• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.245-249
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• 2006

디지털 컨버전스와 유비쿼터스 시대의 시작은 디지털 미디어 기술의 발전과 방송 통신 사업의 컨버전스를 가속화 시켰으며, 그 결과로DMB, WCDHA, Wibro, IP-TV, HSDPA 등의 새로운 형태의 차세대 제품과 서비스들이 뉴미디어 매체의 핵심으로 등장하고 있다. 국내에서 방송 통신의 컨버전스의 빠른 진행은 세계 최초로 디지털 멀티미디어 방송(DMB) 서비스 시작을 가능하게 하였다. DMB 서비스는 멀티미디어 서비스가 핵심이다. DMB 데이터 서비스인 Broadcasting Website Service(BWS)는 현재 지상파 DMB방송 사업자인 KBS, MBC, SBS, YTNDMB가 본방송 준비 막바지 단계이며, 삼성 전자와 LG전자를 비롯한 단말기 개발사들도 데이터 서비스를 위한 제품 출시에 서두르고 있는 등 DMB 산업의 활성화의 주역이 될 것으로 예상된다. DMB의 데이터 서비스는 뉴스, 날씨, 프로그램 정보 등의 단순 정보보기 수준에 그치지 않고, 리턴 채널을 이용한 양방향 서비스와, SMS, 전화걸기 등 휴대전화 단말의 고유기능과의 연계를 통한 다양한 서비스도 선보일 것이다. 더 나아가 향후 T-Commerce와 개인 광고 등 새로운 비즈니스 모델과 사업영역으로 확산시켜 나갈 수 있을 것이다. 그러나, 아직까지 DMB와 데이터 서비스는 초기단계로서, 표준 기술의 규격 작업, 이론적 논의들, 관련 사업자들의 비즈니스 준비 등에서 검토되어, 실제 사용자들을 대상으로한 연구 분석이 이루어 지지 않았다는 연구의 한계를 가지고 있다. 본격적으로 방송, 통신 컨버전스 데이터 서비스가 시작되면서, 사용자들에 초점을 맞춘 많은 연구가 이루어지길 바라며, 이러한 연구의 분석를 통해 또 다른 새로운 서비스와 비즈니스 기회의 창출을 기대해 본다.여 RD(Rate Distortion) 최적화 기반 모드 결정을 빨리 완료함으로써 고속 프레임간 모드 결정을 가능하게 한다. 제안된 방법은 프레임 간 모드 결정을 고속화함으로써 스케일러블 비디오 부호화기의 연산량과 복잡도를 최대 57%감소시킨다. 그러나 연산량 감소에 따른 비트율의 증가나 화질의 열화는 최대 1.74% 비트율 증가 및 0.08dB PSNR 감소로 무시할 정도로 작다., 반드시 이에 대한 검증이 필요함을 알 수 있었다. 현지관측에 비해 막대한 비용과 시간을 절약할 수 있는 위성영상해석방법을 이용한 방법은 해양수질파악이 가능할 것으로 판단되며, GIS를 이용하여 다양하고 복잡한 자료를 데이터베이스화함으로써 가시화하고, 이를 기초로 공간분석을 실시함으로써 환경요소별 공간분포에 대한 파악을 통해 수치모형실험을 이용한 각종 환경영향의 평가 및 예측을 위한 기초자료로 이용이 가능할 것으로 사료된다.염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에

• Journal of Convergence Society for SMB
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• v.5 no.2
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• pp.27-32
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• 2015

Recently, service-related products have gained more attention than general products on the existing social commerce sites. Based on the situation, the effect that the service quality of social commerce has on customer satisfaction was analyzed in this study. It is a study that analyzes how much the service quality affects the customer satisfaction after the purchase, targeting consumers who have made purchases of social commerce products. In the case of social commerce, it is well-known that the diversity and convenience of products have a significant effect on customer satisfaction. Social commerce is currently being dumped beyond the 900 sites and dozens of cases of news, real-time searches of popular portal sites appeared not to be bored enough to related sites to drive the popularity coming quickly dug into our everyday lives of human beings. Yet the perception of social commerce seems not properly established because of the new concept was suddenly going to go through penetration without a collective interpretation and acceptance process. Most of the companies that often mimic the syoseol commerce is large, the blame did not depart from the forms of social shopping. We believe that personal and exhibit their skills and talents, and to wonder to see the social rather than the individuals who make unilateral companies.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1273-1284
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• 2016

