• Proceedings of the KOR-BRONCHOESO Conference
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• 1991.06a
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• pp.15-15
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• 1991

한국어 자음에 대한 생리적인 분류는 조음점 및 조음발법에 따라 다시 세분화할 수 있는데 그중에서 조음발법에 따라 파열음, 마찰음, 파찰음 및 비음들 여러가지로 분류할 수 있다. 그중 특히 파열음은 그 개방하는 방법에 따라 연음(lenis), 경음(glottalized) 및 기식음(aspirated)등으로 구분하는데 이러한 각음을 육안으로 확인하면 모음이 발성되기 위한 성대진동이 있기전의 자음을 위한 성대의 운동의 현상을 보면 기식음에서는 성대열림이 가장 크고 연음에서도 열림이 크지만 기식음보다는 적고 경음에서는 성대의 열림이 가장 작았다. 이러한 현상은 후두내시경에 의해 쉽게 확인할 수 있었는데 이것을 과학적으로 규명하기 위해서는 여러연구에 의해 가능하나 흔히 후두근전도 검사에 의한 성대내전근과 외전근의 역할의 차이를 비교함으로서 가능해지리라 예상되어 본 연구를 시행하였다. 사용된 문형 또는 단어는 한가지를 제외하고는 모두 의미있는 단어를 사용하였으며 EMG recording을 위해 사용된 근육은 후두내전근인 Vocalis muscle과 후두외전근인 Posterior cricoarytenoid muscle이 사용되었고 전기신호는 computer data processing system에 의해 분석되어졌다. 결과는 내시경에 의한 성대열림의 거리측정 결과를 분석함과 동시에 후두내근에 대한 근전도검사에 의한 분석을 토대로 하였으며 이를 간단히 설명하면 이제까지 많은 사람들은 한국어 자음에 대한 각각의 특징적인 현상들을 주로 성대내전근의 역할에 의해 규명하였으나 본 결과로는 성대내전근의 역할도 중요하지만 성대외전근의 역할 또한 상호 연관성을 가지면서 매우 중요한 역할을 한다는 점이다.for the Isotropic plates can be used. Use of some coefficients can produce "exact" value for laminates with such configuration.trap with 2.88[eV] deep of injected space charge from the chathode in the crystaline regions. The origin of ${\alpha}$$_2$ peak was regarded as the detrapping process of ions trapped with 0.9[eV] deep originated from impurity-ion remained in the specimen during production process of the material, in the crystalline regions. The origin of ${\beta}$ peak was concluded to be due to the depolarization process of "C=0"dipole with the activation energy of 0.75[eV] in the amorphous regions. The origin of ${\gamma}$ peak was responsible to the process combined with the depolarization of "CH$_3$", chain segment, with the activation energy of carriers from the shallo

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.423-428
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• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.185-199
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• 2018

The purpose of this study is to derive the core technologies of VR using patent analysis and to explore the direction of social and public interest in VR using news analysis. In Study 1, we derived keywords using the frequency of words in patent texts, and we compared by company, year, and technical classification. Netminer, a network analysis program, was used to analyze the IPC codes of patents. In Study 2, we analyzed news articles using T-LAB program. TF-IDF was used as a keyword selection method and chi-square and association index algorithms were used to extract the words most relevant to VR. Through this study, we confirmed that VR is a fusion technology including optics, head mounted display (HMD), data analysis, electric and electronic technology, and found that optical technology is the central technology among the technologies currently being developed. In addition, through news articles, we found that the society and the public are interested in the formation and growth of VR suppliers and markets, and VR should be developed on the basis of user experience.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.1
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• pp.67-76
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• 2021

Objectives : The aim of this study was to compare activities of daily living (ADLs) according to degenerative changes in brain [i.e., medial temporal lobe atrophy (MTA), white matter hyperintensities] and to examine the association between neurocognitive functions and ADLs in Korean patients with dementia due to Alzheimer's disease (AD) and mild cognitive impairment (MCI). Methods : Participants were 111 elderly subjects diagnosed with AD or MCI in this cross-sectional study. MTA in brain MRI was rated with standardized visual rating scales (Scheltens scale) and the subjects were divided into two groups according to Scheltens scale. ADLs was evaluated with the Korean version of Blessed Dementia Scale-Activity of daily living (BDS-ADL). Neurocognitive function was evaluated with the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease assessment packet (CERAD-K). Independent t-test was performed to compare ADLs with the degree of MTA. Pearson correlation and hierarchical multiple regression analyses were performed to analyze the relationship between ADLs and neurocognitive functions. Results : The group with high severity of the MTA showed significantly higher BDS-ADL scores (p<0.05). The BDS-ADL score showed the strongest correlation with the word list recognition test among sub-items of the CERAD-K test (r=-0.568). Findings from the hierarchical multiple regression analysis revealed that the scores of MMSE-K and word list recognition test were factors that predict ADLs (F=44.611, p<0.001). Conclusions : ADLs of AD and MCI patients had significant association with MTA. Our study, which identifies factors correlated with ADLs can provide useful information in clinical settings. Further evaluation is needed to confirm the association between certain brain structures and ADLs.

