• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.851-858
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• 2002

본 연구는 주식시장에서 투자종목을 선택할 때에 주로 사용되고 있는 '평균-분산(Mean-Variance)접근방법'과는 달리, '확률적 지배(stochastic dominance)'의 개념을 적용하여 포트폴리오를 구성하는 방법을 연구하였다. 즉, 기준이 되는 확률분포 (KOSPI)를 1차 확률적으로 지배하는 포트폴리오를 구성하는 최적가중치를 체계적으로 탐색하는 방법을 모색하였다. 최적화 과정에서 고려해야 하는 함수의 모양과 볼록성 여부를 알아보았고, 일차도함수를 분석적으로 구해서 도함수기법을 이용하는 알고리즘을 개발하여 그 효율성을 시험해 보았다.

• Tunnel and Underground Space
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• v.20 no.1
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• pp.39-48
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• 2010

The present study developed a program which can assess the reliability of tunnel support systems based on a probability-based method. The developed program uses FLAC2D as a solver, and can automatically execute all the processes, associated with numerical and probabilistic analysis. Since a numerical analysis, which models the ground, requires a significant calculation time, it is actually impossible to apply simulation-based methods to probabilistic assessment on the reliability of tunnel support systems. Therefore, the present study used a point estimate method, which is efficient for probabilistic analysis since the method can significantly reduce the number of samples when compared with the simulation-based method. The developed program was applied to a tunnel project, and the results were compared with those through a deterministic approach. From the comparison, it was identified that a probabilistic approach can quantitatively assess the reliability of tunnel support systems based on probability of failure and can be used as a tool for decision making in tunnel support designs.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• Journal of Aerospace System Engineering
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• v.13 no.5
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• pp.72-78
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• 2019

Inlet hammershock is the critical loads condition for designing the inlet duct structure of a fighter. The sudden flow reduction in engine compressor causes inlet hammershock with high pressure. The traditional method was used to combine extreme conditions (maximum speed, sea level altitude, and cold day) to analyze this compression wave inlet hammershock pressure. However, after the 90s there have been papers that presented the probabilistic approach for the inlet hammershock to achieve the appropriate design pressure. This study shows how to analyze the inlet hammershock pressure by making practical use of the Republic of Korea Air Force real flight usage data under probabilistic approach and then analyze approximately 30% decreased inlet hammershock pressure compared with the traditional valve.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.352-360
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• 2019

This paper proposes a docking assessment algorithm for an autonomous underwater vehicles (AUVs) with sensor uncertainties. The proposed algorithm consists of two assessments, state assessment and probability assessment. The state assessment verifies the reachability by comparing forward distance to the docking station with expected distance to reach same depth as the docking station and necessity for correcting its route by comparing calculated inaccessible areas based on turning radius of the AUV to position of the docking station. When the AUV and the docking station is close enough and the state assessment is satisfied, the probability assessment is conducted by computing success probability of docking based on the direction angle, relative position to the docking station, and sensor uncertainties of the AUV. The final output of the algorithm is decided by comparing the success probability to threshold whether to try docking or to correct its route. To verify the validation of the suggested algorithm, the scenario that the AUV approaches to the docking station is implemented through Matlab simulation.

• The Korean Journal of Pesticide Science
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• v.14 no.2
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• pp.110-115
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• 2010

The aim of this study is to investigate the exposure assessment of Korean consumers to five neonicotinoid pesticides in fruit vegetables cultivated in Korea, using a probabilistic approach. We used five neonicotionid pesticides residues(acetamiprid, clothianidin, imidacloprid, thiacloprid, thiamethoxam) data in fruit vegetables reported by Rural Development Administration for the 2009 monitoring programme. Total exposure of five neonicotinoid pesticides for Korean consumer ranged from 0.087 to 0.236 ${\mu}g$/kg/day at the $95^{th}$ percentile. The $95^{th}$ percentile values of total exposure of five neonicotinoid pesticides by probabilistic approach were lower than those by deterministic approach, although mean values of total exposure by probabilistic approach were similar with those of total exposure by deterministic approach. Total exposure to acetamiprid residue may be mainly due to the exposure to acetamiprid through the consumption of strawberry. Also, acetamiprid residues in strawberry were considered as much more contributory factor to total exposure of acetamiprid than consumption data of strawberry. This contributory properties of acetamiprid were similar with those of all other neonicotinoid pesticides, excluding thiacloprid.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1588-1597
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• 1996

This paper describes an estimation method for lexical probabilities in Korean lexical disambiguation. In the stochastic to lexical disambiguation lexical probabilities and contextual probabilities are generally estimated on the basis of statistical data extracted form corpora. It is desirable to apply lexical probabilities in terms of word phrases for Korean because sentences are spaced in the unit of word phrase. However, Korean word phrases are so multiform that there are more or less chances that lexical probabilities cannot be estimated directly in terms of word phrases though fairly large corpora are used. To overcome this problem, similarity for word phrases is defined from the lexical analysis point of view in this research and an estimation method for Korean lexical probabilities based on the similarity is proposed. In this method, when a lexical probability for a word phrase cannot be estimated directly, it is estimated indirectly through the word phrase similar to the given one. Experimental results show that the proposed approach is effective for Korean lexical disambiguation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.835-842
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• 2003

현행 기업신용평가모형에 관한 연구는 크게 부실예측모형 및 채권등급 평가모형으로 구분된다. 이러한 신응평가모형에 관한 연구는 단순히 부실여부 또는 이미 전문가 집단에 의해 사전에 정의된 등급체계만을 예측하는 데 초점을 맞추고 있었다. 그러나. 대부분의 금융기관에서 사용하는 신응평가모형은 기업의 부실여부만을 예측하거나 기존의 채권등급을 예측하기 위만 목적보다는 기업의 고유 신응위험을 평가하여 이에 적합한 신용등급을 부여함으로써, 효율적인 대출업무를 수행하기 위해 활용되고 있다. 본 연구에서는 기존의 부실예측모형들을 대상으로 다중 부실확률모형 (Business Failure Probability Map; BFPM) 접근방법을 이용한 신응등급화 방법을 제안하고자 한다. 본 연구에서 제시된 다중 부실확률모형은 신경망모형과 로짓모형을 통합하여 부도율, 점유율을 고려한 다단계 신용등급을 예측할 수 있게 해준다. 다중 부도확률지도 접근방법을 이용하여 각 금융기관에서 정의하는 수준의 신용리스크를 효과적으로 추정하고, 이를 기준으로 보다 객관적인 다단계 신용등급을 산출하는 새로운 신응등급화 방법을 제시 하고자 한다.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.439-449
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• 2020

Meta-analysis provides a way of integrating several independent studies of interest. Since small studies with statistically significant results are more likely to be published, publication bias, which is a special case of selection bias, often occurs in meta analysis. Conditional likelihood and weighted estimating equation have been proposed to deal with publication bias, but they require to specify a correct selection probability model. In contrast, the pairwise pseudolikelihood approach can correct publication bias without fully specifying the correct selection probability model, but its performance in meta-analysis was not investigated. In this paper, we perform a numerical study about whether the pairwise pseudolikelihood approach is effective for solving publication bias arising from typical meta-analysis settings.