• Journal of Korea Entertainment Industry Association
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• v.13 no.6
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• pp.265-275
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• 2019

The purpose of this study was to analysis of the changes of the white noise on the change of the writing ability and hand function of the children with the intellectual disabilities and then provide the basic information about that. The subjects was 12 children with intellectual disabilities. White noise was applied to analyze the subjects' writing ability and hand function before and after application. The provision of the white noise was continuous and uniform through the white noise generator. The analysis of the writing ability was performed by using the KNISE-BAAT assessment and the writing, vocabulary and composing ability were evaluated for the writing ability of the subjects. Also, the analysis of the hand function was performed by using the pegboard sub-item of the Manual Function Test. The results of the writing ability showed the statistically significant increase of the writing and vocabulary ability, but in the case of the composing ability, there was no statistically significant increase in the composing ability. Also, the results of the hand function showed the statistically significant increase in the both hands. The use of the white noise should be considered as a compensatory approach to improve the writing ability and hand function of the children with intellectual disabilities. Also, in order to improve the level of the performance, learning level, and academic achievement of the children of the intellectual disabilities, the application of the white noise in the living and learning environment should be needed to consider.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.33-48
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• 2023

In this paper we consider automatic speech recognition (ASR) for Korean speech data in which elderly persons randomly speak a sequence of words such as animals and vegetables for one minute. Most of the speakers are over 60 years old and some of them are dementia patients. The goal is to compare deep-learning based ASR models for such data and to find models with good performance. ASR is a technology that can recognize spoken words and convert them into written text by computers. Recently, many deep-learning models with good performance have been developed for ASR. Training data for such models are mostly composed of the form of sentences. Furthermore, the speakers in the data should be able to pronounce accurately in most cases. However, in our data, most of the speakers are over the age of 60 and often have incorrect pronunciation. Also, it is Korean speech data in which speakers randomly say series of words, not sentences, for one minute. Therefore, pre-trained models based on typical training data may not be suitable for our data, and hence we train deep-learning based ASR models from scratch using our data. We also apply some data augmentation methods due to small data size.

• Journal of the Korean Society for Library and Information Science
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• v.46 no.1
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• pp.163-181
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• 2012

We investigated how useful video tags were in summarizing video speech and how valuable positional information was for speech summarization. Furthermore, we examined the similarity among sentences selected for a speech summary to reduce its redundancy. Based on such analysis results, we then designed and evaluated a method for automatically summarizing speech transcripts using a modified Maximum Marginal Relevance model. This model did not only reduce redundancy but it also enabled the use of social tags, title words, and sentence positional information. Finally, we compared the proposed method to the Extractor system in which key sentences of a video speech were chosen using the frequency and location information of speech content words. Results showed that the precision and recall rates of the proposed method were higher than those of the Extractor system, although there was no significant difference in the recall rates.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.491-497
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• 2009

This paper proposes a new feature weighting method for document sentiment classification. The proposed method considers the difference of sentiment intensities among sentences in a document. Sentiment features consist of sentiment vocabulary words and the sentiment intensity scores of them are estimated by the chi-square statistics. Sentiment intensity of each sentence can be measured by using the obtained chi-square statistics value of each sentiment feature. The calculated intensity values of each sentence are finally applied to the TF-IDF weighting method for whole features in the document. In this paper, we evaluate the proposed method using support vector machine. Our experimental results show that the proposed method performs about 2.0% better than the baseline which doesn't consider the sentiment intensity of a sentence.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.333-340
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• 2009

Most of the previous automatic word spacing systems are not suitable to use for mobile devices with relatively low computing powers because they require many system resources. We propose an automatic word spacing system that requires reasonable memory usage and simple numerical computations for mobile devices with low computing powers. The proposed system is a two step model that consists of a statistical system and a rule-based system. To reduce the memory usage, the statistical system first corrects word spacing errors by using a modified hidden Markov model based on character unigrams. Then, to increase the accuracy, the rule-based system re-corrects miscorrected word spaces by using lexical rules based on character bigrams or more. In the experiments, the proposed system showed relatively high accuracy of 94.14% in spite of small memory usage of about 1MB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1318-1324
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• 2007

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.255-273
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• 2019

Recently, the table of contents (TOC) has been becoming increasingly accessible and utilized. The study conducted descriptive statistics and comparative analysis of the table of contents in terms of parts of speech and subject in text. For this purpose, this study chose the books of the social sciences field from acquisition lists of an academic library, obtained Dewey class numbers of target books from KERIS union catalog, and extracted TOC data from online bookstore. Morphological analysis was performed on each book titles and TOCs, and descriptive statistics and frequency analysis were carried out. As a result, nouns made up roughly half of the morphemes of titles or the TOCs. TOCs had about 50 times more nouns than titles. The percentage of unique nouns that appeared only in the table of contents is estimated to be 95.2% of the TOC's total nouns. The table of contents also showed a differences in its lengths depending on the field of social science.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.61-72
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• 2024

The purpose of this study is to investigate retest reliabilities in longitudinal study, the same test is administered repeatedly over time. Linear mixed models were used to establish various situations of tests occurred in longitudinal study. Combination of two types of true value and three types of systematic error was considered. In order to apply the models to real longitudinal data, height data from the Berkeley growth study and vocabulary score data from the University of Chicago experimental school were used. Using the mixed model, there is an advantage that the reliability can be determined by selecting the covariance structure of the true value and the error separately. However, in order to properly analyze the reliability, researchers need to consider variations that can occur in measurement, such as characteristics of subject, the test, and the the treatment applied in the study. And the proper model should be selected and the quality of the measurement should be evaluated for each trial.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.149-152
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• 2013

이 연구는 스마트교육환경에서의 교육용 앱을 평가하기 위한 신뢰롭고 타당한 도구를 개발하는데 있다. 기존 선행연구에 기초해서 교육용 앱의 평가를 위한 평가모형을 도출했으며, 이 모형은 4개의 평가영역(교수 학습측면, 화면디자인측면, 기술측면, 경제 윤리측면)과 13개 평가요소들로 구성되었다. 이 잠재모형의 통계적 타당성 검증을 위한 자료수집을 하고자, 경기도 소재 중학교 1곳과 고등학교 2곳의 학생 156명을 대상으로 교육용 앱을 평가하는데 있어서 각 평가문항이 갖는 중요도를 평가하였다. 수집된 자료를 탐색적 요인분석한 결과, 교육용 앱을 평가하는 영역으로 교수 학습(흥미성 자기주도성 실용성, 인지발달성), 화면디자인(디자인의 적합성, 어휘의 정확성), 기술(호환성, 안정성), 경제 윤리(경제성, 윤리성) 등 4개 영역이 제안되었다. 또한 문항내적일관성을 확인하고자 신뢰도 분석한 결과, 각 평가영역 별 Cronbach ${\alpha}$는 .88, .85, .82, .80으로 모두 적합한 수준을 보였다. 따라서, 이 연구를 통해 도출된 교육용 앱 평가도구는 통계적으로나 타당성과 신뢰성 측면에서 의미 있는 것으로 판단할 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.4
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• pp.149-154
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• 2014

A user should pick up relevant answers by himself from various search results when using user participation question answering community like Knowledge-iN. If refined answers are automatically provided, usability of question answering community must be improved. This paper divides questions in Q&A documents into 4 types(word, list, graph and text), then proposes summarizing methods for each question type using document statistics. Summarized answers for word, list and text type are obtained by question clustering and calculating scores for words using frequency, proximity and confidence of answers. Answers for graph type is shown by extracting user opinion from answers.