• 한국IT서비스학회:학술대회논문집
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• 2002.11a
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• pp.451-456
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• 2002

SIP는 새로운 서비스로의 확장에 유연하다는 장점을 가지고 있다. 본 논문에서는 SIP 프로토콜을 게임서버에 접목하여, 이를 통해 얻을 수 있는 부가서비스를 제안한다. 게임서버의 사용자 인증부분을 SIP Registrar 서버로 확장하여 해결하고, SIP Personal Mobility Service를 이용하여 게임에 접속되지 않은 사용자에게 게임 정보에 대한 피드백(feedback)이 올 수 있도록 설계하였다. 즉, 사용자가 이동 가능한 위치를 SIP Registrar 서버에 등록하면, 게임서버는 게임의 진행정보를 오프라인 상태의 사용자에게 전달한다. 이 부가 서비스는 단순히 게임엔진 뿐만 아니라 증권, 뉴스속보 서비스 등 실시간 정보를 요구하는 서비스에 접목하기에 충분한 장점을 가지고 있다. 본 논문에서는 SIP 프로토콜을 게임서버에 적용하기 위한 방안으로 인터넷상의 SIP 사용자에게 실시간 온라인 게임 정보 전송 서비스 아키텍처를 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.153-161
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• 2009

In this paper, we designed and implemented realtime DID(digital information display) system based on web server that displayed multimedia contents. The contents are weather, news information on the internet web sites and public relations or advertisements data on local systems. The DID system has client/server architecture that the server send to client that schedule informations and multimedia contents received form web server and the client displayed the contents though scheduled information. Therefore the systems overcome network fault for the mean time. Also, the system has realtime services of web page filtering function that extract the partial information of specific web pages.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.448-451
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• 2022

주식과 암호화폐 거래는 매매방식에 있어서 유사한 점이 있지만 기업의 사업분야, 자본금, 순이익 등의 경영현황과 미래가치에 영향을 많이 받는 주식과는 다르게 암호화폐는 실물 실체가 없으며 탈중앙화, 전산화된 데이터를 기반으로 하며 심리적인 요소가 크게 작용하여 단기적인 변동이 클 수 있다. 본 연구에서는 이러한 암호화폐 거래의 특성을 활용하여 특정 암호화폐에 관련된 뉴스기사들을 수집하고 그 암호화폐의 가격 변화 데이터와 연관되어 가격예측 딥러닝 모델을 생성하고 해당 암호화폐에 대한 신규 뉴스기사가 발생되었을 때 이를 이용하여 매수, 매도, 관망 등과 같은 매매 정보를 예측 적용할 수 있게 하였다. 첫째, 뉴스 기사에서 언급한 암호화폐를 매수, 매도, 관망 중 어느 편이 좋을 것인지 추천하는 알고리즘을 구현하였고, 둘째, 매수 이후 매매 차익을 위한 매도 시점이나 매도 이후 저가매수에 유리한 시점을 제안하는 알고리즘을 구현하였다. 또한, 실시간 뉴스기사 수집 및 예측한 매매 판단에 따라 매매 자동화 시스템을 구현하여 수익률을 직접 확인함으로써 그 유효성을 검증하였다.

• Digital Contents
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• no.4 s.131
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• pp.100-104
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• 2004

처음 웨더뉴스 사이트에 대한 분석 요청을 받을 때 기자로부터의 요구사항은 사이트의 다른 부분, 이를테면 프리젠테이션 영역이나 수집방법, 비즈니스 로직보다는 데이터베이스 그 자체에 초점을 맞춰달라는 것이었다. 실상‘데이터베이스’라는 것은‘데이터’ 와는 조금 다른 성향을 가지고 있다. ‘데이터’는 세상의 모든 사물, 모든 사물이 운동하는 형상, 인간의 감각기관이나 그 외의 다양한 방법으로 인지할 수 있는 모든 수치화될 수 있거나 그렇지 못한 것까지를 포함하는 포괄적인 개념이다. 그러나‘데이터베이스’는 이러한 추상적인 개념과는 근본적으로 달리 현상에 있는 수많은 데이터중 필요한 데이터를 수집하는 것이 기본이며, 이를 저장하고 필요에 의해 원하는 방법대로 꺼내어 볼 수 있도록 하는 것까지를 모두 갖춰 하나의 시스템이 되어야만 비로소 하나의 데이터베이스가 되는 근본 요소가 갖춰졌다고 할 수 있는 것이다. 이번 글에서는 웨더뉴스사가 날씨 정보를 실시간으로 또한 안정적으로 운영하기 위한 노력과 이를 비즈니스화 한 방안, 그리고 다양한 디스플레이 방법을 살펴보도록 하겠다.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.726-734
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• 2019

This study investigates the number of news and correlated keywords concerning to Korean Wireless Emergency Alert(KWEA). The news was collected using BIGKinds, a news big data system provided by the Korea Press Foundation. When analyzing the annual published news articles, we investigated the frequency of the news grouped by disaster types, and the frequency of the news distinguishing between the earthquake and non-earthquake disasters, and finally the frequency of correlated keywords concerning to the disasters. We found that the KWEA news totaled 182 in 2016 due to the unprecedented powerful KyongJu earthquake, an increase of 20 times over the previous year. Ever since 2016, the news about the KWEA continued to hit high figures consistently. After the peak in KyongJu earthquake in 2016, the proportion of non-earthquakes had also increased in 2017 and 2018. Next, the keyword correlation analysis showed that the KWEA news gave major coverage to the following entities: The Ministry of the Interior and Safety which operates the KWEA, Korea Meteorological Administration, and the general public.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• The KIPS Transactions:PartD
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• v.14D no.4 s.114
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• pp.427-434
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• 2007

Although wireless devices are the most suitable device for ubiquitous environment, they have restrictive capacities when using internet services than desktop environments. Therefore this research proposes a wireless internet service method that uses contents-based personalization. The existing websites can easily and promptly access desired news articles and other data through RSS-linked web contents and by the personalization method. The proposed method will make using wireless internet easier while lowering contents production costs. Moreover, personalized mobile web news contents that satisfy the preferences of users can be serviced.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.351-362
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• 2017

Recently, analysis techniques to extract new meanings using big data analysis and various services using them have been developed. A disaster safety service among such services has been paid attention as the most important service. In this paper, we design and implement a flood disaster safety system using real time weather big data. The proposed system retrieves and processes vast amounts of information being collected in real time. In addition, it analyzes risk factors by aggregating the collected real time and past data and then provides users with prediction information. The proposed system also provides users with the risk prediction information by processing real time data such as user messages and news, and by analyzing disaster risk factors such a typhoon and a flood. As a result, users can prepare for potential disaster safety risks through the proposed system.

• Korean journal of communication and information
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• v.26
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• pp.157-208
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• 2004

This study explored how television news constructed an issue of the building of a nuclear waste facility on Wido, an issue which displayed a social conflict in the latter half of the year 2003. To do this, this study conducted frame analysis on KBS and MBC main news including national and local ones, broadcasted from 11 July, 2003 to 10 December, 2003. It was found that television news tended to stress violent protests against site designation and social disorder rather than the causes of a conflict and its solutions. Therefore, news reporting excluded fundamental reasons of conflict such as the governmental decision-making process of site designation, geological suitability, safety issue and nuclear energy policy, emphasizing the confrontation and clash between pro and con groups of site designation. This indicates that television news defines an issue of the building of a nuclear waste facility as the local conflict between groups, the police and demonstrators, or neighbors who approve and protest the site designation, not as the national issue of nuclear policy.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.