• 인지과학
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• 제17권3호
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• pp.207-229
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• 2006

작업기억의 읽기 범위에 따른 개인차가 무관련 정보의 억제과정에 작용하는 과정을 살펴보기 위해서 두 개의 독립적인 실험을 실시하였다. 실험 1은 탐사재인과제을 사용하였으며 작업기억 범위가 작은 폭 집단에 비해 큰 폭 집단에서 관련정보의 촉진적 활성화 기제보다는 무관련 정보의 선택적 억제기제가 잘 작동한다는 결과를 얻었다. 실험 2의 어휘판단과제에서도 동일한 결과를 보였으며 두 실험 모두 큰 폭 집단이 작은 폭 집단에 비해 기억부담의 영향을 받지 않는다는 공통된 결과를 얻었다. 이러한 결과는 억제가 작업기억 개인차를 설명하는 중요한 요인임을 시사해준다.

• 디자인학연구
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• 제21권
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• pp.219-231
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• 1997

의복은 단순한 상징적 체계, 즉 입는다는 기능성에 벗어나 미학의 한 위상으로, 의미를 전달하는 기호코드로 새로운 자리 매김을 하고 있다. 이러한 현상을 파악하고자, 의상에서 인체의 부재를 통해 전달하고자 하는 의미를 강조한 3점의 작품을 분석한 결과, 1) 입음/걸려짐, 긍정적/부정적, 능동적/수동적, 있음/없음을 통해 남성/여성의 의미를 대립시키고, 2)사회적인/사적인, 인체/옷을 통해 남성과 여성을 적절함/부적절함, 주체적인/부수적인 의미로 환원시켰다. 3) 하나의/다수의, 단순함/다양함, 제한적인/자유로운 의미를 통해 옷 입는 방법에 대한 사유를 하고 있음을 알 수 있었다.

• 공학교육연구
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• 제17권6호
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• pp.3-11
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• 2014

This study investigated the effects of WIE(Women into Engineering) programs on women engineers' employment and career duration. For this study, the 91 female graduates with a degree in the field of engineering of K university who graduated from 2010 to 2013 were collected. The female graduates with a degree in the field of engineering of K university were divided into 4 groups-an employment group of relevant field of major, an employment group of irrelevant field of major, a group of graduate school, and a group of unemployment. The results were: first, the female graduates' program participation scores showed meaningful diversity according to the types of employment of female graduates with a degree in the field of engineering. Among the 4 types of employment of female graduates with a degree in the field of engineering, the group of graduate schoolers got the highest program participation scores, and the employment group of relevant field of major got the second scores, the group of unemployment got the third scores, and the employment group of irrelevant field of major got the last scores. Second, The female graduates' program participation scores showed meaningful diversity according to the career duration of female graduates with a degree in the field of engineering. The career duration in accordance with the program participation scores showed meaningful difference between the employment group of relevant field of major and the employment group of irrelevant field of major.

• 품질경영학회지
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• 제47권3호
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• pp.537-552
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• 2019

Purpose: The development of information technology makes it easy to utilize high-dimensional categorical data. In this regard, the purpose of this study is to propose a novel method to select the proper categorical variables in high-dimensional categorical data. Methods: The proposed feature selection method consists of three steps: (1) The first step defines the goodness-to-pick measure. In this paper, a categorical variable is relevant if it has relationships among other variables. According to the above definition of relevant variables, the goodness-to-pick measure calculates the normalized conditional entropy with other variables. (2) The second step finds the relevant feature subset from the original variables set. This step decides whether a variable is relevant or not. (3) The third step eliminates redundancy variables from the relevant feature subset. Results: Our experimental results showed that the proposed feature selection method generally yielded better classification performance than without feature selection in high-dimensional categorical data, especially as the number of irrelevant categorical variables increase. Besides, as the number of irrelevant categorical variables that have imbalanced categorical values is increasing, the difference in accuracy between the proposed method and the existing methods being compared increases. Conclusion: According to experimental results, we confirmed that the proposed method makes it possible to consistently produce high classification accuracy rates in high-dimensional categorical data. Therefore, the proposed method is promising to be used effectively in high-dimensional situation.

• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.874-886
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• 2019

To delve into the semantic gap problem of the automatic video summarization, we focused on an endogenous ERP responses at around 400ms and 600ms after the on-set of audio-visual stimulus. Our experiment included two factors: the topic exploration of experimental conditions (Topic Given vs. Topic Exploring) as a between-subject factor and the topic relevance of the shots (Topic-Relevant vs. Topic-Irrelevant) as a within-subject factor. For the Topic Given condition of 22 subjects, 6 short historical documentaries were shown with their video titles and written summaries, while in the Topic Exploring condition of 25 subjects, they were asked instead to explore topics of the same videos with no given information. EEG data were gathered while they were watching videos in real time. It was hypothesized that the cognitive activities to explore topics of videos while watching individual shots increase the amplitude of endogenous ERP at around 600 ms after the onset of topic relevant shots. The amplitude of endogenous ERP at around 400ms after the onset of topic-irrelevant shots was hypothesized to be lower in the Topic Given condition than that in the Topic Exploring condition. The repeated measure MANOVA test revealed that two hypotheses were acceptable.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.1389-1391
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• 2011

In this paper, we propose a methodology to annotate the digital documents through keyphrase extraction using domain specific taxonomy. Limitation of the existing keyphrase extraction algorithms is that output keyphrases may contain irrelevant information along with relevant ones. The quality of the generated keyphrases by the existing approaches does not meet the required level of accuracy. Our proposed approach exploits semantic relationships and hierarchical structure of the classification scheme to filter out irrelevant keyphrases suggested by Keyphrase Extraction Algorithm (KEA++). Our experimental results proved the accuracy of the proposed algorithm through high precision and low recall.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.669-672
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• 2001

We propose an efficient algorithm for an interactive content-based image retrieval using relevance feedback. In the proposed algorithm, a new query feature vector first is yielded from the average feature vector of the relevant images that is fed back from the result images of the previous retrieval. Each component weight of a feature vector is computed from an inverse of standard deviation for each component of the relevant images. The updated feature vector of the query and the component weights are used in the iterative retrieval process. In addition, the irrelevant images are excluded from object images in the next iteration to obtain additional performance improvement. In order to evaluate the retrieval performance of the proposed method, we experiment for three image databases, that is, Corel, Vistex, and Ultra databases. We have chosen wavelet moments, BDIP and BVLC, and MFS as features representing the visual content of an image. The experimental results show that the proposed method yields large precision improvement.

• 경영과학
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• 제23권1호
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

• Journal of Advanced Marine Engineering and Technology
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• 제40권5호
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• pp.437-446
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• 2016

Feature selection has become an essential technique to reduce the dimensionality of data sets. Many features are frequently irrelevant or redundant for the classification tasks. The purpose of feature selection is to select relevant features and remove irrelevant and redundant features. Applications of the feature selection range from text processing, face recognition, bioinformatics, speaker verification, and medical diagnosis to financial domains. In this study, we focus on filter methods based on information entropy : IG (Information Gain), FCBF (Fast Correlation Based Filter), and mRMR (minimum Redundancy Maximum Relevance). FCBF has the advantage of reducing computational burden by eliminating the redundant features that satisfy the condition of approximate Markov blanket. However, FCBF considers only the relevance between the feature and the class in order to select the best features, thus failing to take into consideration the interaction between features. In this paper, we propose an improved FCBF to overcome this shortcoming. We also perform a comparative study to evaluate the performance of the proposed method.

• 한국수학교육학회지시리즈A:수학교육
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• 제54권2호
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• pp.143-165
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• 2015

The purpose of this study is to find out the level relevance of the kindergarten curriculum in terms of the kindergarten and elementary school curriculum articulation. For this purpose, a model was developed to assess the level relevance of the curriculum. Next, the achievement standards of the curriculum were analyzed by using this model. Finally, teachers' guidebooks were analyzed, too. The following results were obtained from the analysis. First, five of the 14 achievement standards are rated as 'relevant', and nine of them were 'irrelevant'. Also, six of the irrelevant achievement standards were rated as 'overlap', two of them were rated as 'retrogression', and one of them was rated as 'gap'. I found a lot of problems with the level relevance in the kindergarten curriculum. As the results to analyze teachers' guidebooks, I found that there were the great frequency difference in the activities of teachers' guidebooks.