• Journal of the Korean Society for information Management
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• v.15 no.3
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• pp.45-71
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• 1998

There is increasing importance of a system to reorganize explosive expansion of internet information resources efficiently; therefore, an increasing concern about classification system as an instrument for facilitating an access to a specific subject and improving efficiency in information retrieval. Comparing the hierarchical structure and access methodology of internet-based classification system with those of library classification such as Dewey Decimal Classification through their structural aspects and retrival process, this paper proposes the proper classification system in internet environment.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.22-28
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• 2008

This paper aims to offer a solution based on semantic document classification to improve e-Government utilization and efficiency for people using their own information retrieval system and linguistic expression. Generally, semantic document classification method is an approach that classifies documents based on the diverse relationships between keywords in a document without fully describing hierarchial concepts between keywords. Our approach considers the deep meanings within the context of the document and radically enhances the information retrieval performance. Concept Weight Document Classification(CoWDC) method, which goes beyond using existing keyword and simple thesaurus/ontology methods by fully considering the concept hierarchy of various concepts is proposed, experimented, and evaluated. With the recognition that in order to verify the superiority of the semantic retrieval technology through test results of the CoWDC and efficiently integrate it into the e-Government, creation of a thesaurus, management of the operating system, expansion of the knowledge base and improvements in search service and accuracy at the national level were needed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.70-73
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• 2002

기업 환경의 변화, 정보통신 기술의 발전 등으로 인한 비즈니스 패러다임의 전환은 산업전반에 있어서 비즈니스 모델(Business Model)의 중요성을 부각시키고 있다. 그러나 기존의 비즈니스 모델에 대한 연구는 온라인에서의 거래(transaction)방식에 의한 사후적 분류와 프로세스 설계에 초점인 맞추어져 있어, 실제 기업, 특히 오프라인 기업의 의사결정을 지원하기에는 미흡한 실정이다. 따라서, 기업의 비즈니스 설계와 이에 따른 전략 수립을 지원하기 위한 비즈니스 모델링 방법론에 대한 연구가 필요하다. 본 연구에서는 비즈니스를 시장(market), 참여자(actor), 거래(transaction)의 3개 계층(tier)으로 파악하고, 각 계층에서의 비즈니스 설계를 위해서 고려되어야할 요소를 찾고, 이에 따라 비즈니스를 표현하는 설계 방법론 (design framework)인 MAT를 제시하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.415-417
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• 2002

날로 커져가는 3D 모델을 효율적으로 사용하기 위한 노력으로 압축처리 방법들이 연구되고 있다. 본 논문에서는 3D 모델의 메쉬를 Layer로 분할하여 Vertex Layer와 Triangle Layer를 생성 후, 삼각형들을 몇가지 연산코드로 분류하여 압축(compression)하는 방법을 제안한다. Triangle Layer는 기본 정점으로부터 연결된 선분의 정점들로 이루어진 Vertex Layer의 쌍을 이용하여 만들어진다. 이 Triangle Layer에 해당 되는 삼각형들의 연결 정보를 제안한 연산코드로 분류하고, 이것을 엔트로피 코딩하여 3D 모델을 압축한다. 이 기법은 삼각형의 형태를 기준으로 한 개나 두 개의 삼각형을 하나의 연산코드로 분류하거나 삼각형의 연결 상황에 따라 하나의 연산코드로 분류하여 연결정보를 표현한다. 복원(decompression)시에는 연산 코드를 이용하여 삼각형의 연결정보를 뽑아내면 원 상태의 3D 모델을 획득할 수 있다. 이 방법은 연결 정보를 무손실 압축하는 방법으로, 지금까지 제안된 압축기법과 비교할 때, 간단하면서도 월등한 압축 효과를 볼 수 있다.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.431-446
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• 2022