In this paper, we analyzed the patents on machine learning using keyword network analysis and clique analysis. To construct a keyword network, important keywords were extracted based on the TF-IDF weight and their association, and network structure analysis and clique analysis was performed. Density and clustering coefficient of the patent keyword network are low, which shows that patent keywords on machine learning are weakly connected with each other. It is because the important patents on machine learning are mainly registered in the application system of machine learning rather thant machine learning techniques. Also, our results of clique analysis showed that the keywords found by cliques in 2005 patents are the subjects such as newsmaker verification, product forecasting, virus detection, biomarkers, and workflow management, while those in 2015 patents contain the subjects such as digital imaging, payment card, calling system, mammogram system, price prediction, etc. The clique analysis can be used not only for identifying specialized subjects, but also for search keywords in patent search systems.

• Journal of Food Hygiene and Safety
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• v.32 no.2
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• pp.96-100
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• 2017

Through the increasing use of internet and smart device, consumers can search the information what they want to find. The information has been accumulated and become into a big data. Analyzing the big data regarding key words associated with foods and foodborne pathogens could be a method for predicting foodborne illness outbreaks, especially in school food services. Therefore, the objective of this study was to elucidate the correlations between key words associated with foods and food safety issues. Frequencies of the key words for foodborne pathogens and food safety issues were searched using an internet portal site from January 1, 2012 to December 31, 2014. In addition, foodborne outbreak data were collected from Ministry of Food and Drug Safety for the same period of time. There was correlation between the time having maximum key word frequencies of foods and foodborne pathogens, and the time for foodborne illness outbreak occurred. In addition, the search frequencies for foods and foodborne pathogens were generally increased right after foodborne outbreaks occurred. However, in some cases foodborne outbreaks occurred after the search frequencies for certain seasonal foods increased These results could be useful in food safety management for reducing foodborne illness and in food safety communication.

• The Journal of Bigdata
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• v.9 no.1
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• pp.75-88
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• 2024

Recently, there have been cases reported in the news of individuals experiencing symptoms of food poisoning after consuming raw beef purchased from online platforms, or reviews claiming that cherry tomatoes tasted bitter. This suggests the potential for analyzing food reviews on online platforms to detect food hazards, enabling government agencies, food manufacturers, and distributors to manage consumer food safety risks. This study proposes a classification model that uses sentiment analysis and large language models to analyze food reviews and detect negative ones, multi-labeling key food safety hazards (food poisoning, spoilage, chemical odors, foreign objects). The sentiment analysis model effectively minimized the misclassification of negative reviews with a low False Positive rate using a 'funnel' model. The multi-labeling model for food safety hazards showed high performance with both recall and accuracy over 96% when using GPT-4 Turbo compared to GPT-3.5. Government agencies, food manufacturers, and distributors can use the proposed model to monitor consumer reviews in real-time, detect potential food safety issues early, and manage risks. Such a system can protect corporate brand reputation, enhance consumer protection, and ultimately improve consumer health and safety.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of agricultural medicine and community health
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• v.40 no.3
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• pp.126-136
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• 2015

Objectives: This study was performed to investigate the effects of a health education program for the aged on knowledge about the warning signs and symptoms of acute myocardial infarction and stroke. Methods: Data from 337 elderly people (159 participated and 178 non-participated) at senior centers in Jeju-si were collected by 1 to 1 interview from January to March 2012, one year after the education program provided. Two stages of study were performed: Cross-sectional, case-control study on the level of knowledge about the warning signs and symptoms; and multivariate logistic regression to fine out predictors of optimal awareness. Results: No significant discrepancy of knowledge level between case and control group was found. The knowledge level as high as a surge was shown in both groups one year later. A surge of knowledge had been shown after the education provided in one month. The factors affecting the optimal level of knowledge were education (Odds ratio 3.01; Confidence Interval 1.72-5.26; P-value <0.001) and 7 days of watching TV news per week (2.97; 1.68-5.23; P<0.001). However, participation in the health education was not significant (1.60; 0.98-2.61; P=0.059). Conclusions: The effects of a targeted program in high-risk groups for cardio-cerebrovascular disease are only guaranteed in the enhancement by a population-based mass-media education campaign.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.