• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.414-420
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• 2007

For a more convenient deduction of the weighted values in AHP of BSC using a spread sheet program, this study derived each weighted values through single and group calculations and it also compared the two calculations to verify mutual identities. Pairwise comparison is generally used in measuring performance of corporations or government organizations, but, many researches have been done without reliability verification due to difficulty in the deduction of weighted value. This trend, like using a wrong measurement, result in defective result of BSC. Therefore, this study presents various methods of single and group case measurement using spread sheet so that it can be utilized in practice. Thus, I expect this study's result be availed in BSC consulting or research of public organizations that have difficulty in measuring qualitative factors.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.747-751
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• 2010

This paper proposes the method that increases the accuracy for tagging word meaning by creating sense tagged data automatically using machine readable dictionaries. The concept of meaning group is applied here, where the meaning group for each meaning of a target word consists of neighbor words of the target word. To enhance the tagging accuracy, the notion of concentration is used for the weight of each word in a meaning group. The tagging result in SENSEVAL-2 data shows that accuracy of the proposed method is better than that of existing ones.

• Annual Conference on Human and Language Technology
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• 2003.10d
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• pp.14-17
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• 2003

문서의 확률을 이용하여 자동으로 문서를 분류하는 문서 범주화 기법의 대표적인 방법이 나이브 베이지언 확률 모델이다. 이 방법의 기본 형식은 출현 용어의 확률 계산 방법이다. 하지만 실제 문서 범주화 과정에서 출현하지 않는 용어들도 성능에 많은 영향을 줄 수 있으며, 출현 용어들에 대한 빈도 이외의 역범주 빈도나 용어가중치를 적용하여 문서 범주화 시스템의 성능을 향상시킬 수 있다. 본 논문에서는 나이브 베이지언 확률 모델에 출현 용어와 출현하지 않는 용어들에 대한 smoothing 기법을 적용하여 실험하였다. 성능 평가를 위해 뉴스그룹 문서들을 이용하였으며, 역범주 빈도와 가중치를 적용했을 때 나이브 베이지언 확률 모델에 비해 약 7% 정도 성능 개선 효과가 있었다.

• Journal of Digital Contents Society
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• v.4 no.1
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• pp.81-90
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• 2003

Multimedia Platform box based on distributed environments have much effect on objects grouping for management of distributed resources. This paper utilizes weight and fuzzy filtering techniques for objects platform in distributed multimedia environments. Weight and Fuzzy filtering techniques perform grouping by references relation of multimedia objects and this paper proposes object dictionary structure.

• 한국IT서비스학회:학술대회논문집
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• 2005.11a
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• pp.167-175
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• 2005

디지털콘텐츠의 시장이 확대됨에 따라 디지털콘텐츠사업의 중요성이 점점 대두되고 있다. 국내의 디지털콘텐츠시장은 2002년부터 매년 1조 이상의 성장을 기록하며 2004년 약 6조 5000억 원의 시장을 기록하고 있다. 디지털콘텐츠시장의 성장은 비단 국내뿐만 아니라 일본에서도 폭발적으로 증가하고 있는데 일본의 2004년 디지털콘텐츠시장 규모는 169억 8200달러로 추정되고 앞으로 그 규모는 더욱 커질 것 이라 예상되고 있다. 본 논문은 국내에서 질적, 양적으로 성장하고 있는 디지털콘텐츠를 일본시장에 진출할 때 고려되어야 할 전략적인 요소들과 이들의 중요도를 제안하는 것이다. 연구 방법으로는 SWOT/AHP방식을 이용하였다. 연구 순서는 일본 디지털콘텐츠시장에 국내 기업이 진출할 때 국내 기업에 대한 SWOT분석을 실시하고, 이를 다시 AHP를 통하여 SWOT의 4가지 그를 강점, 약점, 기회, 위협의 가중치와 각 그룹 내의 요인들에 대한 개별적인 가중치를 산출한다. 연구의 결과로 도출된 가중치들은 일본 디지털콘텐츠시장에 진입할 때 무엇을 더 고려해줘야 하는지를 나타내고 있어 전략 수립의 기초로 삼을 수 있을 것으로 생각된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1009-1012
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• 2002

