• Geomechanics and Engineering
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• 제20권2호
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• 한국경영과학회지
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• 제18권1호
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• pp.71-78
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• 1993

A Baysian approach is proposed is estimating the mission failure rates by criticalities. A mission failure which occurs according to a Poisson process with unknown rate is assumed to be classified as one of the criticality levels with an unknown probability. We employ the Gamma prior for the mission failure rate and the Dirichlet prior for the criticality probabilities. Posterior distributions of the mission rates by criticalities and predictive distributions of the time to failure are derived.

• 유통과학연구
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• 제17권11호
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• pp.73-79
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• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

• 대한기계학회논문집A
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• 제27권10호
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• pp.1676-1685
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• 2003

Tensile and low cycle fatigue tests on prior cold worked 3l6L stainless steel were carried out at various temperatures ftom room temperature to 650$^{\circ}C$. Fatigue resistance was decreased with increasing temperature and decreasing strain rate. Cyclic plastic deformation, creep, oxidation and interactions with each other are thought to be responsible for the reduction in fatigue resistance. Currently favored life prediction models were examined and it was found that it is important to select a proper life prediction parameter since stress-strain relation strongly depends on temperature. A phenomenological life prediction model was proposed to account for the influence of temperature on fatigue life and assessed by comparing with experimental result. LCF failure mechanism was investigated by observing fracture surfaces of LCF failed specimens with SEM.

• 한국철도학회논문집
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• 제9권6호
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• pp.732-738
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• 2006

This research provides a scheme for Highly Accelerated Stress Test that is necessary to demonstrate reliability prediction of Korean Rapid Transit Railway Train Control System sub-equipment, which is calculated by a relevant standard for failure rate prediction of electronic products. Although determining failure information generated in the process of trial running by statistic analysis is widely accepted as a measure of confirmation for reliability prediction, this research suggests the modeling for System Life Test determined by accelerating stress factors as a measure of confirmation for reliability prediction of sub-equipment unit that is generated ahead of a trial running in System Life Cycle. Consequently, the research demonstrates sub-equipment unit reliability test, which is based on the model derived from Accelerated Stress Test, according to accuracy level and the number of samples, and conducts an official experiment by making out a reliability test procedure sheet based on test time as well.

• 대한토목학회논문집
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• 제26권4A호
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• pp.647-657
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• 2006

허용응력설계법과 극한한계 상태 설계법에 근거한 케이블과 보강형을 갖는 PSC 사장교의 예를 통해서 통계학적 확률분포를 고려한 확률론적인 위험성을 평가하였다. 사용성 한계상태 및 극한 한계상태에서의 케이블요소의 파괴확률과 거더의 최대 정모멘트. 부모멘트 발생단면, 그리고 최대전단력의 작용단면에서 각각의 요소 파괴 확률을 설계변수의 응답면에서 검토하였다. 응답면 기법(RSM)은 복잡한 다자유도 구조물에서 MCS를 사용하여 얻을 수 없는 상대적으로 매우 작은 파괴 확률값을 얻기 위해 사용이 가능할 뿐만 아니라, FOSM으로 쉽게 얻을 수 없는 한계상태방정식의 미분형태에도 성공적으로 적용이 가능 하다. 케이블과 보강형으로 구성된 병렬저항구조를 시스템 해석을 위해 각각 직렬구조로 연결하여 전체구조물의 체계신뢰성을 평가하고, 제안된 붕괴모드조합 예측값과 비교분석하였다. 제안된 붕괴모드의 조합에 의한 파괴확률검토는 조건부 파괴에 대한 동일한 발생확률을 구하며, 순열방법보다 개선된 시간비용과 효율성을 제공하며, 상하한계파괴확률을 구하는 체계 신뢰성해석에서 검토되지 않는 요소파괴의 조합에 의한 시스템의 위험성 검토를 제공한다.

• 터널과지하공간
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• 제18권6호
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• pp.447-456
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• 2008

노천광산에서 사면설계는 안정성과 경제성 측면에서 동시에 접근하여 결정해야 한다. 또한 일반 도로나 철도 연변의 사면과는 달리 대부분 지보나 보강없이 굴착해야 하기 때문에 사면각도가 가장 중요한 설계 변수이다. 본 연구에서는 인도네시아 파시르에 위치한 노천채광방식의 대규모 석탄광산 사면에 대하여 안정성 측면에서의 사면 각도 및 한계사면높이를 결정하였으며 이러한 설계가 가지고 있는 불확실성을 보완할 수 있는 계측 및 계측자료 해석을 수행하였다. 연구 결과, 사면각도(Overall Elope angle) $30^{\circ}$를 유지하는 경우 안전율 1.5를 확보하는 최대개발심도는 $353{\sim}438m$로 계산되었으나 강도정수에 대한 민감도분석결과를 고려할 때 사면높이는 300m를 초과하지 않는 것이 바람직하다. 또한 변위계측자료에 대한 역변위속도 분석 결과가 현장사면 사례와 잘 일치하여 이 방법을 통해 사면의 불안정성 및 파괴시기를 대략적으로 예측할 수 있을 것으로 판단된다.

• 대한안전경영과학회지
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• 제15권1호
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• pp.109-120
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• 2013

As information-oriented industry has been developed and electronic devices has come to be smaller, lighter, multifunctional, and high speed, the components used to the devices need to be much high density and should have find pattern due to high integration. Also, diverse reliability problems happen as user environment is getting harsher. For this reasons, establishing and securing products and components reliability comes to key factor in company's competitiveness. It makes accelerated test important to check product reliability in fast way. Out of fine pattern failure modes, failure of Electrochemical Migration(ECM) is kind of degradation of insulation resistance by electro-chemical reaction, which it comes to be accelerated by biased voltage in high temperature and high humidity environment. In this thesis, the accelerated life test for failure caused by ECM on fine pattern substrate, $20/20{\mu}m$ pattern width/space applied by Semi Additive Process, was performed, and through this test, the investigation of failure mechanism and the life-time prediction evaluation under actual user environment was implemented. The result of accelerated test has been compared and estimated with life distribution and life stress relatively by using Minitab software and its acceleration rate was also tested. Through estimated weibull distribution, B10 life has been estimated under 95% confidence level of failure data happened in each test conditions. And the life in actual usage environment has been predicted by using generalized Eyring model considering temperature and humidity by developing Arrhenius reaction rate theory, and acceleration factors by test conditions have been calculated.

• 문화기술의 융합
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• 제8권4호
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• pp.347-353
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• 2022

외식업은 소비자의 수요가 많고 진입장벽이 낮아 창업이 활발하게 일어난다. 하지만 외식업은 폐업률이 높고, 프랜차이즈의 경우 동일 브랜드 내에서도 매출 편차가 크게 나타난다. 따라서 외식업 프랜차이즈의 폐업을 방지하기 위한 연구가 필요하다. 이를 위해, 본 연구에서는 프랜차이즈 가맹점 매출에 영향을 미치는 요인들을 살펴보고, 도출된 요인들에 머신러닝 기법을 활용하여 프랜차이즈의 성패를 예측하고자 한다. 강남구 프랜차이즈 매장의 PoS(Point of Sale) 데이터와 공공데이터를 활용하여 가맹점 매출에 영향을 미치는 여러 요인들을 추출하고, VIF(Variance Inflation Factor)를 활용하여 다중공산성을 제거하여 타당성 있는 변수 선택을 진행한 뒤, 머신러닝 기법 중 분류모델을 활용하여 프랜차이즈 매장의 성패 예측을 진행한다. 이를 통해 최고 정확도 0.92를 가진 프랜차이즈 성패 예측 모델을 제안한다.