• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1571-1574
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• 2012

소프트웨어 개발에 있어 각 단계별 프로세스 활동들에 대한 분석 및 평가는 소프트웨어의 품질을 좌우하는 큰 요인이다. 따라서 많은 소프트웨어 척도들이 소프트웨어 품질을 분석하는데 이용되고 있으며 유사 프로젝트를 통해 설정되는 기준치와 척도 값의 비교가 수행된다. 하지만 기존의 유사 프로젝트를 찾기란 쉽지 않은 일이며 유사 프로젝트를 찾더라도 해당 프로젝트의 개발 환경은 현재 개발 중인 프로젝트의 환경과 다른 경우가 많다. 따라서 본 논문에서는 외적인 기준치에 의존하지 않고 현재 개발 단계의 인스펙션 결과를 분석하는 방법을 제시하도록 한다. 산포도를 이용한 상대적 데이터 분석이 이용되며 국방 도메인에서 개발 중인 프로젝트 내부 31 개의 기능으로부터 수집된 데이터를 통한 사례분석을 수행하도록 한다. 이를 통해 기능들 간 현재 개발 과정의 일관성 유지 여부를 평가하고 다음 개발 단계의 프로세스 활동 강화 여부에 대한 권고 사항을 제시할 수 있다.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.595-616
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• 2023

The Critical Consciousness Scale (CCS) is a scale developed by Diemer and colleagues (2017) that can measure the capacity of the oppressed or marginalized people to critically analyze their social and political conditions, support societal equality, and take action to change the perceived inequities. In this study, we validated the CCS for Korea by adapting and localizing the scale and validating it among university students. Content validity was verified by having five individuals with master's and doctoral degrees in psychology evaluate the suitability of the translated items. Afterwards, reliability and validity were verified through a survey of 314 university students nationwide using the CCS, along with the opportunity inequality recognition scale, recognition of the need for environmental change scale, social participation scale, and belief in a just world scale. To verify the scale's validity, exploratory factor analysis was conducted, confirming three subfactors. Then, a confirmatory factor analysis was carried out, where 14 items out of the original 22 were retained. The construct validity and reliability of these 14 items were found to be satisfactory. Additionally, in the correlation analysis between the CCS and similar scales, a significant clear relationship was found. The CCS showed a positive correlation with scales such as opportunity inequality recognition, need for environmental change recognition, and social participation, and a negative correlation with the belief in a just world scale. Based on these results, the CCS can be considered valid and reliable. Finally, the limitations and significance of this study were discussed.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.220-229
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• 2002

협력적 추천 기법은 유사한 이웃의 선호도를 이용하여 고객에게 개인화된 아이템을 추천해 주는 방법으로 비교적 높은 정확도를 보이며 추천 시스템의 중심으로 연구되어져 왔다. 그러나, 지금까지의 추천 시스템은 도메인의 특성을 제대로 고려하지 못한채 추천을 시행함으로써 특정 도메인에서 추천의 정확도가 떨어지는 문제점이 발생하였다. 이러한 문제점들을 보완하기 위하여 본 논문에서는 평균 고객 유사도, 평균 아이템 유사도, 밀집도 등의 추천 선행 평가 척도를 제안하고, 추천 선행평가 척도와 추천의 정확도와의 상관관계를 보이며, 이를 이용하여 짧은 수행시간 안에 추천 적용이 가능한 마케팅 도메인 및 고객군을 선정하는 방법을 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.343-347
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• 2010

This paper presents an efficient image recognition method using the hybrid coefficient measure of correlation and distance. The correlation coefficient is applied to measure the statistical similarity by using Pearson coefficient, and distance coefficient is also applied to measure the spacial similarity by using city-block. The total similarity among images is calculated by extending the similarity between the feature vectors, then the feature vectors can be extracted by PCA and ICA, respectively. The proposed method has been applied to the problem for recognizing the 960(30 persons * 4 expressions * 2 lights * 4 poses) facial images of 40*50 pixels. The experimental results show that the proposed method of ICA has a superior recognition performances than the method using PCA, and is affected less by the environmental influences so as lighting.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.47-53
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• 2021