• Investigative Magnetic Resonance Imaging
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• v.8 no.1
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• pp.9-16
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• 2004

Purpose : The purpose of this study was to assess supplementary motor area (SMA) activation during motor, sensory, word generation, listening comprehension, and working memory tasks using functional magnetic resonance imaging (fMRI). Materials and Methods : Sixteen healthy right-handed subjects (9M, 7F) were imaged on a Siemens 1.5T scanner. Whole brain functional maps were acquired using BOLD EPI sequences in the axial plane. Each paradigm consisted of five epochs of activation vs. the control condition. The activation tasks consisted of left finger complex movement, hot sensory stimulation of the left hand, word generation, listening comprehension, and working memory. The reference function was a boxcar waveform. Activation maps were thresholded at an uncorrected p=0.0001. The thresholded activation maps were placed into MNI space and the anatomic localization of activation within the SMA was compared across tasks. Results : SMA activation was observed in 16 volunteers for the motor task, 11 for the sensory task, 15 for the word generation task, 5 for the listening comprehension task, and 15 for the working memory task. The rostral aspects of the SMA showed activity during the word generation and working memory tasks, and the caudal aspects of the SMA showed activity during the motor and sensory tasks. Right (contralateral) SMA activation was observed during the motor and sensory tasks, and left SMA activation during the word generation and memory tasks. Conclusion : Our results suggest that SMA is involved in a variety of functional tasks including motor, sensory, word generation, and working memory. The results obtained also support the notion that functionally specific subregions exist within the region classically defined as the SMA.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2907-2915
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• 2018

Journal of the Korean Data Analysis Society founded in 1998 has played the role of a major application journal. In this study, we checked the objective of this journal by checking the abstracts for 10 years. Abstract data was crawled from the online journal site (kdas.jems.or.kr) and analyzed by topic model. As a result, we found 18 topics from 2680 abstracts that had several contents, for example, nursing, marketing, economics, regression, factor analysis, data mining and statistical inferences. Topic1 (regression) is most frequent with 460 documents and we found the usefulness of regression in the applied science area. We confirmed the significant 10 association rules using by Fisher's exact test. Also, for exploring the trend of topics, we conducted the topic analysis for two periods which are 2006-2011 period and 2012-2016 period. We found that the control study was more frequent than survey study over time and regression and factor analysis were frequent regardless of time.

• Korean Journal of Psychosomatic Medicine
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• v.8 no.1
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• pp.58-64
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• 2000

Objectives : This study aimed to examine a correlation between the somatic complaints, emotion, vocabulary and alexithymia as a component of personality in normal persons. Methods : 204 subjects were collected by age-based systematic sampling from the 662 persons without confirmed medical illness. We used the Korean version of 20-item Toronto Alexithymia Scale(TAS-20K) to measure alexithymia. The somatic complaints were checked by the list of somatic symptoms on the diagnostic criteria of somatization disorder and major depressive episode in DSM-IV. The vocabulary was evaluated by the total number of associating-words from the spontaneous association of word and the secondary association to given words. The anxiety and depression were evaluated using 5-point self-report scale. Results : 1) The degree of alexithymia was significantly correlated with the somatic complaints, anxiety, depression. 2) The somatic complaints were significantly correlated with the anxiety and depression. 3) The number of associating-words showed negative correlation with the age. 4) The degree of alexithymia was not correlated with the number of associating-words. Conclusion : The more degree of alexithymia increased, the more somatic complaints appeared. There was a significant correlation between the degree of alexithymia, anxiety and depression. But the degree of alexithymia was not correlated with the amount of vocabulary.

