• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.190-196
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• 2007

With the rapid development of electronic commerce, online reputation management systems are of increasing importance in building trust and managing risk. Reputation rating mode and aggregating method are the most crucial parts of a reputation management system. In this paper, we analyze the merits and disadvantages associated with the rating mode and aggregating approach of current reputation management systems, and put forward some suggestions. These suggestions are helpful in improving current reputation management systems and developing new reputation management systems.

• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.77-85
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• 2007

With the increase in the demand for sustainable and environment-friendly development all over the world, it becomes an urgent issue for Korea to reduce $CO_2$ emission. Since building industry accounts for about 40% of international energy and resource consumption and $30{\sim}40%$ of $CO_2$ emission, it is essential to prepare for energy-efficient building. This study aims to seek for improvement direction for a domestic Building Energy Efficiency Rating System through the comparison with foreign systems. Two foreign building energy rating systems which have the similar application scope with domestic one, HERS(Home Energy Rating System) and SAP(Standard Assessment Procedure)2005 were selected. As compared with foreign systems, we intended to suggest improvement direction for effective application of Building Energy Efficiency Rating System in Korea.

• Korean Management Science Review
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• v.20 no.2
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• pp.165-177
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• 2003

This paper presents the prototype of corporate credit rating system using analytic hierarchy process (AHP). Prior studios have proposed various models of credit rating system, but most studies considered only financial information. Financial information, however, is only a small part of corporate information. In this study, the proposed credit rating system integrates both financial and non-financial information. Fifteen corporations are tested for the usefulness of the proposed system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.257-262
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• 2001

As computer-related technologies have been developed, legacy design systems have not been appropriate for new computing environment. Therefore, it is necessary that most of them are either modified or newly developed. However, this requires quite much amount of cost and time. This paper presents a method of extending legacy design system without modification using XML and XSLT. In order to apply the developed method, a good example of legacy design systems, AGMA gear rating system has been extended so as to be suitable for the distributed computing environment. An XML document for AGMA gear rating process is defined. It is transformed to the form of the input document of AGMA gear rating system by XSLT processor according to the transformation rules defined in the AGMA gear rating XSLT document. After that, AGMA gear rating system reads this input document and generates an output document in the server. These operations are automatically executed by the external legacy system controller without user interactions. Using these operations, AGMA gear rating web service has been developed based on SOAP and WSDL to provide the functions of legacy AGMA gear rating system through the distributed network. Any system or user can implement AGMA gear rating process independently to the platform type, without making it for oneself, by simply referring the AGMA gear rating web service via Internet.

• The Journal of Information Systems
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• v.16 no.3
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.75-82
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• 2023

In collaborative filtering systems, the item rating prediction values calculated by the systems are very important for customer satisfaction with the recommendation list. In the time-aware system, predictions are calculated by reflecting the rating time of users, and in general, exponentially lower weights are assigned to past rating values. In this study, to find out whether the influence of rating time on the rating value varies according to various factors, the correlation between user rating value and rating time is investigated by the degree of user rating activity, the popularity of items, and item genres. As a result, using two types of public datasets, especially in the sparse dataset, significantly different correlation index values were obtained for each factor. Therefore, it is confirmed that the influence weight of the rating time on the rating prediction value should be set differently in consideration of the above-mentioned various factors as well as the density of the dataset.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.7
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• pp.66-73
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• 1995

Rating systems of bevel gears(straight, spiral, and zerol bevel gears) which are commonly used as power transmission devices for non-papallel axes are developed on the personal computer, which analyze and/or evaluate the gear design and the service performance at the point of view of strength and durability. The typical considerations of the ratings are the bending strength, the surface durability, and the scoring resistance. The ratings are carried out using the reliable standards of AGMA & Gleason Works. Therefore, the system is built so that the variables or factors considered differently in those standards and the strength, dura- bility, and scoring partially in Gleason are appraised seperately by each method, and a series of the estimation processes is integrated into the system so as to compare each result. The developed rating systems can be used in the initial stage of gear design process, and also a better design can be performed by the evaluation of the initial design at the view point of gear strength and durability. Additionally, it is useful for the trouble-shooting of bevel gear system and to the purpose of introducing the methods for maintaining design strength in service, with appraising the gear strength after design or with appraising the influencing factor as a whole. Therefore, this rating systems can help the aim of design automation of bevel gears.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.199-211
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• 2015

The safety and performance of bridges could be monitored and evaluated by Structural Health Monitoring (SHM) systems. These systems try to identify and locate the damages in a structure and estimate their severities. Current SHM systems are applied to a single bridge, and they have not been used to monitor the structural condition of a network of bridges. This paper propose a new method which will be used in Synthetic Rating Procedures (SRP) developed by the authors of this paper and utilizes SHM systems for monitoring and evaluating the condition of a network of bridges. Synthetic rating procedures are used to assess the condition of a network of bridges and identify their ratings. As an additional part of the SRP, the method proposed in this paper can continuously monitor the behaviour of a network of bridges and therefore it can assist to prevent the sudden collapses of bridges or the disruptions to their serviceability. The method could be an important part of a bridge management system (BMS) for managers and engineers who work on condition assessment of a network of bridges.

• Asia pacific journal of information systems
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• v.25 no.3
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• pp.579-596
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• 2015

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.