In this study, the characteristics of the experiences and perceptions of vulnerable adolescents on the school environment were explored and categorized, and of which the meanings were confirmed. For this purpose, Q methodology was used, and 94 Q samples were extracted by analyzing pre-interviews, literature analysis, and media search contents to form a Q population. Q classification was performed on the subjects. The schools belonging to the sample P are 7 high schools in Seoul and 4 high schools in Gyeonggi-do. As a result of analyzing the data collected in Q sorting using QUANL-PC program, a Q analysis program, it was classified into 4 types. Type 1 is 'a conformal and moeling type', and Type 2 is 'negative and defiant type', Type 3 is 'passive and helpless type' and Type 4 is 'progressive and struggling type'. Through this study, it is expected that this study will be able to understand the experiences and perceptions of the school environment of underprivileged adolescents, and to obtain the necessary clues for realistic support strategies for them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.4
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• pp.191-199
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• 2014

Currently, HTTP traffic has been developed rapidly due to appearance of various applications and services based web. Accordingly, HTTP Traffic classification is necessary to effective network management. Among the various signature-based method, Payload signature-based classification method is effective to analyze various aspects of HTTP traffic. However, the payload signature-based method has a significant drawback in high-speed network environment due to the slow processing speed than other classification methods such as header, statistic signature-based. Therefore, we proposed various classification method of HTTP Traffic based HTTP signatures of hierarchical structure and to improve pattern matching speed reflect the hierarchical structure features. The proposed method achieved more performance than aho-corasick to applying real campus network traffic.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.77-87
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• 2009

Bayesian methods have been focused in recent years for solving small area estimation problems. In this paper, the hierarchical Bayes procedure is implemented via MCMC techniques and compared with the results of One-way, GLM-Normal, and GLM-Gamma cases by analyzing real data of insurance benefit for customer segmentations. After analyzing insurance benefit real data for customer segmentations, we can conclude that the insurance benefit estimator through the small area estimation is more efficient than the estimators by other methods. In addition, we found that the small area estimation gave accurate estimation result for the small number domains.

• Journal of Korean Society of Transportation
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• v.31 no.4
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• pp.85-94
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• 2013

The Continuous Risk Profile (CRP) has been well known to be the most accurate and efficient among existing network screening methods. However, the classical CRP uses safety performance functions (SPFs) which require a huge investment to construct a database system. This study aims to suggest a new CRP method using average crash frequencies of homogeneous groups, instead of SPFs, as rescaling factors. Hierarchical clustering analysis is performed to classify freeway segments into homogeneous groups based on the data of AADT and number of lanes. Using the data from I-880 in California, the proposed method is compared to other several network screening methods. The results show that the proposed method decrease false positive rates while it does not produce any false negatives. The method developed in this study can be easily applied to screen freeway networks without any additional complex database systems, and contribute to the improvement of freeway safety management systems.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.613-619
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• 2006

This study proposed an unsupervised image classification through the dendrogram of agglomerative clustering as a higher stage of image segmentation in image processing. The proposed algorithm is a hierarchical clustering which includes searching a set of MCSNP (Mutual Closest Spectral Neighbor Pairs) based on the data structures of RAG(Regional Adjacency Graph) defined on spectral space and Min-Heap. It also employes a multi-window system in spectral space to define the spectral adjacency. RAG is updated for the change due to merging using RNV (Regional Neighbor Vector). The proposed algorithm provides a dendrogram which is a graphical representation of data. The hierarchical relationship in clustering can be easily interpreted in the dendrogram. In this study, the proposed algorithm has been extensively evaluated using simulated images and applied to very large QuickBird imagery acquired over an area of Korean Peninsula. The results have shown it potentiality for the application of remotely-sensed imagery.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.961-975
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• 2016

The hierarchically penalized support vector machine (H-SVM) has been developed to perform simultaneous classification and input variable selection when input variables are naturally grouped or generated by factors. However, the H-SVM may suffer from estimation inefficiency because it applies the same amount of shrinkage to each variable without assessing its relative importance. In addition, when analyzing imbalanced data with uneven class sizes, the classification accuracy of the H-SVM may drop significantly in predicting minority class because its classifiers are undesirably biased toward the majority class. To remedy such problems, we propose the weighted adaptive H-SVM (WAH-SVM) method, which uses a adaptive tuning parameters to improve the performance of variable selection and the weights to differentiate the misclassification of data points between classes. Numerical results are presented to demonstrate the competitive performance of the proposed WAH-SVM over existing SVM methods.