• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.139-153
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• 2016

Cyber incident collected from cyber-threat-intelligence sharing Center is growing rapidly due to expanding malicious code. It is difficult for Incident analysts to extract and classify similar features due to Cyber Attacks. To solve these problems the existing Similarity Analysis Method is based on single or multiple cyber observable of similar incidents from Cyber Attacks data mining. This method reduce the workload for the analysis but still has a problem with enhancing the unreality caused by the provision of improper and ambiguous information. We propose a incident analysis model performed similarity analysis on the hierarchically classified cyber observable based on cyber incident that can enhance both availability by the provision of proper information. Appling specific cyber incident analysis model, we will develop a system which will actually perform and verify our suggested model.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1429-1437
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• 2013

Research articles in food related to climate change were analyzed by implementing a text-mining algorithm, which is one of nonstructural data analysis tools in big data analysis with a focus on frequencies of terms appearing in the abstracts. As a first step, a term-document matrix was established, followed by implementing a hierarchical clustering algorithm based on dissimilarities among the selected terms and expertise in the field to classify the documents under consideration into a few labeled groups. Through this research, we were able to find out important topics appearing in the field of food related to climate change and their trends over past years. It is expected that the results of the article can be utilized for future research to make systematic responses and adaptation to climate change.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.381-393
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• 2024

Purpose Metabolic abnormalities in hepatic encephalopathy (HE) cause brain edema or demyelinating disease, resulting in symmetric regional cerebral edema (SRCE) on MRI. This study aimed to investigate the usefulness of the clustering analysis of SRCE in predicting the development of brain failure. Materials and Methods MR findings and clinical data of 98 consecutive patients with HE were retrospectively analyzed. The correlation between the 12 regions of SRCE was calculated using the phi (φ) coefficient, and the pattern was classified using hierarchical clustering using the φ² distance measure and Ward's method. The classified patterns of SRCE were correlated with clinical parameters such as the model for end-stage liver disease (MELD) score and HE grade. Results Significant associations were found between 22 pairs of regions of interest, including the red nucleus and corpus callosum (φ = 0.81, p < 0.001), crus cerebri and red nucleus (φ = 0.72, p < 0.001), and red nucleus and dentate nucleus (φ = 0.66, p < 0.001). After hierarchical clustering, 24 cases were classified into Group I, 35 into Group II, and 39 into Group III. Group III had a higher MELD score (p = 0.04) and HE grade (p = 0.002) than Group I. Conclusion Our study demonstrates that the SRCE patterns can be useful in predicting hepatic preservation and the occurrence of cerebral failure in HE.

• Korean Journal of Social Welfare
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• v.64 no.3
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• pp.155-181
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• 2012

The paper aims to improve the quality and validity of community plan using the content analysis on gyeongju-si. To achieve it, the relevant literatures reviewed, and established the basic framework and three criteria of community plan, then adapted these criteria to the gyeongju-si community plan. Findings are like: Firstly, the measurement criteria of validity are clarified into three; vertical consistency on the whole plan's hierarchical structure, horizontal relevancy between the section plans, clearness of statement. Secondly, generally, gyeongju si plan didn't satisfy the criteria of validity. Based on these results, it specifically offers a impoverishment strategy including the guidelines' upgrading.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.555-562
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• 2004

Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute $L_2$ Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• Convergence Security Journal
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• v.19 no.2
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• pp.129-136
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• 2019

The main tasks of commander and staffs in ground operations are a continuation performing the process of making decisions in various situations. Since the current decision-making process is largely dependent on qualitative methods, it is difficult to integrate with the decision-making tools associated with the 4th Industrial Revolution. The purpose of this study is to suggest the process of deriving the relative importance of the evaluation factors using the AHP with focusing on assessing the avenues of approach in IPB related to the ground operation plan. The most important aspect of IPB is the evaluation of the avenues of approach. Evaluation factors include target accessibility, observation and seasons, concealment and cover-up, ease of maneuverability, and ease of transition to adjacent access roads. The existing methods are the comparison method with evaluation factors and the analysis with the advantages and disadvantages. However, it has been criticized that they regard evaluation factors as equal importance. The results show that target accessibility has the highest score related with priority when considering the criteria.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.29-40
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• 2011

The objective of this paper is to develop an intelligent decision support system that is able to advise disaster countermeasures and degree of incidents on the basis of the collected and analyzed signs of disasters. The concepts derived from ontology, text mining and case-based reasoning are adapted to design the system. The functions of this system include term-document matrix, frequency normalization, confidency, association rules, and criteria for judgment. The collected qualitative data from signs of new incidents are processed by those functions and are finally compared and reasoned to past similar disaster cases. The system provides the varying degrees of how dangerous the new signs of disasters are and the few countermeasures to the disaster for the manager of disaster management. The system will be helpful for the decision-maker to make a judgment about how much dangerous the signs of disaster are and to carry out specific kinds of countermeasures on the disaster in advance. As a result, the disaster will be prevented.

• Journal of Convergence for Information Technology
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• v.10 no.2
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• pp.154-159
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• 2020

The purpose of this study is program development for efficient medical institution management and finding comprehensive measures to increase the credibility of medical institutions. For this purpose, a survey on medical service satisfaction was conducted for elderly people aged 65 or older living in four other areas. The results of the analysis were as follows: First, the survey participants had higher confidence in private medical institutions. Second, there was no relationship between the reliability of public and private medical institutions and the selection of medical institutions. Third, the environment of the survey subjects was related to the selection of medical institutions. The credibility of the surveyed public and private medical institutions affects the selection of medical institutions, and the economic power and understanding of the medical institutions also influence the selection of medical institutions.. Therefore, it is suggested that public medical institutions need to improve the satisfaction of medical services in the future, and management efficiency of public medical institutions in addition to private medical institutions is urgently needed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.183-193
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• 2020

This study was conducted to evaluate the effect of environment factors on the growth of autumn season cultivation of Kimchi cabbage using the big data in terms of public open data(weather, soil information, and growth of crop, etc.). The growth data and the environment data such as temperature, daylength, and rainfall from 2010 to 2019 were collected. As a result of composing the correlation matrix, the height and leaf number showed high correlation in growing degree days(GDDs) and daylength, and the yield showed negative correlation in growing degree days and the concentration of clay. GDDs and daylength explained about 89% and 84% of variation in height, respectively. These two environmental factors also explained about 85% and 79% of variation in leaf numbers, respectively. In contrast, the coefficient of determination was low for yield when GDDs and concentration of clay was used. The outcome of regional statistical analysis indicated that relationship between yield and sum of sand and silt were high in Haenam and Jindo areas. Hierarchical cluster analysis, which was performed to verify the association of yield, GDDs, and concentration of clay, showed that Haenam and Jindo were clustered together. Although GDDs and yield vary by year and region, and there are regions with similar concentration of clays, observation data are grouped as the result. These suggests that GDDs and soil texture are expected to be related to yield. The cluster analysis results can be used for further data analysis and agricultural policy establishment.