Sparse ratings data hinder reliable similarity computation between users, which degrades the performance of memory-based collaborative filtering techniques for recommender systems. Many works in the literature have been developed for solving this data sparsity problem, where the most simple and representative ones are the methods of utilizing Jaccard index. This index reflects the number of commonly rated items between two users and is mostly integrated into traditional similarity measures to compute similarity more accurately between the users. However, such integration is very straightforward with no consideration of the degree of data sparsity. This study suggests a novel idea of applying different similarity measures depending on the numeric value of Jaccard index between two users. Performance experiments are conducted to obtain optimal values of the parameters used by the proposed method and evaluate it in comparison with other relevant methods. As a result, the proposed demonstrates the best and comparable performance in prediction and recommendation accuracies.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.83-89
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• 2022

As a representative technique of recommender systems, collaborative filtering has been successfully in service through many commercial and academic systems. This technique recommends items highly rated by similar neighbor users, based on similarity of ratings on common items rated by two users. Recently research on time-aware recommender systems has been conducted, which attempts to improve system performance by reflecting user rating time of items. However, the decay rate uniform to past ratings has a risk of lowering the rating prediction performance of the system. This study proposes a rating time-aware similarity measure between users, which is a novel approach different from previous ones. The proposed approach considers changes of similarity value over time, not item rating time. In order to evaluate performance of the proposed method, experiments using various parameter values and types of time change functions are conducted, resulting in improving prediction accuracy of existing traditional similarity measures significantly.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.163-170
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• 2023

The user-based collaborative filtering technique, one of the implementation methods of the recommendation system, recommends the preferred items of neighboring users based on the calculations of neighboring users with similar rating histories. However, it fundamentally has a data scarcity problem in which the quality of recommendations is significantly reduced when there is little common rating history. To solve this problem, many existing studies have proposed various methods of combining Jaccard index with a similarity measure. In this study, we introduce a time-aware concept to Jaccard index and propose a method of weighting common items with different weights depending on the rating time. As a result of conducting experiments using various performance metrics and time intervals, it is confirmed that the proposed method showed the best performance compared to the original Jaccard index at most metrics, and that the optimal time interval differs depending on the type of performance metric.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.213-216
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• 2003

본 논문에서는 교사가 학생의 성적을 효과적으로 평가하기 위하여 유사 척도 방법을 이용한 서술식 성적평가 시스템을 제안한다. 사용자(교사)로부터 수행평가요소의 결과와 과목의 최종적인 평가를 퍼지 추론에 적용하여 객관적인 성적평가를 한 후, 추론규칙과 실제 학생의 점수의 유사도를 이용하여 가장 높은 값의 성적평가 문장을 추출하여 서술식 평가 문장을 생성하도록 하였다.

• The Journal of Korean Association of Computer Education
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• v.19 no.1
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• pp.111-118
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• 2016

Measuring similarity in collaborative filtering-based recommender systems greatly affects system performance. This is because items are recommended from other similar users. In order to overcome the biggest problem of traditional similarity measures, i.e., data sparsity problem, this study suggests a new similarity measure that is the optimal combination of previous similarity and the value reflecting the number of co-rated items. We conducted experiments with various conditions to evaluate performance of the proposed measure. As a result, the proposed measure yielded much better performance than previous ones in terms of prediction qualities, specifically the maximum of about 7% improvement over the traditional Pearson correlation and about 4% over the cosine similarity.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.4
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• pp.310-316
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• 2014

The location template matching(LTM) method is a technique of identifying an impact location on a structure, and requires a certain measure of similarity between two time signals. In general, the correlation coefficient is widely used as the measure of similarity, while the group delay based method is recently proposed to improve the accuracy of the impact localization. Another possible measure is the frequency response assurance criterion(FRAC), though this has not been applied yet. In this paper, these three different measures of similarity are examined comparatively by using experimental data in order to understand the properties of these measures of similarity. The comparative study shows that the correlation coefficient and the FRAC give almost the same information while the group delay based method gives the shape oriented information that is best suitable for the location template matching method.