• Korean Journal of Construction Engineering and Management
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• v.1 no.1 s.1
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• pp.53-62
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• 2000

This study proposes FREES(Fuzzy Risk Evaluation Expert System) for analyzing and evaluating risks occurring during the construction process. The feasibility of this system model was tested by virtual scenario. For the development of the model, at first, risk breakdown structure was established based on risks identified in the existing researches, that is quantitative and qualitative. FREES can reflect human cognition process in the risk analysis and evaluation by adopting artificial intelligence fuzzy theory, differentiating the existing quantitative analysis model. The FREES can be applied to all the project phases from planning to operation & maintenance stage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.81-89
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• 2009

In this thesis we have developed an algorithm to evaluate each student's level of understanding and find out which subjects they are not good at when they get on-line lectures at a cyber university. This algorithm is based on fuzzy logic using each student's scores adjusted according to the level of difficulty. In addition, we also introduced a "duplex intellectual lecturing system" and an "evaluation method based on the level of understanding." With the duplex intellectual lecturing system students can ask questions on line at a cyber university and under the new evaluation method, there is no need for students to move between classes. As a result of a computer simulation test, the duplex on-line lecturing system has proven to be more effective than the current one-way cyber lecturing system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.6
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• pp.382-388
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• 2017

This study performed an OpenFOAM-based numerical modeling for simulating performance of wave reduction by a floating vertical plate. Based on the Waves2FOAM library, an internal wave generation and energy dissipation with sponge layers schemes were further implemented. The performance of wave generation and dissipation was first tested with a simple two-dimensional analysis. Then, numerical simulation was carried out with the experimental data of Briggs et al. (2001) for the two regular wave cases. In general, the modeling results agreed well with the experimental data, showing better agreement than the numerical analysis by WAMIT that is included in Briggs et al. (2001).

• Journal of Information Management
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• v.33 no.1
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• pp.1-17
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• 2002

This study examined several recommendation techniques to construct an effective book recommender system in a library. Experiments revealed that a hybrid recommendation technique is more effective than either collaborative filtering or content-based filtering technique in recommending books to be borrowed in an academic library setting. The recommendation technique based on association rule turned out the lowest in performance.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05e
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• pp.31-34
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• 2003

폴리머 애자의 장기성능예측을 위하여 많은 수단과 측정방법이 연구되어 왔다. 그러나 폴리머 애자에 있어서 성능저하는 대부분 하우징 재료의 열화에 기인되고 있어 예측 및 진단목적의 연구가 하우징 재료의 가속열화에 초점이 맞추어져 있다. 폴리머 애자는 초고압 절연성능과 기계적 강도가 우수하여 송배전 설비에 많이 사용되고 있지만 애자의 형상과 장기성능과의 관계가 정량적으로 규명되어 있지 않아 개발하는 회사는 물론 사용하는 전력회사가 함께 어려움을 가지고 있다. 폴리머 애자의 형상이 폴리머 애자의 장기 성능에 어떻게 영향을 주는 지를 비교분석하고 평가하는 연구가 필요한 시점이다. 본 연구에서는 규칙 교대 갓, 불규칙 교대 갓의 폴리머 애자를 조립방식으로 제작하고, 규칙 교대 갓의 폴리머 애자를 일체형 진공사출 방식으로 제작을 하여 건조섬락전압, 주수섬락전압, 오손섬락전압을 측정하였다. 오손섬락전압은 주수섬락에 비하여 60% 정도 감소함을 보였고 불규칙한 교대 갓의 폴리머 애자의 경우 특성의 편차가 크게 나타났다. 폴리머 애자의 경우 소재의 우수한 특성도 중요하지만 형상적인 인자도 중요한 설계요소이므로 최적화 방안이 요구된다.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.247-248
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• 2008

본 논문에서는 TYPE-2 퍼지 추론 기반의 RBF 뉴럴 네트워크(TYPE-2 Radial Basis Function Neural Network, T2RBFNN)를 설계하고 PSO(Particle Swarm Optimization) 알고리즘을 이용하여 모델의 파라미터를 동정한다. 제안된 모델의 은닉층은 TYPE-2 가우시안 활성 함수로 구성되며, 출력층은 Interval set 형태의 연결가중치를 갖는다. 여기에서 규칙 전반부 활성함수의 중심 선택은 C-means 클러스터링 알고리즘을 이용하고, 규칙 후반부 Interval set 형태의 연결가중치 결정에는 경사 하강법(Gradient descent method)을 이용한 오류 역전파 알고리즘을 사용하여 학습한다. 또한, 최적의 모델을 설계하기 위한 학습율 및 활성함수의 활성화 영역 결정에는 입자 군집 최적화(PSO; Particle Swarm Optimization) 알고리즘으로 동조한다. 마지막으로, 제안된 모델의 평가를 위하여 모의 데이터 집합(Synthetic dadaset)을 적용하고 근사화 및 일반화 능력에 대하여 토의한다.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.147-165
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• 2006

In this study, experiments for selection of association terms were conducted in order to discover the optimum method in selecting additional terms that are related to an initial query term. Association term sets were generated by using support, confidence, and lift measures of the Apriori algorithm, and also by using the similarity measures such as GSS, Jaccard coefficient, cosine coefficient, and Sokal & Sneath 5, and mutual information. In performance evaluation of term selection methods, precision of association terms as well as the overlap ratio of association terms and relevant documents' indexing terms were used. It was found that Apriori algorithm and GSS achieved the highest level of performances.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1259-1268
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• 2002

One of the major goals suggested in distributed system is to improve the performance of the system through the load balancing of whole system. Load balancing among systems improves the rate of processor utilization and reduces the turnaround time of system. In this paper, we design the rule of decision-making and information interchange based on knowledge based mechanism which makes optimal load balancing by sharing the future load state information inferred from past and present information of each nodes. The result of performance evaluation shows that utilization of processors is balanced, the processing time is improved and reliability and availability of systems are enhanced. The proposed mechanism in this paper can be utilized in the design of load balancing algorithm in distributed operating systems.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1138-1146
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• 1996

Fuzzy logic ignores some informations in the reasoning process. Neural networks are powerful tools for the pattern processing. However, to model human knowledges, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy logical reasoning, we construct fuzzy inference net-work based on the neural logic network, extending the existing rule-inferencing network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search cost for searching sequentially and searching by means of priorities.