• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• English Language & Literature Teaching
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• v.9 no.spc
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• pp.185-208
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• 2003

The purpose of this paper is to examine some of the theories concerning socioaffective strategies, to analyze the dialogues of the students negotiating for meaning of a reading material and to suggest some implications of socioaffective strategies for teaching reading. The examination of the theories - the interaction hypothesis and the sociocultural theory - suggest that the use of socioaffective strategies facilitates more effective understanding of information that is to be found. distributed, and taken in among the participants. The discourse analyses of the students' interaction in a Korean college English reading class show ample evidence of the use of socioaffective strategies that helped them understand the meaning of a text. However, the analyses show that the strategies are mostly used to ask questions concerning the meaning of clauses. Only few analytical questions are raised for some structural and pragmatical features in the text which are crucial to the understanding of its meaning. Imbalance also exists in the types of the questions used by the participants. The analyses indicate that, instead of negotiating more interactively, the students tend to rely upon a more advanced student when they face difficult English sentences. Therefore as a conclusion this paper emphasizes the importance of teaching socioaffective strategies to help students to help themselves to become more cooperative, independent and analytical in reading English texts.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.31-39
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• 2003

It is often assumed that text regions in images are characterized by some distinctive or characteristic spatial frequencies. This feature is highly intuitive, and thus appealing as much. We propose a method of detecting horizontal texts in natural scene images. It is based on the use of two features that can be employed separately or in succession: the frequency of edge pixels across vertical and horizontal scan lines, and the fundamental frequency in the Fourier domain. We confirmed that the frequency features are language independent. Also addressed is the detection of quadrilaterals or approximate rectangles using Hough transform. Since texts that is meaningful to many viewers usually appear within rectangles with colors in high contrast to the background. Hence it is natural to assume the detection rectangles may be helpful for locating desired texts correctly in natural outdoor scene images.

• Human Ecology Research
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• v.57 no.1
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• pp.73-89
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• 2019

This study explores the social phenomenon of the universalization of one-person households through a literature analysis and text mining in order to explore a future direction for Home Economics Education(HEE) development in the one-person household era. From 2010 to 2018, texts from newspaper articles and book content of one-person households were analyzed through R program. The results of the study are as follows. In order to develop students' competency to live a happy life in the one-person household era, it is necessary to: (1) expand the preemptive and collaborative research of HEE, (2) develop and operate a curriculum to raise the living competency to live alone, (3) expand opportunities for secondary school students as well as off-campus youth, middle-aged, and elderly students, and (4) develop various HEE's elective curriculum focusing on the ability to live as one-person household. Also, (5) in order to overcome the psychological and social poverty and isolation of one-person households, HEE should strengthen the learner's ability to form relationships through self-esteem, care of others, community life, communication and conflict resolution education. In conclusion, HEE's independent living competency, relationship formation competency, and practical problem solving competency are all necessary competencies to live in one-person households. In this study, it is meaningful to suggest a future direction for HEE and to use new research methods such as word cloud techniques in the absence of HEE's previous research in relation to the increase of one-person households.

• Journal of Information Science Theory and Practice
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• v.8 no.4
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• pp.67-86
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• 2020

Information need has been one of the main motivations for a person using a search engine. Queries can represent very different information needs. Ironically, a query can be a poor representation of the information need because the user can find it difficult to express the information need. Query Expansion (QE) is being popularly used to address this limitation. While QE can be considered as a language-independent technique, recent findings have shown that in certain cases, language plays an important role. Arabic is a language with a particularly large vocabulary rich in words with synonymous shades of meaning and has high morphological complexity. This paper, therefore, provides a review on QE for Arabic information retrieval, the intention being to identify the recent state-of-the-art of this burgeoning area. In this review, we primarily discuss statistical QE approaches that include document analysis, search, browse log analyses, and web knowledge analyses, in addition to the semantic QE approaches, which use semantic knowledge structures to extract meaningful word relationships. Finally, our conclusion is that QE regarding the Arabic language is subjected to additional investigation and research due to the intricate nature of this language.

