• Spatial Information Research
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• v.13 no.2 s.33
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• pp.139-155
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• 2005

Today, ever growing population density in Seoul or areas near Seoul is problematic. To overcome the problem, the government tries to construct so called, 'New-towns'near Seoul to spread out the population over larger areas. But building new-towns created another problem of traffic between Seoul and New-towns. It can be solved if more public transportation systems such as urban railroads are constructed. In order to succeed in urban railroad construction, we have to make a perfect plan through in depth research on characteristics of the urban train and the construction site. In this paper, we propose a method to design the Decision Support Systems(DSS) using Geographic Information Systems(GIS) and Spatial Analysis for the planning of urban railroad construction.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Information Systems Review
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• v.13 no.2
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• pp.73-87
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• 2011

This study measures the research productivity of IS researchers in South Korea. In this study, we select 11 leading IS journals, those are MIS Quarterly(MISQ). Information Systems Research(ISR), Journal of Management Information Systems(JMIS), Journal of the Association for Information Systems(JAIS), European Journal of Information Systems(EJIS), Information Systems Journal(ISJ), Decision Support Systems(DSS). Communications of the Association for Information Systems(CAIS), Information and Management(I&M), Journal of Strategy Information Systems(JSIS), and Journal of Information Technology(JIT). We analyzed the published articles of IS researchers in South Korea between 2007 and 2009 using scientometric techniques for measuring research productivity. The findings of this study provide evidences that IS research productivity has become a comprehensive information to business tool to assist advertizing of electing student of IS department and university, and take a person into IS professor in the IS job market. IS productivity research on Korean IS researcher rarely showed. The unique study showed valuable contributions for academicians to provide a better comprehension of publishing trend of leading IS journals.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.125-144
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• 2006

The development of mobile applications is fast in recent years. However, nearly all applications are for messaging, financial, locating services based on simple interactions with mobile users because of the limited screen size, narrow network bandwidth, and low computing power. Processing an algorithm for supporting a group decision process on mobile devices becomes impossible. In this paper, we introduce the mobile-oriented simple interactive procedure for support a group decision making process. The interactive procedure is developed for multiple objective linear programming problems to help the group select a compromising solution in the mobile Internet environment. Our procedure lessens the burden of group decision makers, which is one of necessary conditions of the mobile environment. Only the partial weak order preferences of variables and objectives from group decision makers are enough for searching the best compromising solution. The methodology is designed to avoid any assumption about the shape or existence of the decision makers' utility function. For the purpose of the experimental study of the procedure, we developed a group decision support system in the mobile Internet environment, MOBIGSS and applied to an allocation problem of investor assets.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.4
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• pp.523-531
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• 2022

The International Atomic Energy Agency (IAEA) entails independent decision-making for the safety supervision of civil nuclear facilities. To evaluate and review the safety of nuclear facilities, the national regulatory body usually consults independent institutions or external committees. Technical Support Organizations (TSOs) include national laboratories, research institutions, and consulting organizations. Support from professional organizations in other countries may also be required occasionally. Most of the world's major nuclear power countries adopt an independent nuclear safety supervision model. Accordingly, China has continuously improved upon the construction of such a system by establishing the National Nuclear Safety Administration (NNSA) as the decision-making department for nuclear and radiation safety supervision, six regional safety supervision stations, the Nuclear and Radiation Safety Center (NSC), a nuclear safety expert committee, and the National Nuclear and Radiation Safety Supervision Technology R&D Base, which serves as the test, verification, and R&D platform for providing consultation and technical support. An R&D system, however, remains to be formed. Future endeavors must focus on improving the technical support capacity of these systems. As an enhancement from institutional independence to capability independence is necessary for ensuring the independence of China's nuclear safety regulatory institution, its regulatory capacity must be improved in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.32-41
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• 2003

In this paper, we implemented a looms management system which supports remote monitoring and scientific management of the looms. In the implemented system, the layout of the looms is placed in the user interface, and each loom's operating state and rate are automatically represented there. The implemented system has aggregate query processing functions for the looms existing in the selected area by the louse and it also has high level query processing functions to support the chart and pivot table; it can be used as a decision support system. The proposed system can detect temporal or persistent problems of the looms. Therefore, it can be used to raise the productivity and to reduce the cost in textile companies by coping with the situation properly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.117-129
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• 2009

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical tools for the bullwhip effect control in supply chains. First, we develop the quantitative models for computing the bullwhip effect in a three-stage supply chain consisted of a single retailer, a single distributor and a single manufacturer when the fixed-interval replenishment policy is applied at each stage. The quantitative models are developed under the different conditions for the demand forecasting and share of customer demand information. They are validated through the computational experiments. Second, we develop a simulation-based decision support system for the bullwhip effect control in a more diverse dynamic supply chain environment. The system includes a what-if analysis function to examine the effects of varying input parameters such as operating policies and costs on the bullwhip effect.

• ETRI Journal
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• v.39 no.5
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.358-362
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• 2011

The National Emergency Management Agency of South Korea has established a Disaster Victims Psychology Support Center. The Disaster Victims Psychology Support Center can enable victims who got psychological damage from disasters to return to their daily lives through healing activity, field visits and advice of experts. The previous Psychology Support Center System managed the information of disaster victims through an independent database. However, this paper proposes a system that is developed to identify medical institutions and mental health centers within a distance of radius, based on the potential Hot-Spot areas of disaster victims using the GIS Systems. The proposed system can efficiently support selection of appropriate institutions for disaster victims using their location and age, classification of damage, and damaged parts of the body. Also, this spatial analysis can assist to decide on a policy based on the location of disaster victims and the extent of damage. Therefore, this paper can provide the required information to support decision making based on the concentrated areas for disaster victims.