• MALSORI
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• no.53
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• pp.37-60
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• 2005

A Study of N-Insertion Preferences in KoreanKyung-A Kook, Ju-Won Kim, Ho-Young LeeSince n-insertion is not an obligatory process in Korean, it is necessary to investigate what factors influence n-insertion preferences and whether n-insertion preferences have been changed over time. To find answers to these questions, an n-insertion preference test using a questionnaire was conducted. 183 words were selected for this test and 167 subjects participated in the test. The results of this test show that the n-insertion preferences were influenced by the speakers' age, the number and structure of the syllable, word class, phonetic environments, and familiarity. It is suggested that the results of this test should be incorporated into the Principles of Standard Pronunciation and in the Grand Dictionary of Standard Korean.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.28-34
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• 1994

In this paper, a new time-domain parameter extraction method for speech recognition is proposed. The suggested emthod is based on the fact that the local peak-to-peak interval, i.e., the interval between maxima and minima of speech waveform is closely related to the frequency component of the speech signal. The parameterization is achieved by a sort of filter bank technique in the time domain. To test the proposed parameter extraction emthod, an isolated word recognizer based on Vector Quantization and Hidden Markov Model was constructed. As a test material, 22 words spoken by ten males were used and the recognition rate of 92.9% was obtained. This result leads to the conclusion that the new parameter extraction method can be used for speech recognition system. Since the proposed method is processed in the time domain, the real-time parameter extraction can be implemented in the class of personal computer equipped onlu with an A/D converter without any DSP board.

• English Language & Literature Teaching
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• v.18 no.2
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• pp.171-187
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• 2012

This study aims to investigate the effects of learning strategy-based early reading instruction for English underachieving students. For this purpose of the study, sixteen learning strategies were driven from the review of previous related literature and the result of the survey conducted to the students and the teachers. Strategy integrated early reading instruction was implemented to nine students for thirteen weeks. The word recognition test was conducted before and after the instruction to examine whether the instruction had effects on the increase of their early reading skill. In addition, in order to investigate certain change in students' affective aspects after the instruction. The research conducted survey to the students. A teacher's field note and students' class journal were also analyzed to verify the results from the quantitative test. The results indicated that the instruction led to the increase of students' early reading skills. It was also found that the instruction motivated the underachieving students to devise a strategy for their learning process.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.221-225
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• 2001

본 논문에서는 대량의 말뭉치를 바탕으로 한국어에 대해 단어 기반의 n-gram 언어 모델과 클래스 기반의 언어 모델을 구축하고, 이를 실험적으로 검증한다. 단어 기반의 n-gram 모델링의 경우 Katz의 백오프와 Kneser-ney의 스무딩(smoothing) 알고리즘에 대해 실험을 수행한다. 클래스 기반의 언어 모델의 경우에는 품사 태그를 단어의 클래스로 사용한 경우와 말뭉치로부터 자동으로 구축된 클래스를 사용한 경우로 나누어 실험한다. 마지막으로 단어 기반 모델과 클래스 기반 모델을 결합하여 각각의 모델과 그 성능을 비교한다. 실험 결과 단어 기반의 언어 모델의 경우 Katz의 백오프에 비해 Knerser-ney의 스무딩이 보다 조은 성능을 나타내었다. 클래스 기반의 모델의 경우 품사 기반의 방범보다 자동 구축된 단어 클래스를 이용하는 방법의 성능이 더 좋았다. 또한, 단어 모델과 클래스 모델을 결합한 모델이 가장 좋은 성능을 나타냈다. 논문의 모든 알고리즘은 직접 구현되었으며 KLM Toolkit이란 이름으로 제공된다.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.183-190
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation, which means changes in all industrial structures, politics, economics and society as well as IT technology, is an important issue. It is difficult to know which research topic is being studied because digital transformation is being studied in various fields. Convergence research is possible because a research topic is studied in various fields such as computer science area and Decision science area. However, it is difficult to know the specific research status of the research topic. In this study, eight research topics were derived using the topic modeling technique of text mining for abstract of academic literature and the trend of each topic was analyzed. We also proposed to create a Topic-Word Proportions Table in the LDA based Topic modeling process to predict the topic of new literature. The results of this study are expected to contribute to advanced convergence research on topic of digital transformation. It is expected that the literature related to each research topic will be grasped and contribute to the design of a new convergence research.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1437-1446
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• 2020

