• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.489-498
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• 2012

This paper consists of two parts: In the first part, we describe our work to build hierarchical knowledge base of digital library patron's research interests and learning topics in various scholarly areas through analyzing well classified Electronic Theses and Dissertations (ETDs) of NDLTD Union catalog. Journal articles from ACM Transactions and conference web sites of computing areas also are added in the analysis to specialize computing fields. This hierarchical knowledge base would be a useful tool for many social computing and information service applications, such as personalization, recommender system, text mining, technology opportunity mining, information visualization, and so on. In the second part, we compare four grouping algorithms to select best one for our data mining researches by testing each one with the hierarchical knowledge base we described in the first part. From these two studies, we intent to show traditional verification methods for social community miming researches, based on interviewing and answering questionnaires, which are expensive, slow, and privacy threatening, can be replaced with systematic, consistent, fast, and privacy protecting methods by using our suggested hierarchical knowledge base.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.31-47
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• 2008

As OWL(Web Ontology Language) has been selected as a standard ontology description language by W3C, many ontologies have been building and developing in OWL. The lena developed by HP as an Application Programming Interface(API) provides various APIs to develop inference engines as well as storages, and it is widely used for system development. However, the storage model of Jena2 stores most owl documents not acceptable into a single table and it shows low processing performance for a large ontology data set. Most of all, Jena2 storage model does not consider hierarchical structures of classes and properties. In addition, it shows low query processing performance using the hierarchical structure because of many join operations. To solve these issues, this paper proposes an OWL ontology relational database model. The proposed model semantically classifies and stores information such as classes, properties, and instances. It improves the query processing performance by managing hierarchical information in a separate table. This paper also describes the implementation and evaluation results. This paper also shows the experiment and evaluation result and the comparative analysis on both results. The experiment and evaluation show our proposal provides a prominent performance as against Jena2.

• Journal of the Korean Society for information Management
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• v.32 no.4
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• pp.77-106
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• 2015

By document co-citation analysis with Korean Citation Index (KCI) data, this study accurately identified the research fronts and hot topics in Korean library and information science (LIS) from 2004 to 2013. 159 core papers in LIS domain and their citations are scraped manually from Korean Citation Index web site. In the cluster analysis and network analysis, 159 core papers were grouped into 27 clusters with multiple papers and 8 singlton clusters. Among the 27 clusters which have multple papers, 'LIS education' cluster was the largest with 16 core papers, and 'citation analysis & intellectual structure analysis' cluster had the strongest citation impact according to the ehs-index. Closer observation of the citations to the core papers in each research front showed that 67.5% of the citations were made by LIS research papers and 32.5% of the citations were made by non-LIS research papers. Considering the share of citations and the citation impact growth index, 'local documentation', 'citation analysis & intellectual structure analysis', and 'research trends analysis' were identified as the most emerging research front in Korean library and information science. The analytical methods used in this study have great potential in discovering the characteristics of research fronts in Korean interdisciplinary research domains.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.75-82
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• 2023

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.

• Journal of the Korean Geographical Society
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• v.35 no.1
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• pp.3-15
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• 2000

산업화와 더불어 심각해지고 있는 수질오염 문제를 해결하기 위해서는 여러 가지 수질관리 방안이 요구된다. 수질오염이 과거에는 국지적이었으나 점차 광범한 지역으로 확장됨에 다라 지속적인 수질 모니터링에 어려움이 따른다. 본 연구에서는 위성영상을 사용한 원격탐사 기법으로 수역의 수질환경 인자를 추출하고자 한다. 사용된 영상은 Landasat TM이며, 연구지역은 한강하류 지역이다. 수질분석 인자는 클로로필-a, 부유물질, 투명도 등을 선정하였으며, 수면분광반사율의 특징 및 수질인자별 처리기법을 개발하는데 목적을 두었다. 분광특성 분석결과를 요약하면, 첫 번째 스펙트럼 반사율 분석결과 클로로필-a의 농도는 0.4~0.5$\mu\textrm{m}$ 파장대역에서 낮은 반사치 경향을 보이며, 녹색파장대인 0.57$\mu\textrm{m}$ 부근에서 반사율이 높아진다. 두 번째 부유물질의 반사도는 농도가 증가할수록 0.8$\mu\textrm{m}$ 부근에서 상대적으로 낮은 반사율이 나타난다. 마지막으로 투명도가 낮은 수면은 0.55$\mu\textrm{m}$에서 높은 반사율 경향을 보인다. Landsat TM영상을 이용하여 주성분분석 및 비연산처리를 실시하여 수질분석을 시도한 결과를 보면 클로로필-a와 투명도는 제1주성분 영상 및 제2주성분 영상에서 현장 실측자료와 유사한 결과를 얻을 수 있었으며, 부유물질은 밴드 2와 밴드 4의 비연산처리를 통하여 분포도를 작성할 수 있었다. 이상의 결과들은 계절적 및 시간적 변화에 따라 파장대역이 달라질 수 있다. 그러므로 위성자료를 이용하여 보다 정확한 수질환경 인자를 추출하기 위해서는 현장실측 및 수역의 분광반사 특성을 지속적으로 조사하여야 한다.때문으로 경주 산사태와 포함-구릉포간 국도면의 산사태가 이 종류의 산사태에 속한다.열 인식의 신뢰도를 향상시킬수 있는 방법을 제안하였다.작성하여 최신 의료영상 처리 기법을 쉽게 임상에 적용하고 실험할 수 있는 장점이 있다. 지대에서 가능하였고, 파종기는 중생종보다 이르게 나타났다. 등숙만한출수기 기준의 안전작기는 조생종과 중생종은 태백고냉지대와 태백준고냉지대, 소백산간지대 일부지역을 제외한 다른 지역에서 설정되었고, 중만생종은 태백고냉지대, 태백준고냉지대, 동해안북부지대, 소백산간지대, 노령소백산간지대의 일부 지역은 벼 담수직파가 불가능하게 판단되었다. information on the regular basis of time and provide it when the users query over the Web-database gateway. The other approach is a shopping agent mechanism, which stores information on "how to shop" and the shopping agent collects the information of product items just after users query about the product and provide the information in real time or notify them by alerting service. Thirty nine shopping information services are compared and classified in this paper and they are extracted from "Naver" and "Yahoo! Korea". The final result shows that most services are just a

