• 한국정보시스템학회지:정보시스템연구
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• 제16권3호
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• pp.1-20
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• 2007

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

• 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.

• 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.

• 산업경영시스템학회지
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• 제18권33호
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• pp.119-122
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• 1995

Application of the PWFRM and SRGM for software reliability Prediction offers not only the judging base of model but also themselves with good applicabilty as easy-to-use tool.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.703-704
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• 2012

• 한국철도학회:학술대회논문집
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• 한국철도학회 2001년도 춘계학술대회 논문집
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• pp.347-358
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• 2001

In this paper, we apply NHPP model example to s/w process in order to get to know s/w reliability. The test is constructed by a test zig of commercial product loaded real embedded system s/w. It is established to s/w reliability prediction and estimation of real-time embedded system s/w. It is computed the prediction value of cumulative failures, the failure intensity, the reliability and the estimation value of MTTF, Failure Rate. To the more realization of high reliability in the real-time embedded system s/w, if the embedded system s/w is ensured to the test coverage and constructed to stable s/w process ＆ operating system, we can improve the performance and the reliability characteristic of the real-time embedded system s/w.

• 한국신뢰성학회지:신뢰성응용연구
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• 제12권1호
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• pp.1-12
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• 2012

The quality of a part has directly effect on part reliability. In the basis of MIL-HDBK-217F model, it is the determined rule that part's quality level should follow its nominal one written in its specification. If quality information is unknown, quality level of the part should be determined as 'Lower'. However, the prediction model is said to be short in reflecting parts applying 'state-of-the-art' technology and result in over-estimated failure rate by some reliability-related authorities or research institutes. In this study, the reliability prediction results by the model of MIL-HDBK-217F and Telcordia SR-332 are compared and analyzed to verify whether the statement is reasonable or not.

• 한국자동차공학회논문집
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• 제14권1호
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• pp.48-53
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• 2006

A methodology is described for predicting the fatigue life of the resistance spot weldment including strain rate effect. Because it is difficult to perform a physical failure test with high strain rate, an analytical method is necessary to get the mechanical properties of various strain rate, To this end, quasi-static tensile-shear tests at several strain rate were performed on spot weldments of SPC. These test provided the empirical data with the strain rate. With these results, we formulated the function of fatigue life prediction using the lethargy coefficient which is the global material property from tensile test. And, we predicted the fatigue life of spot weldment at dynamic strain rate. To confirm this method for fatigue life prediction, analytical results were compared with the experimental fatigue data.

• Smart Structures and Systems
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• 제22권2호
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• 한국신뢰성학회지:신뢰성응용연구
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• 제11권3호
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• pp.293-303
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• 2011

Reliability Prediction using MIL-HDBK-217F has some restrictions due to its one modeling basis. One of the restrictions is caused by selecting one operating environment of a system, which is chosen regardless of its detailed conditions, e.g., external impact and vibration. Especially, an equipment, which is installed on a mobile vehicle though its movement is quasi-static, is controversial to designate its environment as ground mobile($G_M$), rather than ground fixed($G_F$). In this paper, failure rates were compared, which are computed using several moving time rates to total operating time. RiAC-HDBK-217Plus was used as the basic calculation model. In addition, $G_F$ conditioned failure rate was evaluated by comparing with that under $G_M$ environment but fixed state.