• Korean Journal of Biological Psychiatry
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• v.27 no.1
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• pp.18-26
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• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• Journal of Wellbeing Management and Applied Psychology
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• v.2 no.2
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• pp.39-45
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• 2019

The purpose of this study is to solve the problem of revision or abbreviation of questionnaires based on the previous studies suggested by many existing empirical studies. In addition, this study aims to provide the theoretical basis of the research method which has been variously approached since it presents the methodology that can directly measure the research object. For this purpose, this study proposed a more elaborate analysis method using the differences in perception of individuals who are interested in cognitive research. Specifically, the perception gap(D) can be used as an independent variable, a dependent variable, and a moderating variable. And this study suggested an effective research approach using the measurement of perception difference. The difference of perception suggested that it can be used as a measure to overcome the limitations of existing researches used it as independent variables or mediating variables that measure only one factor of expectation and performance or importance and satisfaction. In addition, it is highly likely that various analyzes on the perception differences, which are the result of measuring target factors for the same person, will be quite effective in the situation where follow-up of respondents is difficult. This study is expected to overcome various limitations reported by empirical studies such as scale utilization problem and follow-up survey difficulty. In future research, it was expected that the limitation of the factor derivation process in the research approach could be complemented by web crawling and text mining of big data analysis.

• Hwankyungkyoyuk
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• v.10 no.2
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• pp.133-144
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• 1997

Environmental education in Korea has been emphasized since the Fourth National Curriculum. The Environment curriculum became independent as 'Environment' for middle school and 'Environmental Science' for high school were set as independent subjects upon the Sixth National Curriculum of Korea. And the Enviroment Textbook for middle school was published by the Ministry of Education of Korea. The purpose of this study is to analyze environment textbook for middle school focusing on the organization, the format, questions and illustrations. It was expected that the results of this study could be used by Environment textbook developers. According to the analysis of 'Environment',on the organization, the textbook was consisted of 219 pages and 22 units. A unit was consisted of many subunits and activities. On the content of the textbook, the objectives of subunits stressed more on explanation form than on exemplification form. On the questions, most of them were in <activities> and the objectives of questions stressed on the presentation of the contents. The specific aspect referring results, cause, and judgement, etc. were vary rarely checked. The questions seemed as good, because questions were mostly pertinent to the what to ask, and had a good connection to the main text. By the analysis of illustration, illustrations were mostly functioned as supportive and rarely as decorative, Most of them were photographs and printed all in black-and-white pictures.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.335-345
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• 2009

In this paper, we propose a new web image caption extraction method considering the positional relation between a caption and an image and the lexical similarity between a caption and the main text containing the caption. The positional relation between a caption and an image represents how the caption is located with respect to the distance and the direction of the corresponding image. The lexical similarity between a caption and the main text indicates how likely the main text generates the caption of the image. Compared with previous image caption extraction approaches which only utilize the independent features of image and captions, the proposed approach can improve caption extraction recall rate, precision rate and 28% F-measure by including additional features of positional relation and lexical similarity.

• Korean Journal of Acupuncture
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• v.34 no.4
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• pp.251-264
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• 2017

Objectives : To identify the prevalence and types of spin in randomised controlled trials(RCTs) of obesity with statistically non-significant results for primary outcomes to provide adequate reporting directions. Methods : Spin is specific reporting strategy that could lead the readers to misinterpret the results of RCTs. RCTs on obesity with statistically non-significant primary outcomes published from July 2015 to June 2016 were retrieved from PubMed. All included RCTs were classified into 3 intervention categories. The identification and classification of spin in the included articles was performed by two independent researchers. Results : Among 46 RCTs with statistically non-significant primary outcomes, 32 studies were assessed as having at least one spin in title, abstract or main text. Of these, 9 articles were on complementary and alternative medicine, 7 on western medicine and 16 on dietary supplement and exercise. The frequency of spin among the types of interventions was similar. The most common type of spin was 'focusing on statistical significance within-group comparison' in results section of abstract and main text, and 'focusing only on treatment effectiveness with no consideration of statistical significance' in conclusion section of abstract and main text. Studies where random sequence generation was appropriately done was less likely to have spin. Conclusions : As a majority of obesity RCTs have spin, researchers should pay more attention to adequately interpreting and reporting statistically non-significant results.