• 대한안전경영과학회지
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• 제10권2호
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• pp.71-83
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• 2008

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, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

• 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.

• Smart Structures and Systems
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• 제32권6호
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• pp.371-382
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• 2023

Box girder bridges are now widely used in bridge construction, and it is necessary to perform load rating regularly to evaluate the load capacity of box girder bridges. Load testing is a common measure for load rating. However, the bridge must be loaded by many trucks under different loading conditions, which is time-consuming and laborious. To solve this problem, this paper proposes a load rating method for box girder bridges based on rapid moving loads testing. The method includes three steps. First, the quasi-influence factors of the bridge are obtained by crossing the bridge with rapidly moving loads, and the structural modal parameters are simultaneously obtained from the dynamic data to supplement. Second, an objective function is constructed, consisting of the quasi-influence factors at several measurement points and structural modal parameters. The finite element model for load rating is then updated based on the Rosenbrock method. Third, on this basis, a load rating method is proposed using the updated model. The load rating method proposed in this paper can considerably reduce the time duration of traditional static load testing and effectively utilize the dynamic and static properties of box girder bridges to obtain an accurate finite element model. The load capacity obtained based on the updated model can avoid the inconsistency of the evaluation results for the different structural members using the adjustment factors specified in codes.

• 환경위생공학
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• 제23권4호
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• pp.83-93
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• 2008

The non-linear least squares model(NLSM) has long been the standard technique used by hydrologists for constructing rating curves. The reasons for its adaptation are vague, and its appropriateness as a method of describing discharge measurement uncertainty has not been well investigated. It is shown in this paper that the classical method of NLSM can model only a very limited class of variance heterogeneity. Furthermore, this lack of flexibility often leads to unaccounted heteroscedasticity, resulting in dubious values for the rating curve parameters and estimated discharge. By introducing a heteroscedastic maximum likelihood model(HMLM), the variance heterogeneity is treated more generally. The maximum likelihood model stabilises the variance better than the NLSM approach, and thus is a more robust and appropriate way to fit a rating curve to a set of discharge measurements.

• Journal of Information Technology Applications and Management
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• 제14권2호
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• 한국환경과학회지
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• 제13권9호
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• pp.759-765
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• 2004

HEC-RAS model is used for estimation of rating-curve of Musung in Wi stream. Discharge is computed from stage estimated by HEC-RAS model, is compared with the discharge of water surface slope method. The relative deviation of observed and computed discharge is 5.37%, and shows as a good results. A rating-curve by HEC-RAS model shows better results than by water surface slope method.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 가을 학술발표회 논문집
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• pp.493-500
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• 2000

Rock Mass Rating has been widely applied to the underground tunnel excavation and many other practical problems in rock engineering. However, Rock Mass Rating is hard to make out because it is difficult to estimate each valuation items through all kind of field situations and items of RMR have interdependence. So the experts of tunnel assessment have problems with rating rock mass. In this study, using multivariate analysis based on domestic data(1011EA) of water conveyance tunnel, we presented rock mass rating system which is objective and easy to use. The constituents of RMR are decided to RQD, condition of discontinuities, groundwater conditions, orientation of discontinuities, intact rock strength, spacing of discontinuities in important order. In each step, we proposed the best multiple regression model for RMR system. And using data which have been collected at other site, we examined that presented multiple regression model was useful.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2013년도 제48차 하계학술발표논문집 21권2호
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• pp.319-320
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• 2013

In these days, an individual is evaluated by his or her credit. So, it is very critical for an individual to know his or her credit rating and then to try to improve the credit rating. But, there are few services to analyze credit status for an individual and inform the person of the credit. In fact, it could be impossible to let a person know how to improve his or her credit rating. Against this backdrop, we research on a credit management model to analyze credit status of an individual rapidly and propose individual-customized method to improve the credit rating. We set up the model and design it in detail. This servie would certainly make it convenient for an individual to retrieve credit rating and improve it.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1990년도 봄 학술발표회 논문집
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• pp.37-43
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• 1990

This paper develops practical models and methods for the assessment of safety and rating of damaged and/or deteriorated bridges by incorporating a system identification technique for the explicit inclusion of the degree of deterioration or damage and of the actual bridge response. And, based on the proposed model, reliability-based rating methods are proposed as LRFR(Load and Resistance Factor Rating) and system reliability-index rating criteria. The proposed limit state model explicitly accounts for the degree of deterioration or damage in terms of the damage and response factors. The damage factor in the paper is proposed as the ratio of the current stiffness to the intact stiffness. Based on the observation and the results of applications to existing bridges, it may be concluded that the proposed rating models, which explicitly account for the uncertainties and the effects of degree of deterioration or damage based on the system identification technique, provide more realistic and consistent safety-assessment and capacity-rating.

• 지식경영연구
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• 제23권1호
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• pp.187-201
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• 2022

관광객이 작성한 온라인 리뷰는 관광산업의 관리 및 운영에 중요한 정보를 제공한다. 평점은 제품이나 서비스에 대한 정량적인 평가로 간편하지만 관광객의 진실한 태도를 반영하기 어려우며 평점과 리뷰내용에 대한 불일치 문제도 발생하고 있다. 불일치 문제는 잠재고객에게 혼동을 줄 수 있으며 구매의사결정에도 영향을 미칠 수 있다. 본 연구에서는 온라인 리뷰기반의 평점 예측모형을 통해 평점과 리뷰내용의 불일치 문제를 해결하고자 한다. 한국을 방문한 외국인 관광객이 작성한 관광지와 호텔에 대한 리뷰의 감성분석을 통해 평점과 감성의 차이를 비교하고 TF-IDF vectorization과 감성분석 결과로 변수를 선정하였다. 로짓, 인공신경망, SVM(Support Vector Machine)을 적용하여 평점을 분류하고, 인공신경망, SVR(Support Vector Regression)을 통해 평점을 예측하였다. 평점 분류모형과 예측모형 모두 불일치한 리뷰를 제거하고 감성분석을 반영한 모형에서 우수한 성과를 보여주었다. 본 연구에서 제안한 온라인 리뷰 기반의 평점 예측모형은 평점과 리뷰내용에 대한 불일치 문제를 해결하여 신뢰할 수 있는 정보를 제공하였으며 평점이 없는 온라인 리뷰에도 활용할 수 있을 것이다.