• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.401-404
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• 2004

본 연구에서는 다양한 뉴스그룹들 중에서 사용자의 취향과 유사한 뉴스그룹들을 코호넨 신경망을 이용하여 추천해주는 방법을 제시한다. 신경망을 학습시키기 위한 뉴스 문서의 키워드들을 선택하기 위해 여러 문서들로부터 후보 용어들을 추출하고 퍼지 추론을 적용하여 대표 용어들을 선택한다. 하지만 신경망의 학습패턴을 관찰해 보면, 맡은 부분이 비어있는 희소성 문제를 발견할 수 있다. 이에 본 연구에서는 통계적인 결정계수를 도입하여 불필요한 차원을 제거한 후 신경망을 학습시키는 새로운 방법을 제안한다. 제안된 방법은 모든 차원을 활용할 때 보다 클러스터내 거리와 클러스터간 거리의 척도를 이용한 클러스터 중첩도 면에서 우수한 분류 성능을 보여줌을 확인하였다.

• Speech Sciences
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• v.3
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• pp.119-131
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• 1998

We have developed a keyword spotting system for automatic speech editing. This system recognizes the only keyword 'MBC news' and then sends the time information to the host system. We adopted a vocabulary dependent model based on continuous hidden Markov model, and the Viterbi search was used for recognizing the keyword. In recognizing the keyword, the system uses a parallel network where HMM models are connected independently and back-tracking information for reducing false alarms and missing. We especially focused on implementing a stable and practical real-time system.

• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.13-26
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• 2019

The rural areas in South Korea have changed rapidly in the process of national land development. Rural landscapes have become discoloured, and their attractiveness has decreased as cities have expanded. But the attractiveness or multifunctional values of rural areas has become more important in contemporary society around the world. According to this social demand, the efforts of conserving the rural landscape are of high priority and the recovery of ruralism in the area is required. This study has tried to understand how the public image of ruralism in South Korea has been influenced by the news media. The study retrieved news articles using the web searching portal site from the six keywords, commonly used to refer to ruralism, including 'rural landscape', 'rural community', 'rural tourism', 'rural life', 'rural amenity', and 'rural environment'. News data from the six keywords were also collected respectively from within the year-period of 2004-05, 2007-08, 2012-13, and 2016-17. In the text mining analysis, the nouns with high Degree Centrality were figured out, and the changes by year-period were identified. Then, LDA topic analysis was performed for text datasets of six keywords. As a result, the study found that the news articles gave an informed focus on only a handful of issues such as 'poor rural living condition', 'regional or village improvement projects', 'rural tourism promotion projects', and 'other government support projects'. On the other hand, nouns related to virtues and values in the rural landscape were less shown in news articles. These results have become more apparent in recent years. In the topic analysis, 35 topics were identified. 'village development projects', 'rural tourism', and 'urban-rural exchange projects' were appeared repeatedly in several keywords. Among the topics, there are also topics closely related to ruralism such as 'rural landscape conservation', 'eco-friendly rural areas', 'local amenity resources', 'public interest values of agriculture', and 'rural life and communities'. The study presented an image map showing ruralism in South Korea using a network map between all topics and keywords. At the end of the study, implications for Korean rural area policy and research directions were discussed.

• Journal of Platform Technology
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• v.11 no.3
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• pp.83-97
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• 2023

This research aims to identify startup trends by analyzing a large number of news articles through semantic network analysis. Using the BIGKinds article analysis service provided by the Korea Press Foundation, 330,628 news articles from 19 newspapers from January 2013 to December 2022 were comprehensively analyzed. The study focused on exploring the changes in key issues over the past decade, considering the impact of the social environment and global economic trends on entrepreneurship. We compared the number of news articles and changes in issues before and after the COVID-19 pandemic, and visualized entrepreneurship trends through frequency analysis, relationship analysis, and correlation analysis. The results of the study showed that the top keywords for entrepreneurship-related words are startup activation and commercialization, and the correlation between COVID-19 and entrepreneurship keywords is almost negligible in a linear sense, but the number of news articles decreased during the pandemic, which has an impact. In particular, the most frequently mentioned keywords are Ministry of SMEs and Startups, place is the United States, and person is limited. The agency was the SBA, and the entrepreneurship sector is more affected by social issues than any other sector, with the important characteristics of increased frequency of prompt access. This study supplies essential basic data for understanding and exploring issues and events related to entrepreneurship and suggests future research topics in the field.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.05a
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• pp.508-525
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• 2015

The purpose of this study is to derive the demands to develop marine science technology to reduce damage of disasters caused by boating accidents. This study analyzed the press release to identify the factors of damages that can result from boating accidents and derive the demands for technology development to approach from the perspective of marine science technology to avoid the elements of damage. For this purpose, this study analyzed the contents of about 77,000 articles posted for a month after the tragedy of the Sewol (April 16 - May 15) to derive the keywords and used SNA for the network analysis of each keyword. The findings of the analysis showed that there were five networks and each network consisted of different aspects of technology development to prepare for the marine disasters. Based on these findings, this study derived the demands for technology development from the perspective of marine science technology required to prepare for the possible marine disasters caused by vessels in the future.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1157-1163
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• 2005

In this paper, an efficient and fast method to segment a video in the MPEG(motion picture expert group) video stream is proposed. For the real time processing of large amount of broadcasting data, we use DC images of I-frames in an MPEG compressed video with minimal decoding. Using the modified histogram comparison which counts on not only luminance but also chrominance information, the scene change detection was performed in the fast and accurate way Also, to discriminate anchorperson frame from non-anchor frame, a neural network method was introduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.528-530
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• 2005

Multicast is an efficient technology for internet video conference, internet live broadcast, data delivery and news services, and especially RMCP, which is a kind of overlay multicast, operates on network independently. It is considered to be deployed current internet network immediately. This paper describes various services using RMCP standard technology which is developed in ITU-T.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.9-15
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• 2018

Policy polls, which investigate the degree of support that the policy has for policy implementation, play an important role in making decisions. As the number of Internet users increases, the public is actively commenting on their policy news stories. Current policy polls tend to rely heavily on phone and offline surveys. Collecting and analyzing policy articles is useful in policy surveys. In this study, we propose a method of analyzing comments using deep learning technology showing outstanding performance in various fields. In particular, we designed various models based on the recurrent neural network (RNN) which is suitable for sequential data and compared the performance with the support vector machine (SVM), which is a traditional machine learning model. For all test sets, the SVM model show an accuracy of 0.73 and the RNN model have an accuracy of 0.83.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.165-175
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• 2023

Boryeong Mud Festival has become a representative local festival that both domestic and foreign tourists can enjoy together. In addition, it is one of the usual hands-on marine festivals in Korea that can be enjoyed with one mind at the Boryeong Mud Festival, regardless of race, age, and language. This study explored the overall perception and trends of the Boryeong Mud Festival using big data extracted online from the Boryeong Mud Festival. First, keywords such as Chungnam, hosting, summer, reporter, experience, opening ceremony, performance, operation, news, tourist, opening, event, and festival were frequently exposed online. Second, due to centrality analysis, the centrality of festival experience programs and performances, opening ceremonies, and Boryeong mayor was high. Third, due to the CONCOR analysis, five clusters of meaningful keywords related to the Boryeong Mud Festival were formed.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.