• Structural Engineering and Mechanics
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• 제27권1호
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• pp.77-97
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• 2007

When assessing buildings that may collapse during a large earthquake, conventional rapid visual screening procedures generally provide good results when identifying buildings for further investigation. Unfortunately, their accuracy at identify buildings at risk is not so good. In addition, there appears to be little room for improvement. This paper investigates an alternative screening procedure based on fuzzy logic and artificial neural networks. Two databases of buildings damaged during the Athens earthquake of 1999 are used for training purposes. Extremely good results are obtained from one database and not so good results are obtained from the second database. This finding illustrates the importance of specifically collecting data tailored to the requirements of the fuzzy logic based rapid visual screening procedure. In general, results demonstrate that the trained fuzzy logic based rapid visual screening procedure represents a marked improvement when identifying buildings at risk. In particular, when smaller percentages of the buildings with high damage scores are extracted for further investigation, the proposed fuzzy screening procedure becomes more efficient. This paper shows that the proposed procedure has a significant optimisation potential, is worth pursuing and, to this end, a strategy that outlines the future development of the fuzzy logic based rapid visual screening procedure is proposed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.552-555
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• 1998

In dealing with representing knowledge under uncertainty there is a sustain tendency to increase flexibility in order to avoid problems of inconsistency in the knowledge. Many knowledge systems(information retrieval systems, expert system) include hybrid representation models. Funny retrieval systems appear as a complement or as an enrichment of this models. In this paper, we describe dynamic rule modification through situation assessment for uncertainty management.

• 해양환경안전학회지
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• 제17권1호
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• pp.53-60
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• 2011

본 연구에서는 의사결정자의 대립관계가 있는 항만개발 문제에 대한 우선순위 평가와 보상관계를 분석하였다. 이를 위해 먼저 항만개발에 대한 관련문헌을 분석하여 평가요소를 추출하였고, FSM법을 이용하여 평가요소를 구조화하고, 구조화 분석을 통해 평가항목을 선정하였다. 두 번째, 항만개발 평가 주체를 지역주민, 이용자, 지자체로 선정하고 AHP법을 이용하여 종합 평가치를 산출하였다. 세 번째 JMPR법을 이용하여 평가주체간 제휴를 구성하였을때 종합 평가결과와 대체안 선정에 따른 불만량을 최소로 하여 평가하는 방법을 제시하였다. 또한 대체안 선정에 따른 보상문제를 정량화하고 보상관계를 분석하였다. 그 결과 대상 항만중 부산항 개발이 가장 우선되어야 하며, 항만이용자는 환경에 대한 인식의 개선과, 지자체에서는 환경 친화적인 항만개발을 위한 환경 인센티브 정책을 추진해야 할 것이다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제23권4호
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• pp.26-41
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• 2018

The purpose of this study is to improve the Original DRASTIC Model (ODM) for the assessment of groundwater contamination vulnerability on the GIS platform. Miryang City of urban and rural features was selected for the study area to accomplish the research purpose. Advanced DRASTIC Model (ADM) was developed adding two more DRASTIC factors of lineament density and landuse to ODM. The fuzzy logic was also applied to ODM and ADM to improve their ability in evaluating the groundwater contamination vulnerability. Although the vulnerability map of ADM was a little simpler than that of ODM, it increased the area of the low vulnerability sector. The groundwater vulnerability maps of ODM and ADM using DRASTIC Indices represented the more detailed descriptions than those from the overlap of thematic maps, and their qualities were improved by the application of fuzzy technique. The vulnerability maps of ODM, ADM and FDM was evaluated by NO3-N concentrations in the study area. It was proved that ADM including lineament density and landuse factors produced a more reliable groundwater vulnerability map, and fuzzy ADM (FDM) made the best detailed groundwater vulnerability map with the significant statistical results.