For the last few years, smart devices have begun to occupy an essential place in the life of children, by allowing them to access a variety of language activities and books. Various studies are being conducted on using smart devices for education. Our study extracts images and texts from kids' book with smart devices and matches the extracted images and texts to create new images that are not represented in these books. The proposed system will enable the use of smart devices as educational media for children. A deep convolutional generative adversarial network (DCGAN) is used for generating a new image. Three steps are involved in training DCGAN. Firstly, images with 11 titles and 1,164 images on ImageNet are learned. Secondly, Tesseract, an optical character recognition engine, is used to extract images and text from kids' book and classify the text using a morpheme analyzer. Thirdly, the classified word class is matched with the latent vector of the image. The learned DCGAN creates an image associated with the text.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.640-643
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• 2010

영어권에서는 통계적 기반 의미 오류 검사기에 대한 연구가 활발하게 진행되었으나 한국어에서는 자주 나타나는 오류를 중심으로 오류 단어를 검사할 문맥이나 공기정보를 갖는 규칙 기반의 연구가 이루어져왔다. 본 논문에서는 표제어나 사전 기반 범주화 정보로 표현되고 있는 기존 문법 검사기의 성능을 개선하는 방안으로 한국어 명사 어휘 의미망인 KorLex를 활용하는 방법을 연구한다. 특히, 용언은 목적어나 주어에 사용되는 명사의 표제어가 의미 제약 조건으로 사용되는 예가 많다. 본 논문에서는 용언의 의미 제약 조건을 명사 표제어 단위에서 명사 의미 그룹(class) 단위로 확대하여 문법 검사기의 의미 오류 검사 규칙의 의미 오류 제약 조건을 일반화 하는 방안을 연구한다.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.421-430
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• 2021

The study was conducted to examine the educational experiences and perceptions of college students in the field of physical education as they were taken remote classes in university due to the effects of the social collective infection caused by COVID-19. To achieve the purpose of the study, an online survey was conducted on 278 university students who major in physical education, and the survey questions include the status of remote classes, remote class recognition (preference, and satisfaction level). As for the analysis method, frequency analysis, response sample t-verification, ANOVA, and word-cradle were performed using SPSS 22.0 and R programs, and all significance levels were set at .05. The results from the above research process are as follows. First, in the types of remote classes in the sports category due to COVID-19, video types were used the most in both theoretical and practical classes, and the following was shown as assignment types. The third type was the voice record lecture type for theoretical classes, and the practical class was the video lecture scene. Second, in the remote class preference for the students, both theory and practical classes, video format were the most prefered, followed by video lecture scene and voice lecture type. Third, the analysis of the differences in satisfaction between theoretical and practical classes of the students showed that there was no difference in satisfaction according to the type of class.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.363-372
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• 2013

When retrieving music with folksonomy tags, internal use of numeric tags (AV tags: tags consisting of Arousal and Valence values ) instead of word tags can partially solve the problem posed by synonyms. However, the two predecessor tasks should be done correctly; the first task is to map word tags to their numeric tags; the second is to get numeric tags of the music pieces to be retrieved. The first task is verified through our prior study and thus, in this paper, its significance is seen for the second task. To this end, we propose the music mapping table defining the relation between AV values and music and ANOVA tests are performed for analysis. The result shows that the arousal values and valence values of music have different distributions for 12 mood tags with or without synonymy and that their type I error values are P<0.001. Consequently, it is checked that the distribution of AV values is different according to music mood.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.127-135
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• 2020

Busan is a world-class tourist city representing Korea, and more professional storytelling is needed in the development and promotion of products. The purpose of this study is to convey the image of Busan using storytelling techniques to foreigners, and then examine the correlation between the intention of purchase and oral intention of the related tourist souvenirs. A survey of 162 foreigners was conducted. The results showed that storytelling exposure and visiting experience on tourist sites and the design of tourist products have a certain correlation between the intention of purchase and oral intentions. Attractive souvenir design, visiting experience in the region and storytelling in the area involved are expected to contribute positively to the revisit of foreign tourists and the attraction of potential tourists. This study is meaningful in that it provides a new direction for promoting Busan tourism products, and in the future, the district will conduct additional verification for tourism cities other than Busan to verify the research hypothesis extensively.