• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.391-398
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• 1999

사례기반 추론을 포함한 Lazy Learning 방법들은 인공신경망이나 의사결정 나무와 같은 Eager Learning 방법들과 비교하여 여러 가지 상대적인 장점을 가지고 있다. 그러나 Lazy Learning 방법은 역시 상대적인 단점들도 가지고 있다. 첫째로 사례를 저장하기 위하여 많은 공간이 필요하며, 둘째로 문제해결 시점에서 시간이 많이 소요된다. 그러나 보다 심각한 문제점은 사례가 관련성이 낮은 속성들을 많이 가지고 있는 경우에 Lazy Learning 방법은 사례를 비교할 때에 혼란을 겪을 수 있다는 점이며, 이로 인하여 분류 정확도가 크게 저하될 수 있다. 이러한 문제점을 해결하기 위하여 Lazy Learning 방법을 위한 속성 가중치 부여 방법들이 많이 연구되어 왔다. 그러나 기존에 발표된 대부분의 방법들이 속성 가중치의 유효 범위를 전역적으로 하는 것들이었다. 이에 본 연구에서는 새로운 지역적 속성 가중치 부여 방법을 제안한다. 본 연구에서 제안하는 속성 가중치 부여 방법(CBDFW : 사례기반 동적 속성 가중치 부여)은 사례별로 속성 가중치를 다르게 부여하는 방법으로서 사례기반 추론의 원리를 속성 가중치 부여 과정에 적용하는 것이다. CBDFW의 장점으로서 (1) 수행 방법이 간단하며, (2) 논리적인 처리 비용이 기존 방법들에 비해 낮으며, (3) 신축적이라는 점을 들 수 있다. 본 연구에서는 신용 평가 문제에 CBDFW의 적용을 시도하였고, 다른 기법들과 비교에서 비교적 우수한 결과를 얻었다.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.255-263
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• 2012

In this paper, the performance evaluation results of the timing synchronization schemes are presented for aeronautical mobile airport communication systems (AeroMACS). AeroMACS, which is based on IEEE 802.16e mobile WiMAX standard, uses the aeronautical frequency band of 5GHz with the bandwidth of 5MHz. The simulation model of AeroMACS is constructed and the evaluation for the timing synchronization performance is performed with the various channel models such as apron (APR), runway (RWY), taxiway (TWY), and park (PRK).

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.64-73
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• 2020

The government announced a plan for fund support to the enterprises with high possibility of recovery and early restructuring for the enterprises with low recovery by objectifying credit assessment system. Such announcement of government could be extended to restructuring risk of middle standing enterprises with low financial soundness by establishing the basis to prepare prompt restructuring by reinforcing the basis for restructuring through capital market. This research analyzed financial soundness based on the financial evaluation of bank by selecting 10 middle standing construction companies which focused on housing business in 2019, based on such analysis result, it was confirmed that there was a high possibility of restructuring risk. This research determined that there would be a decrease in growth rate of construction industry on the whole in 2020 due to fall of economic growth rate and reinforced real estate regulation, accordingly, there's a big possibility for middle standing construction companies with paid-in capital ratio due to its low possibility of maintenance of stable credit rating. This research established KCSI assessment model by utilizing the material of a reliable research institute in order for middle standing construction companies to evade restructuring risk, and indicated risk ratio differentiated per each item through a working-level expert survey. Such research result could suggest credit risk reduction method to middle standing construction company management staffs, and prepare a basis to evade restructuring risk.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.1
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• pp.104-112
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• 2008

This paper presents the computer program for station reliability assessment. The developed program is based on Monte-Carlo simulation using system state transition sampling, which has the merit of rapid assessment compared to state duration sampling. One of the contributions of this paper is introduction of exclusiveness among events, which makes non-exponential probabilistic distribution be utilized in modeling repair time. The developed program is applied to well-blown sample system, and its assessment results are listed in this paper to show the reliability of the program.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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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.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• 월간 기계설비
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• no.9 s.218
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• pp.43-50
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• 2008

고도로 발전하고 있는 건설산업과 소비자의 요구가 점차 세분화 되면서 객관적인 평가기능을 갖추고 있는 품질경영시스템이 지난 1992년부터 건설업계에 도입됨에 따라 건설업계는 시공의 표준화가 정착되어 가는 중이다. 최근에는 환경 및 안전.보건에 대한 관심이 높아지면서 건설업계는 환경.안전보건경영시스템 획득으로 기업경영의 우수성을 객관적으로 인증 받는 추세이다. 설비건설업계의 경우 환경경영시스템(ISO 14001)을 인증 받은 업체가 36개사, 안전보건경영시스템(K-OHSMS 18001)은 2개사, 안전경영시스템(KOSHA 18001, 건설업)은 3개사가 인증을 받는 등 아직 초기단계에 있으나 점차 확대될 전망이다. 환경.안전보건경영시스템을 인증 받을 경우 금융기관의 기술신용평가시 가산점 부여, 시공능력평가시 가산점 부여, 기술비 세액공제, 인증획득 자금지원, 융자신청시 가산점 부여, 기업실무자 교육비지원 등의 혜택이 주어지나, 무엇보다도 지속적인 관리로 업무의 질적 향상과 체계적인 관리를 통해 한 단계 업그레이드 가능한 것을 장점으로 꼽을 수 있다. 본지는 환경·안전보건경영시스템 인증 절차 및 인증시 효과에 대해 알아본다.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.225-236
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• 1994

Many research result conducted by neural network researchers claimed that the degree of generalization of the neural network system is higher or at least equal to that of statistical methods. However, those successful results could be brought only if the neural network was trained by appropriately sound data, having a little of noisy data and being large enough to control noisy data. Real data used in a lot of fields, especially business fields, were not so sound that the network have frequently failed to obtain satisfactory prediction accuracy, the degree of generalization. Enhancing the degree of generalization with noisy data is discussed in this study. The suggestion, which was obtained through a series of experiments, to enhance the degree of generalization is to remove inconsistent data by checking overlapping and inconsistencies. Furthermore, the previous conclusion by other reports is also confirmed that the learning mechanism of neural network takes average value of two inconsistent data included in training set［2］. The interim results of on-going research project are reported in this paper These are ann architecture of the neural network adopted in this project and the whole idea of developing on-line credit evaluation system,being intergration of the expert(resoning)system and the neural network(learning system.Another definite result is corroborated through this study that quickprop,being agopted as a learing algorithm, also has more speedy learning process than does back propagation even in very noisy environment.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.1-22
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• 2023

Existing studies on mixed ratings that combine ESG ratings and credit ratings have been rare. Through meta-frontier analysis, this study examines the relationship between the prime and non-prime groups in ESG ratings, credit ratings, and mixed ratings that consider ESG ratings and credit ratings at the same time. Efficiency was compared. Meta-frontier analysis was used to compare the efficiency of 143 listed manufacturing companies in Korea between the prime and non-prime groups based on the ESG ratings assigned to them by KCGS and the credit ratings assigned by Korea's three major credit rating agencies. As a result of this study, first, the meta-efficiency of the prime mixed-grade group was statistically more efficient than the non-prime mixed-grade group under the variable return scale (VRS) assumption. Second, the prime ESG rating group had a relatively higher proportion of scale inefficiency than the non-prime ESG rating group. Third, in terms of economies of scale, the prime credit rating group had a higher proportion of diminishing returns to scale (DRS) than the non-prime credit rating group. This study will help companies interested in sustainability management to do ESG management.