• 환경영향평가
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• 제5권1호
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• pp.47-60
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• 1996

지하수를 음용수로 공급시 질산오염문제는 한국을 포함한 세계의 여러나라에서 보고되어 왔다. 질산($NO_3$)은 청색증 유발물짙로서 뿐만 아니라 발암물질일 가능성도 동물실험을 통해 제기되고 있는 실정이다. 질산에 오염된 지하수로 인한 인체의 위해성을 감소시키기 위하여 여러 가지의 질산 위해성 관리 대책들은 개발될 수 있다. 이러한 여러가지의 대책들을 비교 평가할 때 기준이 되는 항목으로는 (1) 허용할 수 있는 인체의 위해도(Risk Level), (2) 비용, 그리고 (3) 적용하는 방법의 기술적 타당성을 들 수 있다. 그러나 불충분한 자료와 인간지식의 한계 때문에 각 기준항목을 대표하는 값의 대부분은 불확실성을 내포하게 된다. 본 논문에서는 의사결정권자들이 불확실성을 고려하면서 여러가지 대책들 중에서 최적의 대책을 선정하는데 이용하기 위한 다기준의사결정기법은 보여진다. 그리고 각 기준값의 불확실성을 표현하고 다기준의사결정기법과 불확실성을 결합시키기 위하여 퍼지집합이론(Fuzzy Set Theory)은 응용되어 진다.

• 환경영향평가
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• 제14권2호
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• pp.55-62
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• 2005

This study applied the Neural Network and Fuzzy theory to show water-purity control and preventive measure in water quality forecasting of the future river. This study picked out NAJU and HAMPYUNG as the subject of investigation and used monthly the water quality and the outflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999 to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for study and that of 2000 are used for verification. To develop model of water quality forecasting, firstly, this research formed Neural Network model and divided Neural Network model into two case - the case of considering lag and not considering. And this study selected optimal Neural Network model through changing the number of hidden layer based on input layer(n) from n to 3n. Through forecasting result, the case without considering lag showed more precise simulated result. Accordingly, this study intended to compare, analyse that Fuzzy model using the method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.

• 융합보안논문지
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• 제18권2호
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• pp.19-24
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• 2018

이 연구에서는 사이버 위협을 평가할 시 복합적인 요소들을 고려한 위협 수준의 정량적 평가방안을 제안하였다. 제안된 평가방안은 공격방법과 행위자, 위협유형에 따른 강도, 근접성의 4가지 사이버 위협 요소를 기반으로 퍼지이론을 사용하여 사이버 위협 수준을 정량화하였다. 본 연구를 통해 제시된 사이버 위협 수준 평가는 언어로 표현된 위협 정보를 정량화된 데이터로 제시해 조직이 위협의 수준을 정확하게 평가하고 판단할 수 있다.

• 산경연구논집
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• 제13권10호
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.181-181
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• 2023

Flood is a major threat to human society, and scientific assessment of flood risk in human living areas is an important task. In this study, two different methods were used to evaluate the flood in Ulsan City, and the results were comprehensively compared and analyzed. Based on the fuzzy mathematics and VIKOR method of the multi-objective decision system, similar evaluation results were obtained in the study area. The results show that due to the large number of rivers in Ulsan City and the relatively high exposure index, the whole city faces a high risk of flooding. However, fuzzy mathematics theory pays more attention to the negative impact of floods on people, and the adaptability in the Nam-gu District is lower. In contrast, the VIKOR method pays more attention to the positive role of the economy and population in flood protection, and thus obtains a higher score. Both approaches demonstrate that the city of Ulsan faces a high risk of flooding and that its citizens and policymakers need to invest in preventing flood damage.

• Earthquakes and Structures
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• 제24권6호
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• pp.429-437
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• 2023

This paper studied the prediction of structural damage indices to buildings after earthquake occurrence using Multiple Linear Regression (MLR) and Fuzzy Linear Regression (FLR) methods. Particularly, the structural damage degree, represented by the Maximum Inter Story Drift Ratio (MISDR), is an essential factor that ensures the safety of the building. Thus, the seismic response of a steel building was evaluated, utilizing 65 seismic accelerograms as input signals. Among the several response quantities, the focus is on the MISDR, which expresses the postseismic damage status. Using MLR and FLR methods and comparing the outputs with the corresponding evaluated by nonlinear dynamic analyses, it was concluded that the FLR method had the most accurate prediction results in contrast to the MLR method. A blind prediction applying a set of another 10 artificial accelerograms also examined the model's effectiveness. The results revealed that the use of the FLR method had the smallest average percentage error level for every set of applied accelerograms, and thus it is a suitable modeling tool in earthquake engineering.