• Journal of Yeungnam Medical Science
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• v.34 no.1
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• pp.62-68
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• 2017

Background: This study was conducted to examine the academic achievements of first year medical students in one medical school based on their characteristics and student selection factors of admission. Methods: The admission scores of student selection factors (Medical Education Eligibility Test [MEET], grade point average [GPA], English test score and interview) and demographic information were obtained from 61 students who had interviewed (multiple mini interview [MMI]) for admission (38 graduate medical school students in 2014, 23 medical college-transfer students in 2015). T-tests and ANOVA were used to examine the differences in academic achievement according to the student characteristics. Correlations between admission criteria scores and academic achievements were examined. Results: MEET score was higher among graduate medical students than medical college transfer students among student selection factors for admission. There were no significant differences in academic achievement of first grade medical school between age, gender, region of high school, years after graduation and school system. The lowest interview score group showed significantly lower achievement in problem-based learning (PBL) (p=0.034). Undergraduate GPA score was positively correlated with first grade total score (r=0.446, p=0.001) among admission scores of student selection factors. Conclusion: Students with higher GPA scores tend to do better academically in their first year of medical school. In case of interview, academic achievement did not lead to differences except for PBL.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.435-439
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• 2007

Document categorization system is important in the internet age in which huge number of documents are created and need to be dealt with. By this reason a lot of research has been done in this field. For the development of the system, a supervised learning method is widely used. This approach needs a test collection as a prerequisite. For the case of English, several test collections are available which provide a lot of help for developing systems and doing research. But no public test collections have been reported and are not available in the case of Korean. To improve the situation for Korean we are undergoing the construction of a Korean test collection. In this paper the approaches being used and current stage of the collection will be described.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.297-304
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• 2010

Clustering method which based on sentence type or document genre is a technique used to improve translation quality of SMT(statistical machine translation) by domain-specific translation. But there is no previous research using sentence type and document genre information simultaneously. In this paper, we suggest an integrated clustering method that classifying sentence type by syntactic structure similarity and document genre by word similarity information. We interpolated domain-specific models from clusters with general models to improve translation quality of SMT system. Kernel function and cosine measures are applied to calculate structural similarity and word similarity. With these similarities, we used machine learning algorithms similar to K-means to clustering. In Japanese-English patent translation corpus, we got 2.5% point relative improvements of translation quality at optimal case.

• Journal of the Korean Chemical Society
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• v.54 no.4
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• pp.465-470
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• 2010

Using interesting video clips corresponding to lesson subjects for students who favour visual representation is one of the good methods to enhance students' preference for science class. There are many moving picture web sites to get video clips easily via internet and 'YouTube' is very popular and one of the largest reservoir. In this study, every student in the 'Chemistry I' class, which is a class for 11th grade, was requested to search a video clip corresponding to lesson subjects and to make a presentation in the class. After 1st semester, students' response about the class using YouTube was examined by survey. As a result, students preferred and were interested in the class using YouTube than class centered on textbook. And students preferred YouTube clips showing unusual experiments that were related with contents of subject. In addition, experiments and watching their real phenomena were an interesting factor and helpful factor of learning chemistry in YouTube video clips, respectively. However, translation of English used in the video clips seemed to be a difficult part for students.

• Phonetics and Speech Sciences
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• v.12 no.3
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• pp.55-63
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• 2020

Voice conversion can be applied to various voice processing applications. It can also play an important role in data augmentation for speech recognition. The conventional method uses the architecture of voice conversion with speech synthesis, with Mel filter bank as the main parameter. Mel filter bank is well-suited for quick computation of neural networks but cannot be converted into a high-quality waveform without the aid of a vocoder. Further, it is not effective in terms of obtaining data for speech recognition. In this paper, we focus on performing voice-to-voice conversion using only the raw spectrum. We propose a deep learning model based on the transformer network, which quickly learns the voice conversion properties using an attention mechanism between source and target spectral components. The experiments were performed on TIDIGITS data, a series of numbers spoken by an English speaker. The conversion voices were evaluated for naturalness and similarity using mean opinion score (MOS) obtained from 30 participants. Our final results yielded 3.52±0.22 for naturalness and 3.89±0.19 for similarity.

• The Journal of the Acoustical Society of Korea
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• v.31 no.4
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• pp.235-243
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• 2012

This paper studies characteristics and causes of sound, and many others by distinguishing passivity and activity of the cases of a dog vocalizing human's words. As a result of the previous cases of vocalization of human's words, the dog was able to understand characteristics of a host's voice and imitate the sound using his own vocal organs. This is the case of passive vocalization accompanied by temporary voice imitation without a function of communication. On the contrary, as a consequence of the recently reported case in which a dog vocalizes such words as "Um-ma" and "Nu-na-ya," it shows the vocalization pattern clearly distinguished from the prior cases. The given dog repeatedly vocalizes pertaining words in an active manner according to circumstances and plays a role of fundamental communication and interaction with its host. The reason why the dog can vocalize the man's words actively is determined to be that the dog has a high level of intelligence and intimacy with its host, that people react actively to its pertaining pronunciation, and so forth. The following results can be used for the study that investigates animals' sound with vocalization possibility and language learning feasibility.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.7-13
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• 2020

