• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.54-61
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• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.11-13
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• 2019

대중들에게 정보를 빠르고 정확하게 제공하는 대표 매체인 뉴스 기사는 일 평균 1만 5천 건 이상이 보도되고 있다. 특정 주제 또는 분야에 대한 전반적인 동향을 파악하고자 대량의 텍스트 데이터를 수집하여 텍스트 마이닝(Text mining)과 머신러닝 등을 적용하는 연구들이 활발하게 수행되고 있다. 본 연구에서는 서울시의 이슈 및 문제를 파악하고자 약 5년간 뉴스 기사를 수집하여 키워드 분석 및 토픽 모델링을 적용하였다. 분석 결과 5년간의 뉴스 기사에서 빈번하게 출현하는 키워드들을 도출하였고 연도별로 도출된 키워드들을 비교분석하였다. 또한 토픽 모델링 적용 결과 뉴스 기사를 구성하는 20개의 주제를 도출하였으며 이를 기반으로 서울시의 주요 이슈들을 파악할 수 있다. 본 연구는 연도별, 분야별 세부 내용 및 시계열 분석, 다른 도시들의 이슈 및 문제를 도출하는데 활용될 것으로 기대된다.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.165-172
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• 2017

In this paper, we analyze the research / technology trends of electric vehicles from 2001 to 2014, through keyword analysis using paper data published in SCIE or SSCI Journal on electric vehicles, time series analysis using patent data by IPC, and network analysis using nodeXL. also we predicted promising technologies of electric vehicles using one of the prediction methods, weighted moving average method. As a result of this study, battery technology among the electric vehicle component technologies appeared as a promising technology.

• Journal of Korean Society of Archives and Records Management
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• v.6 no.2
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• pp.5-25
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• 2006

In this study, twelve research papers on "records management and archives research areas" that were written from the 1980s to the 2000s were analyzed according to a timeline. The results show that the main subject areas during the 1980's were records and archives. During the 1990s, with the increase of electronic records, research areas expanded to information systems and related social environments. In the 2000s, more advanced technical and interdisciplinary subjects have emerged in the digital society. These results show that research areas in records management and archives still have much potential for growth and will expand in the near future.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.315-334
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• 2013

This study performs opinion mining of newspaper articles, based on topics extracted by topic modeling. We analyze the attitudes of the news media towards a major issue of 'presidential election', assuming that newspaper partisanship is a kind of opinion. We first extract topics from a large collection of newspaper texts, and examine how the topics are distributed over the entire dataset. The structure and content of each topic are then investigated by means of network analysis. Finally we track down the chronological distribution of the topics in each of the newspapers through time serial analysis. The result reveals that both the liberal newspapers and the conservative newspapers exhibit their own tendency to report in line with their adopted ideology. This confirms that we can count on opinion mining technique based on topics in order to analyze opinion in a reliable fashion.

• Spatial Information Research
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• v.19 no.6
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• pp.43-54
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• 2011

This study proposes a rule-based image classification method for the time-series analysis of changes in the land surface of the Seongnam-Yongin area using satellite-image data from 2000 to 2009. In order to identify the change patterns during each period, 11 classes were employed in accordance with statistical/mathematic rules. A generalized algorithm was used so that the rules could be applied to the unsupervised-classification method that does not establish any training sites. The results showed that the urban area of the object increased by 145% due to housing-site development. The image data from 2009 had a classification accuracy of 98%. For method verification, the results were compared to land-cover changes through Post-classification comparison. The maximum utilization of the available data within multiple images and the optimized classification allowed for an improvement in the classification accuracy. The proposed rule-based image-classification method is expected to be widely employed for the time-series analysis of images to produce a thematic map for urban development and to monitor urban development and environmental change.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.123-135
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• 2015

In order to satisfy the high expectations of consumers for a variety of consumer's desired subject area, information could be provided in the form of a map according to the analysis information. With the name change in 2015, LX would intend to play a role in building the information infrastructure that can be supported government policy as an intermediary between the government and private sector. Therefore, in this study, we would like to propose a plan that provide personalized information to the consumer. Through compositing a variety of time-series data(inner or outer of LX) based on public information, and analyzing spatially and temporally the rapidly changing land status. For these purpose, prior research and domestic or abroad thematic map service about thematic map making were reviewed. And the reason why the LX makes information map was presented. Also, themes of 3 field were selected, and depending on the data processing or analysis level and theme were subdivided, and then production and expression method were proposed.

• 한국벤처창업학회:학술대회논문집
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• 2020.06a
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• pp.31-37
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• 2020

높은 위험을 안고 사업을 하는 벤처 기업은 자금 조달이 쉽지 않다. 벤처 캐피탈은 벤처의 재정적 필요를 해결하고 부족한 역량을 보충함으로써 벤처의 성공을 돕고 고위험 고수익의 벤처 생태계를 지탱하는 중요한 역할을 담당한다. 국내 벤처 캐피탈의 성장과 지속적인 관심에도 불구하고 학문적 성과가 충분히 축적되고 있는지는 의문이다. 이에 따라 본 연구는 2011년부터 2019년까지 벤처창업을 주제로 한 연구의 주요 흐름을 텍스트 마이닝 방법을 통해 고찰함으로써 문제를 진단하고 시사점을 도출하고자 한다. KCI 키워드 트렌드와 벤처 캐피탈의 성장에 관한 시계열 상관분석의 결과 학술적 성과가 벤처 캐피탈의 성장 추이를 따라가지 못하는 것으로 보인다. 또한 벤처창업연구의 주제 흐름을 바이그램과 TF-IDF로 관찰한 결과 2016 이후 창업 기업에 대한 연구 관심이 두드러지고 2019년에 들어 벤처 캐피탈에 관한 연구 커뮤니티의 관심이 높아진 것으로 나타났다. 본 연구의 결과는 벤처 캐피탈에 관한 주요 연구 토픽을 보다 더 적극적으로 발굴하고 탐구함으로써 연구 커뮤니티의 책무를 강화하고 한국의 벤처 캐피탈 성장과 그에 따른 이슈들을 논의할 이론적 기틀 마련이 필요함을 환기한다.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.151-160
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• 2016

The purpose of this research is to investigate Korean popular attitudes and social perceptions of 'sharing economy' terminology at the current moment from a creative or socio-economic point of view. In Korea, this study discovers and interprets the objective and tangible annual changes and patterns of sociocultural collective intelligence that have taken place over the last five years by applying text mining in the big data analysis approach. By crawling and Googling, this study collected a significant amount of time series web meta-data with regard to the theme of the sharing economy on the world wide web from 2010 to 2014. Consequently, huge amounts of raw data concerning sharing economy are processed into the value-added meaningful 'word clouding' form of graphs or figures by using the function of word clouding with R programming. Till now, the lack of accumulated data or collective intelligence about sharing economy notwithstanding, it is worth nothing that this study carried out preliminary research on conducting a time-series big data analysis from the perspective of knowledge management and processing. Thus, the results of this study can be utilized as fundamental data to help understand the academic and industrial aspects of future sharing economy-related markets or consumer behavior.