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.30-35
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• 2008

The purpose of this study is to analyze and define user's emotional satisfaction factors to the PC OS GUI image. The study is to investigate the relationship between PC OS GUI Image and Sensitive Vocabula교 based on user's emotional preference. 47 user preferred sensitive words are collected by the initial survey. Through the similarity test, 47 words are narrowed down to 20 comprehend words. The semantic differential methods is used in the final survey with 5 step questionnaire. From this process, user preferred the GUI design that is vocabularized as Clear, Easy, Safety, Stability. Additionally, the result shows that the image of Clear is related to Safety and the image of Easy is related to Stability. The result of the study could be used in design PC OS GUI as base data.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.319-327
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• 2016

The objective of this study is to identify the secular trend in age at menarche and to investigate the factors influence the early menarche(<12 years old) among Korean middle and high school girls. We analyzed data from the Korean Youth Risk Behavior Web-based Survey(KYRBWS) 2006-2014. This study was a descriptive study of 216,917 Korean middle and high school girls born between 1988 and 2002. Linear trends test performed to assess the trend age at menarche and percentage of early menarche. Multiple logistic regression analysis was to assess the risk factors influence the early menarche. Mean age at menarche decreased from $12.61{\pm}1.32$ years for middle and high school girls born 1988 to $11.88{\pm}0.75$ years for those born 2002(p for trends<.001). Percentage of early menarche increased from 19.7% to 25.2% between 1988 and 2002(p for trends<.001). Living in city, higher stress level, short sleep duration, and higher body mass index were associated with an early menarche among middle and high school girls(all p<.001). We found that age at menarche is still falling in the Korean adolescents, and it need intervention strategies to control the early menarche.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.127-136
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• 2012

Many research of hand gesture recognition based on vision system have been conducted which enable user operate various electronic devices more easily. 3D position calculation and meaningful gesture classification from similar gestures should be executed to recognize hand gesture accurately. A simple and cost effective method of 3D position calculation and gesture spotting (a task to recognize meaningful gesture from other similar meaningless gestures) is described in this paper. 3D position is achieved by calculation of two cameras relative position through pan/tilt module and a marker regardless with the placed position. Fuzzy garbage model is proposed to provide a variable reference value to decide whether the user gesture is the command gesture or not. The reference is achieved from fuzzy command gesture model and fuzzy garbage model which returns the score that shows the degree of belonging to command gesture and garbage gesture respectively. Two-stage user adaptation is proposed that off-line (batch) adaptation for inter-personal difference and on-line (incremental) adaptation for intra-difference to enhance the performance. Experiment is conducted for 5 different users. The recognition rate of command (discriminate command gesture) is more than 95% when only one command like meaningless gesture exists and more than 85% when the command is mixed with many other similar gestures.

• Knowledge Management Research
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• v.25 no.1
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• pp.173-192
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• 2024

This study aims to understand the changes in the beauty industry due to the pandemic from the consumer's perspective based on consumers' opinions about their skin online before and after the pandemic. Furthermore, this study tries to derive strategies for companies and governments to support sustainable growth and innovation in the beauty industry. To this end, posts on social media from 2017 to 2022 that contained the keyword 'skin concerns' are collected, and after data preprocessing, 96,908 posts are used for the structural topic model. To examine whether consumers' interest areas related to skin change according to the pandemic situation, the analysis period is divided into 7 periods, and the variables that distinguish each stage are used as meta-variables for the structural topic model. As a result, it is found that consumers' interests can be divided into 22 topics, which can be categorized into four main categories: beauty manufacturing, beauty services, skin concerns, and other. The results of this study are expected to be utilized in construction of product development and marketing strategies of related companies and the establishment of economic support policies by the government in response to changes in demand in the beauty industry due to the pandemic.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.