• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.911-919
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• 2011

In recent medical analysis, it becomes more important to looking for risk factors related to mental illness. If we find and identify their relevant characteristics of the risk factors, the disease can be prevented in advance. Moreover, the study can be helpful to medical development. These kinds of studies of risk factors for mental illness have mainly been discussed by using the logistic regression model. However in this paper, data mining techniques such as CART, C5.0, logistic, neural networks and Bayesian network were used to search for the risk factors. The Bayesian network of the above data mining methods was selected as most optimal model by applying delirium data. Then, Bayesian network analysis was used to find risk factors and the relationship between the risk factors are identified through a radial graph.

• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.145-158
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• 2016

This paper introduces a risk graph which is one method for determining the SIL as a measure of the effectiveness of signaling system. The purpose of this research is to make up for the weakness of the qualitative determination, which has input value ambiguity and a boundary problem in the SIL range. The fuzzy input valuable consists of consequence, exposure, avoidance and demand rate. The fuzzy inference produces forty eight fuzzy rule by adapting the calibrated risk graph in the IEC 61511. The Max-min composition is utilized for the fuzzy inference. The result of the fuzzy inference is the fuzzy value. Therefore, using the de-fuzzification method, the result should be converted to a crisp value that can be utilized for real projects. Ultimately, the safety requirement for hazard is identified by proposing a SIL result with a tolerable hazard rate. For the validation the results of the proposed method, the fuzzy risk graph model is compared with the safety analysis of the signaling system in CENELEC SC 9XA WG A10 report.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.79-92
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• 2020

As the number of systems increases and the network size increases, automated attack prediction systems are urgently needed to respond to cyber attacks. In this study, we developed four types of information gathering sensors for collecting asset and vulnerability information, and developed technology to automatically generate attack graphs and predict attack targets. To improve performance, the attack graph generation method is divided into the reachability calculation process and the vulnerability assignment process. It always keeps up to date by starting calculations whenever asset and vulnerability information changes. In order to improve the accuracy of the attack target prediction, the degree of asset risk and the degree of asset reference are reflected. We refer to CVSS(Common Vulnerability Scoring System) for asset risk, and Google's PageRank algorithm for asset reference. The results of attack target prediction is displayed on the web screen and CyCOP(Cyber Common Operation Picture) to help both analysts and decision makers.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.61-82
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• 2020

The security risk management used by some service providers is not appropriate for effective security enhancement. The reason is that the security risk management methods did not take into account the opinions of security experts, types of service, and security vulnerability-based risk assessment. Moreover, the security risk assessment method, which has a great influence on the risk treatment method in an information security risk assessment model, should be security risk assessment for fine-grained risk assessment, considering security vulnerability rather than security threat. Therefore, we proposed an improved information security risk management model and methods that consider vulnerability-based risk assessment and mitigation to enhance security controls considering limited security budget. Moreover, we can evaluate the security cost allocation strategies based on security vulnerability measurement that consider the security weight.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.425-428
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• 2002

The technology of Computer Graphics (CG) has been in great progress for almost 20 years and has proven to be a valuable tool for a broad variety of fields, including nuclear engineering. To work in any hazardous environment for example radiation field is particularly challenging because the danger is not always visually apparent. In this study as the application of CG to nuclear engineering field, we proposed to develop a radiation risk recognition aided system in which various radiation information; radiation risks, radiation distribution, hazard information and so on, were visualized by CG. The system used the server and client system. In the server there were two parts; one (main-server) was the database part having various data and the other (sub-server) was the visualization part visualizing the human phantom by POV-Ray. In the client there was the input and output part. The outputs from the system were various radiation information represented by coloring, circle graph and line graph intuitionally. The system is useful for a broad range of activities including radiation protection, radiation management, dose minimization, and demonstration to the public.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• 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.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.1
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• pp.19-28
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• 2024

Environmental regulations are becoming more stringent in response to climate change, especially concerning marine pollution caused by ship emissions. Large ships are adjusting by integrating technologies to reduce pollutant emissions and transitioning to eco-friendly fuels such as low-sulfur oil and LNG. However, small ships face space constraints for installing LNG propulsion systems and the risk of power depletion with pure electric propulsion. Consequently, there's growing interest in researching hybrid propulsion methods that combine electricity and diesel for smaller vessels. Hybrid propulsion systems utilize diverse energy sources, requiring an effective method for evaluating their efficiency. This study proposes employing Bond graph modeling to comprehensively analyze energy dynamics within hybrid propulsion systems, facilitating better understanding and optimization of their efficiency. Modeling of the ship's energy system using Bond graphs will be able to provide a framework for integrating various energy sources and evaluating their effects.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.197-212
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• 2022

The study uses the audited financial statements of 26 Vietnamese commercial banks listed on the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HOSE) during the 2008-2018 period to estimate the system GMM model, which provides empirical evidence on the effect of the variables of customer deposit to total assets (DEPO) ratio, loan to assets (LTA) ratio, liquidity of commercial banks (LIQ), credit development (CRD) ratio, external funding (EFD) ratio, and credit loss provision (LLP) ratio on liquidity risk. The study confirms that commercial banks' internal factors play the most important role, and there is no empirical evidence on macro variables that affect liquidity risk. Finally, in accordance with the theoretical framework, the study uses an estimation method with the R language and the bootstrap methodology to give empirical proof of the nonlinear correlation and U-shaped graph between commercial bank size and liquidity risk. The importance of commercial bank size in absorbing and moderating the effects of liquidity shocks is demonstrated, however, excessive growth in commercial bank size would increase liquidity risk in commercial bank operations.

• Journal of the Korean Institute of Gas
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• v.18 no.6
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• pp.32-39
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• 2014

To ensure the reliability of the safety system so that handing large quantities of hazardous materials in chemical plant is considered basic information in chemical process design. However, the reliability of the production system may be reduced when the reliability of the safety system emphasized in order to ensure the safety of chemical process. It is necessary to balance the reliability of the production system and reliability of the safety. In this study, a quantitative risk assessment was performed by selecting the furnace process, which is widely used in the chemical plant in order to suggest a way to ensure the safety and productivity of chemical process, based on the quantitative data. Quantitative risk assessment methodology have been used directed graph analysis methodology. It is possible to evaluate the reliability of the safety system and the production system. In this study, the optimum system design requirement to improve the safety and the productivity of the furnace is two-out-of-three logic for TT and PT.