• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.31-42
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• 2011

Smart-Phones are utilized in disaster management field because it can deliver disaster information to large population simultaneously and quickly, and provide accurate information through situation-based service using the LBS(Location Based Service). To study on the utilization of smart phone for disaster information collection and dissemination method, this study suggest a framework which connects smart phone by loading application for reporting disaster. The disaster monitoring and situation dissemination system framework using smart phone is composed of 4 parts. First, smart phone application enters image, video, voice and text information and location of the disaster. Second, the disaster report reception and situation dissemination server receives the information, save in the DB, and send through smart phone SMS. Third, store into disaster information database. Fourth, display the disaster report and management information on 2D GIS, support the decision making process in deciding whether to manage as disaster, and disaster management web service which disseminates situation.

• Journal of Soil and Groundwater Environment
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• v.27 no.spc
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• pp.1-18
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• 2022

A conceptual site model is used to support decision-making of response strategy development, determination, and implementation within a risk-based contaminated site management system. It aims to provide base information of the relevant site characteristics and surface/subsurface conditions in order to understand the contaminants of concern and the associated risk they pose to the receptors. This study delineated the technical details of conceptual site model development, and discussed the possibility of applying it in domestic subsurface contamination management. Conceptual site models can be developed in various formats such as tables, diagrams, flowcharts, and figures. Contaminated sites are managed for a long period of time following the steps of investigation, remediation design, remediation, verification, and post-remedation management. The conceptual site model can be enhanced in each stage of the contaminated site management based on the continuously updated information on the site's subsurface environment. In the process of enhancement for conceptual site model, precision is gradually improved, and it can evolve from a conceptual and qualitative form to a more quantitatvive and three-dimensional model. In soil pollution management, it is desirable to incorporate the conceptual site model into the soil scrutiny system to better assess the current status of the contaminated site and support follow-up investigation and management.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.73-82
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• 2000

This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.3
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• pp.170-176
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• 2004

This study was carried out to establish a spatial decision support system for evaluating climatic aspects of a given geographic location in complex terrains with respect to the quality apple production. Monthly climate data from S6 synoptic stations across South Korea were collected for 1971-2000. A digital elevation model (DEM) with a 10-m cell spacing was used to spatially interpolate daily maximum and minimum temperatures based on relevant topoclimatological models applied to Jangsoo county in Korea. For daily minimum temperature, a spatial interpolation scheme accommodating the potential influences of cold air accumulation and the temperature inversion was used. For daily maximum temperature estimation, a spatial interpolation model loaded with the overheating index was used. Freezing risk in January was estimated under the recurrence intervals of 30 years. Frost risk at bud-burst and blossom was also estimated. Fruit quality was evaluated for soluble solids, anthocyanin content, Hunter L and A values, and LID ratio, which were expressed as empirical functions of temperature based on long-term field observations. AU themes were prepared as ArcGlS Grids with a 10-m cell spacing. Analysis showed that 11 percent of the whole land area of Jangsoo county might be suitable for quality 'Fuji' apple production. A computer program (MAPLE) was written to help utilize the results in decision-making for site-selection of new orchards in this region.

• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.38-49
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• 2019

While the overall industrial accident rate has been decreased, but those of the construction industry has not been. For safety management during the planning/design phase, which accounts for 45% of the cause of accident at the construction site, Design For Safety (DFS) was established to minimize a hazard and risk in 2016. Currently, DFS system has difficulty settling down in Korea due to the several reasons. So, this paper aims to propose to the Key Success Factor (KSF) and related action plan to improve DFS system. This study was conducted by following 2 steps: i) identification of problems on current DFS, and ii) proposal of KSF and following action plan for DFS. The DFS in Korea was compared with UK, Singapore, Australia, and US on 7 criteria (application target, execution period, change of design, collaboration among participants, expert participants, alternative review and decision support system). DFS was compared with other countries's system based on the identical criteria and the corresponding improvement measures were also proposed. The results of this study can be utilized to improve DFS system in various aspects.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.149-157
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• 2014

Diabetes is a chronic disease that requires continuous medical care and patient-self management education to prevent acute complications and reduce the risk of long-term complications. The worldwide prevalence and incidence of diabetes mellitus are reached epidemic proportions in most populations. Early detection of diabetes could help to prevent its onset by taking appropriate preventive measures and managing lifestyle. The major objective of this research is to develop an automated decision support system for detection of diabetes using mixture of experts model. The performance of the classification algorithms was compared on the Pima Indians diabetes dataset. The result of this study demonstrated that the mixture of expert model achieved diagnostic accuracies were higher than the other automated diagnostic systems.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Journal of Arbitration Studies
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• v.13 no.2
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• pp.321-351
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• 2004

According to increase of Franchise Agreements, troubles related to those agreements and trading acts occur frequently. As Franchise system had come from Western countries, franchise agreement troubles tend to international disputes. In fact, those parties entered into a franchise agreement prefer arbitration to lawsuit as a dispute resolution system because arbitration is easy to risk-management for cost and time. The essential conditions for Franchise agreements are as follows ; for Franchise to grant Intellectual Properties to Franchisee, to give an impression of the same company between Franchise and Franchisee, to control and support Franchisee, for Franchisee to be an independent merchant, and to pay Franchiser license fee. Because Franchise Agreement is also based on liberty of contract, Franchise and Franchisee could enter into any kind of agreement. However, Franchiser can make an unfair agreement abusing a position of advantage. This paper check those unfair terms and conditions in Franchise agreement. Once they enter into an agreement, they should fulfil their contract. In case of trouble on performing the contract, both of them have to discuss to solve that trouble faithfully. But, they enter into either lawsuit or arbitration in accordance with agreement when they can't reach a decision in general. Specially, which is the most popular dispute resolution hands in case of Intellectual Property License agreement. General international Franchise Agreements have arbitration terms, but there is other case such as separate Arbitration Agreement if the want, which is separate from Franchise License agreement, so even though Franchise License agreement is invalidated, Arbitration agreement continues to exist, This paper reviews Franchise system and the terms of arbitration in Franchise agreement.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2859-2870
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• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.