• Korean Journal of Cognitive Science
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• v.17 no.3
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• pp.207-229
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• 2006

Two experiments examined the relation of working memory capacity and the inhibition mechanism in working memory. Experiment 1 demonstrated that the high WM span group inhibited irrelevant information selectively and the low WM span group maintained both relevant and irrelevant information within WM. Experiment 2 showed that there were similar patterns of response time between high and low WM span groups in the lexical decision task. These results suggest that the ability to maintain relevant information and inhibit irrelevant information selectively in WM is an important factor of individual differences in working memory.

• Archives of design research
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• v.21
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• pp.219-231
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• 1997

Now, Clothing has new position which is a part of aesthetics and symbolic code. To investigate this phenomenon, three works which express the meaning with "absence of body" in clothing were analyzed. In results, 1) the confrontation of male/female was represented by positive/negative, active /passive, present/absent. 2) male/female means sociaVprivate, body/clothing the relevant/the irrelevant subjective/additive. 3) one/numbers, simple/various, limited! free present the various way of wearingrious way of wearing

• Journal of Engineering Education Research
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• v.17 no.6
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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.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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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.

• Journal of Korea Multimedia Society
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• v.22 no.8
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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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.

• Proceedings of the IEEK Conference
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• 2001.09a
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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.

• Korean Management Science Review
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• v.23 no.1
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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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• v.40 no.5
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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.

• The Mathematical Education
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• v.54 no.2
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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.