• Koreanishche Zeitschrift fur Deutsche Sprachwissenschaft
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• v.3
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• pp.277-301
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• 2001

우리가 간단히 사용하는 언어는 실제적으로 아주 복잡한 진행과정을 가지고 있다. 사전상의 각 어휘는 대화상황에서 상호 작용하는 초기단계의 역할을 하며, 표현은 과거나 현재에 행해지는 대화상황 및 대화참여자의 발화 과정에서 생기는 일종의 일체감을 표시한다. 의사 소통을 한다는 것은 단어나 문장에 대한 다양한 의미와 각각의 개념에서 지시되는 표현을 수단으로 발생하는 대화상의 연관성을 의미한다. 이러한 모든 것은 의사소통에 있어 의미의 다양성과 관련을 맺고있다. 우리는 표현을 통하여 매우 복잡하고 다양한 양상들을 볼 수 있다. 대화내용에 따라 똑같은 표현들이 서로 다르게 이해될 수 있기 때문이다. 언어는 단지 사람이 행하는 언어처리의 일부만을 보여줄 뿐이다 언어를 처리하는데 있어서 문제가 되는 것은 매우 복잡하고 구성적인 진행과정이다. 청자는 의사소통이 진행되는 과정에서 활자와 함께 주어진 정보를 처리함으로써 상황을 내적 형상화하게 된다. 따라서 청자는 표현의 의미를 이해하려고 노력하며, 다양한 방법을 동원한 지식을 사용한다. 의사소통에 있어서 통사적$\cdot$의미적인 지식, 문맥에 맞는 대화지식 혹은 일반 지식을 대화상황에 맞게 적용하는 것이 그 예라 할 수 있다. 지시적 언어의 표현은 사전적으로 고정된 단어의 의미를 규정하거나 또는 이와 같은 단어의 의미에 정확하고 적절한 지시사를 규정하는 근거가 된다. 인칭$\cdot$장소$\cdot$시간을 지시하는 언어 Personal-, Lokal-, Temporaldeixis는 언어 시스템을 형성하게 되는데, 활자와 청자는 이러한 표현을 인칭$\cdot$장소$\cdot$시간으로 형상화하면서 의사소통을 한다. 따라서 자연어의 처리과정에 나타나는 다양한 표현들에 대한 심리학 및 언어학의 강력한 연구가 요구된다.에 기대어 텍스트, 문장, 어휘영역 등이 투입되어 적용되었으며, 이에 상응되게 구체적인 몇몇 방안들이 제시되었다. 학습자들이 텍스트를 읽고 중심내용을 찾아내며, 단락을 구획하고 또한 체계를 파악하는데 있어서 어휘연습은 외국어 교수법 측면에서도 매우 관여적이며 시의적절한 과제라 생각된다. Sd 2) PL - Sn - pS: (1) PL[VPL - Sa] - Sn - pS (2) PL[VPL - pS] - Sn - pS (3) PL(VPL - Sa - pS) - Sn - pS 3) PL[VPL - pS) - Sn -Sa $\cdot$ 3가 동사 관용구: (1) PL[VPL - pS] - Sn - Sd - Sa (2) PL[VPL - pS] - Sn - Sa - pS (3) PL[VPL - Sa] - Sn - Sd - pS 이러한 분류가 보여주듯이, 독일어에는 1가, 2가, 3가의 관용구가 있으며, 구조 외적으로 동일한 통사적 결합가를 갖는다 하더라도 구조 내적 성분구조가 다르다는 것을 알 수 있다. 우리는 이 글이 외국어로서의 독일어를 배우는 이들에게 독일어의 관용구를 보다 올바르게 이해할 수 있는 방법론적인 토대를 제공함은 물론, (관용어) 사전에서 외국인 학습자를 고려하여 관용구를 알기 쉽게 기술하는 데 도움을 줄 수 있기를 바란다.되기 시작하면서 남황해 분지는 구조역전의 현상이 일어났으며, 동시에 발해 분지는 인리형 분지로 발달하게 되었다. 따라서, 올리고세 동안 발해 분지에서는 퇴적작용이, 남황해 분지에서는 심한 구조역전에 의한 분지변형이 동시에 일어났다 올리고세 이후 현재까지, 남황해 분지와 발해 분지들은 간헐적인 해침과 함께 광역적 침강을 유지하면서 안정된 대륙 및 대륙붕 지역으로 전이되었다.

• Korean Association of Arts Management
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• no.50
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• pp.5-31
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• 2019

The main purpose of this study was to use Deep Learning based Topic Modeling and Semantic Network Analysis to examine research trend of arts management-related papers in korea. For this purpose, research subjects such as 'The Journal of Cultural Policy', 'The Journal of Cultural Economics', 'The Journal of Culture Industry', 'The Journal of Arts Management', and 'The Journal of Human Content', which are the registered journal of the National Research Foundation of Korea directly or indirectly related to arts management field. From 1988 to 2017, a total of 2,110 domestic journals' signature, abstract, and keyword were analyzed. We tried Big Data analysis such as Topic Modeling and Semantic Network Analysis to examine changes in trends in arts management. The analysis program used open software R and standard statistical software SPSS. Based on the results of the analysis, the implications and limitations of the study and suggestions for future research were discussed. And the potential for development of convergent research such as Arts & Artificial Intelligence and Arts & Big Data.