoning (CBR), which is the process of solving new problems based on the solutions of similar past problems, can be considered as an effective method for cost estimating. To obtain this, it is also required to define the attribute similarities and the attribute weights. However, no existing method is capable of determining attribute weights of qualitative variables. Consequently, it has been a well-known barrier of accurate early cost estimates. Using Genetic Algorithms (GA), this research suggests the method of determining the attribute weight of qualitative variables. Based on building project case studies, the proposed methodology was validated.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.239-248
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• 2007

In the highly competitive construction industry, a slight inaccuracy of estimation can easily cause the loss of a project. Erroneous experience-based cost estimates or allocations of on-site supervisory manpower often offset the profit gained from the project and may jeopardize the management processes. To counter these types of problems, we develop a model using mathematical analysis and case-based reasoning to automate the allocation of on-site supervisory manpower and estimate construction site costs. The method is founded upon laborious data collection processes and analysis by matching statistical assumptions, and is applicable to construction projects. In the modeling the costs and allocation of on-site supervisory manpower are quantified for both owners and contractors before initiating or bidding on the projects. The findings confirm that the degree of variation of the model predictions has an accuracy rate at 88.47%. Single-site construction projects can be accurately predicted and the assignment of supervisory manpower feasibly automated.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.435-438
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• 2006

Single Nucleotide Polymorphism(SNP)는 인간 유전자 서열의 0.1%에 해당하는 부분으로 이는 각 개인의 체질 및 각종 유전질환과 밀접한 관련이 있다고 알려져 있으며 이 SNP 정보를 이용 각종 질환의 유전적 원인규명에 대한 많은 생물학적 연구가 진행되고 있다. 그러나 아직 SNP를 이용한 효율적인 분석방법에 대한 전산학적 연구는 많지 않다. 본 논문에서는 대표적인 패턴인식기 중 하나인 Support Vector Machine(SVM)을 이용 한국인의 대표적인 유전질환으로 알려진 위암에 대한 예측율을 실험하였다. 실험 데이터는 간 및 소화기 질환 유전체 센터에서 얻어진 위 질환 환자를 대상으로 하였으며 실험 결과 예측율은 67.3%로 이는 Case Based Reasoning(CBR)방법의 55% 보다 더 좋은 예측 결과를 보였다.

• 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 the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.10
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• pp.149-156
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• 2010

In this paper, the optimal design of a wind generator, implemented with the hybridized GA(Genetic Algorithm) and ES(Expert System), has been performed to maximize the AEP(Annual Energy Production) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, to solve the problem of calculation iterate, ES finds the superior individual and apply to initial generation of GA and it makes reduction of search domain. Meanwhile, for effective searching in reduced search domain, it propose Intelligent GA algorithm. Also, it shows the results of optimized model 500[kW] wind generator using hybridized algorithm and benchmark result of compare with GA.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1242-1247
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• 2009

The sudden changes in the construction market and the progressively intensifying price wars have amplified the importance of the construction cost estimation in the initial and planning phases of construction projects. However the methodologies and process of estimating construction cost in the planning and design phase are not standardized in the domestic market, in contrast to the markets of more developed countries. Therefore this paper proposes a new approximate estimation model to be used from the initial stages of construction projects. This methodology that extracts, modifies and synthesizes comparable elements of previous cases. This will introduce the foundation for the implementation of systems with improved usability and applicability.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.1
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• pp.79-87
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• 1999

사례기반추론(Case-Based Reasoning , CBR)은 새로운 문제가 주어질 때 과거의 유사한 문제 해결 사례를 기반으로 그 해법을 적절히 변용함으로써 새로운 문제에 적합한 해결책을 효율적으로 도출하고자 하는 문제 해결 접근 방법이다. 사례기반설계는 사례기반추론을 설계에 응용한 방법으로 유사한 요구 조건하에서 설계된 과거사례를 설계에 참고 및 활용하는 방법으로 선박개념설계 등 여러 분야에서 활용하고 있다. 이러한 사례기반설계기법을 이용하여 효율적으로 고품질의 설계를 도출하기 위해서는 설계하고자 하는 대상의 설계상의 요구조건과 부합되는 사례를 적절히 선정해야 하고, 선정된 사례와 현 설계조건과의 차이점을 명확하게 인지하여 현 상황에 맞게 변용할 수 있어야 한다. 본 논문에서는 과거 사례 선정 기록을 활용하여 그 선정 경향을 기억기반학습기법을 이용하여 학습함으로써 새로운 설계 시 적절한 사례를 선정하는 인덱싱 기법을 제시한다. 사례기반설계의 전형적인 예인 선박개념설계에서 설계 시 참조용도로 사용할 실적선을 선정하는 문제에 적용하여 실험에 본 결과 decision tree 나 간단한 휴리스틱을 적용하여 참조사례를 제시한 방법에 비해 본 논문에서 제시하는 기억기반학습을 적용한 방법이 우수함을 확인하였다.