• Journal of Environmental Impact Assessment
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• v.6 no.2
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• pp.113-121
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• 1997

LHR(Landfill Hazard Ranking Model) was developed for assessing the relative hazard of landfills by using the method of value-structured approach. LHR consists of combining a multiattribute decision-making method with a qualitative risk assessment approach. A pairwise comparision method was applied to determine weights of landfill factors related. To prove the validity of weights allocation of landfill hazard evaluation factors, sensitivity analysis was applied. Firstly, the impact on landfill hazard score according to variations of weights of landfill hazard factors was analyzed. Secondly, the impact on landfill hazard score according to conditions change of landfill hazard factors was analyzed. As a result of sensitivity analysis, LHR composite scores are largely influenced by some factors following sequential order such as waste volume, proximity to sensitive environments, containment facilities, distance from drinking water supplies, and waste toxicity. The relative order of landfill hazard evaluated by LHR is not influenced by the weights change of individual factors. Therefore, LHR seems to be a credible model to determine priorities of landfill remediation based on the vulnerability of water resources.

• Korean Journal of Rural Living Science
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• v.5 no.2
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• pp.117-123
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• 1994

The purpose of this study was to establish the range of the body frame size determination for elderly subjects. Anthropometric data were collected from over 60 years old 229 elderly peoples. Three methods for determining frame size were to measure elbow breadth, ankle breadth, and height to wrist circumference ratio. Among the three variables, ankle breadth exhibited the lowest correlation with skinfold thickness and changed little with age. A pairwise comparision among three methods for determining frame size showed that the highest incidence of overall agreement was achieved when the ankle breadth and elbow breadth were compared. This analysis supported that ankle breadth can be used as an indicator of frame size. Small, medium and large categories of ankle breadth were presented depending on whether the measurements were below the 15th, between the 15th and 85th, and above the 85th percentile for ankle breadth.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• The Korean Journal of Financial Management
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• v.14 no.2
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• pp.229-252
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• 1997

본 연구에서는 실무 및 학계에 종사하는 45명의 전문가 집단을 대상으로 쌍별비교(pairwise comparision)에 의한 설문조사에서 얻어진 전문가들의 의견을 AHP 분석을 통하여 종합하는 과정을 거쳐 부도예측모형을 설계하여 검증한 뒤, LOGIT모형과 비교하였다. 본 연구에 의하면 부도예측모형에서 정량적인 정보보다 정성적인 정보가 더 중요한 역할을 한다는 D.Bunn-G.Wright(1991)의 연구와 일치하는 결과를 얻을 수 있었다. 본 연구에서 발견된 분석결과를 요약하면 다음과 같다. 첫째로 LOGIT 모형과 AHP 모형에서 모두 정량적인 정보만을 고려하는 경우보다 정성적인 정보를 함께 고려한 모형에서 부도예측율이 더 높은 것으로 나타나고 있어 부도가능성을 예측하는데 있어 정성적인 정보가 중요한 역할을 한다는 결론을 얻었다. 둘째로 AHP를 이용한 부도예측 모형을 설계할 때 각 속성에 대한 전문가(45명)들의 의견을 종합하는 방법으로 산술평균과 기하평균을 이용한 검증결과에 의하면 기하평균방법을 통하여 전문가들의 의견을 종합하는 것이 보다 합리적이라는 실증적 증거를 얻을 수 있었다. 셋째로 Akaike의 기준값을 분석한 결과에 의하면 LOGIT 모형은 정량적인 정보와 정성적인 정보를 모두 이용한 모형이 가장 우수한 것으로 판명되었고, 모형의 부도예측력도 가장 높은 것으로 밝혀졌다. AHP 모형은 정성적인 정보만을 이용한 모형에서 가장 높은 부도예측을을 나타내었으며, 기하평균을 이용한 AHP 모형은 LOGIT 모형보다 항상 높은 부도예측율을 보여주었다.