With the recent World Health Organization (WHO) Declaration of Pandemic for Coronavirus Disease-19 (COVID-19), COVID-19 is a global concern and many deaths continue. To overcome this, there is an increasing need for sharing information between countries and countermeasures related to COVID-19. However, due to linguistic boundaries, smooth exchange and sharing of information has not been achieved. In this paper, we propose a Neural Machine Translation (NMT) model specialized for the COVID-19 domain. Centering on English, a Transformer based bidirectional model was produced for French, Spanish, German, Italian, Russian, and Chinese. Based on the BLEU score, the experimental results showed significant high performance in all language pairs compared to the commercialization system.

• Journal of the Korean Institute of Navigation
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• v.14 no.4
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• pp.53-70
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• 1990

Much efforts have been made to improve the training education system for last decades. however, it still leaves much room form improving the system. The reason for this is that the have been many changes in given educational conditions, national and international, and that there existed the lack of training facilities on shore and the limits of capacity on the training ship. The existing program adopts a straight-through system of which the course has to be completed at same time, and also forces students to study the course, disregarding their aptitude for sea life. Consequently, the program resulted in frustrating the learning desire of some students and, as a consequence, in deteriorating the quality of the entire training education. This paper aims to develop an efficient training program including curriculla by the literature survey and the teaching and sea experiences on the training ship "HANBADA" and merchant ships, where the authors have been for many years. Compared with the existing one, the new training model suggested in this paper has some advantages as follows : First, the new model adopts multi-state system which consists of various short-term training courses according to each purpose. This system will be helpful for student to find their aptitude for sea life earlier and to understand classes of major subjection shore. Second, the model includes new curriculla which consist of core subjects (for example, navigation, marine operation, marine transportation, watch keeping and nautical English for deck cadets and internal and external combustion engine, auxiliary machinery, electric and electronics and engine maintenance for engine cadets), by incorporating existing 20 subjects in 5 subjects. These curriculla may contribute to embodying the characteristics of training education where the above mentioned subjects must be linked with each other. In order to implement this new training model efficiently and effectively, the following prerequisties must be prepared : $\circled1$ The contents of each subject included in the new model should be systematically developed. $\circled2$ The educational schedule should be adjusted according to the new model.new model.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.526-537
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• 2019

Purpose: The aim of this study was to evaluate the validity and reliability of the Korean version of the Clinical Teaching Behavior Inventory (CTBI). Methods: The English CTBI-23 was translated into Korean with forward and backward translation. Survey data were collected from 280 nurses' preceptors at five acute-care hospitals in Korea. Content validity, construct validity, and criterion-related validity were evaluated. Cronbach's ${\alpha}$ was used to assess reliability. SPSS 24.0 and AMOS 22.0 software was used for data analysis. Results: The CTBI Korean version consists of 22 items in six domains, including being committed to teaching, building a learning atmosphere, using appropriate teaching strategies, guiding inter-professional communication, providing feedback and evaluation, and showing concern and support. One of the items in the CTBI was excluded with a standardized factor loading of less than .05. The confirmatory factor analysis supported good fit and reliable scores for the Korean version of the CTBI model. A six-factor structure was validated ($x^2=366.30$, p<.001, CMIN/df=2.0, RMSEA=.06, RMR=.03, SRMR=.05, GFI=.90, IFI=.94, TLI=.92, CFI=.94). The criterion validity of the core competency evaluation tool for preceptors was .77 (p<.001). The Cronbach's ${\alpha}$ for the overall scale was .93, and the six subscales ranged from .72 to .85. Conclusion: The Korean version CTBI-22 is a valid and reliable instrument for identifying the clinical teaching behaviors of preceptors in Korea. The CTBI-22 also could be used as a guide for the effective teaching behavior of preceptors, which can help new nurses adapt to the practicalities of nursing.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.247-271
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• 2019

Uncertainty in scientific knowledge means an uncertain state where propositions are neither true or false at present. The existing studies have analyzed the propositions written in the academic literature, and have conducted the performance evaluation based on the rule based and machine learning based approaches by using the corpus. Although they recognized that the importance of word construction, there are insufficient attempts to expand the word by analyzing the meaning of uncertainty words. On the other hand, studies for analyzing the structure of networks by using bibliometrics and text mining techniques are widely used as methods for understanding intellectual structure and relationship in various disciplines. Therefore, in this study, semantic relations were analyzed by applying Word2Vec to existing uncertainty words. In addition, WordNet, which is an English vocabulary database and thesaurus, was applied to perform a network analysis based on hypernyms, hyponyms, and synonyms relations linked to uncertainty words. The semantic and lexical relationships of uncertainty words were structurally identified. As a result, we identified the possibility of automatically expanding uncertainty words.