정보검색시스템에서 가장 중요한 것은 사용자의 요구에 부합하는 결과를 도출하는 것이다. 이를 위하여 사용자의 질의와 연관된 모든 문서들을 추출하게 되는데, 이 많은 결과 문서들 중에서 사용자가 원하는 문서는 소수이고, 원하는 문서를 찾는 것도 쉽지 않다. 따라서 적절한 결과 문서 도출을 위하여 연관된 문서들끼리 그룹화 시키는 클러스터링 방법이 많이 이용된다. 본 논문에서는 클러스터링에 영향을 끼치는 요소 중 문서별 색인어의 가중치가 클러스터링에 끼치는 영향을 알아보았다. 이를 위해 가중치의 변화에 따른 클러스터링 된 결과를 LSI 를 이용하여 도식화하고 그 결과를 분석하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5089-5096
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• 2010

Text categorization is one of important features in information searching system which classifies documents according to some criteria. The general method of categorization performs the classification of the target documents by eliciting important index words and providing the weight on them. Therefore, the effectiveness of algorithm is so important since performance and correctness of text categorization totally depends on such algorithm. In this paper, an enhanced method for text categorization by improving word weighting technique is introduced. A method called Okapi BM25 has been proved its effectiveness from some information retrieval engines. We applied Okapi BM25 and showed its good performance in the categorization. Various other words weights methods are compared: TF-IDF, TF-ICF and TF-ISF. The target documents used for this experiment is Reuter-21578, and SVM and KNN algorithms are used. Finally, modified Okapi BM25 shows the most excellent performance.

• Journal of KIISE
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• v.43 no.10
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• pp.1144-1153
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• 2016

Group recommendation analyzes the characteristics and tendency of a group rather than an individual and provides relevant information for the members of the group. Existing group recommendation methods merely consider the average and frequency of a preference. However, if the users' preferences have large deviations, it is difficult to provide satisfactory results for all users in the group, although the average and frequency values are high. To solve these problems, we propose a method that considers not only the average of a preference but also the deviation. The proposed method provides recommendations with high average values and low deviations for the preference, so it reflects the tendency of all group members better than existing group recommendation methods. Through a comparative experiment, we prove that the proposed method has better performance than existing methods, and verify that it has high performance in groups with a large number of members as well as in small groups.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.496-502
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• 2021

Recently, deep speaker embedding approach has been widely used in text-independent speaker verification, which shows better performance than the traditional i-vector approach. In this work, to improve the deep speaker embedding approach, we propose a novel method called group-based speaker embedding which incorporates group information. We cluster all speakers of the training data into a predefined number of groups in an unsupervised manner, so that a fixed-length group embedding represents the corresponding group. A Group Decision Network (GDN) produces a group weight, and an aggregated group embedding is generated from the weighted sum of the group embeddings and the group weights. Finally, we generate a group-based embedding by adding the aggregated group embedding to the deep speaker embedding. In this way, a speaker embedding can reduce the search space of the speaker identity by incorporating group information, and thereby can flexibly represent a significant number of speakers. We conducted experiments using the VoxCeleb1 database to show that our proposed approach can improve the previous approaches.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.101-109
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• 2008

In this paper, we introduce clustering scheme to calculate probability of detection which is practically required for conventional weighted-collaborative sensing technique. We also propose an improved weighted-collaborative spectrum sensing scheme using new weight generation algorithm to achieve better performance in Cognitive Radio systems. We calculate Pd in each cluster which is a CR users group with similar channel situation. New weight factor is generated using square sum of all cluster's Pds. Simulations under slow fading show that we can get better total detection probability and lower false alarm rate when PU (Primary User) suddenly terminates their transmission.