• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.1
• /
• pp.23-29
• /
• 2015

In this study, the spatial relevance between the regional housing price data and the spatial distribution of the location-based social media data is explored. The spatial analysis with rasterization was applied to this study, because the both data have a different form to analyze. The geo-tagged Twitter data had been collected for a month and the regional housing price index about sales and lease were used. The spatial range of both data includes Seoul and the some parts of the metropolitan area. 2,000m grid was constructed to consider the different spatial measure between two data, and they were combined into the constructed grids. The Hotspot Analysis was operated using the combined dataset to see the comparison of spatial distribution, and the bivariate spatial correlation coefficients between two data were measured for the quantitative analysis. The result of this study shows that Seocho-gu area is detected as a common hotspot of tweet and housing sales price index data. though the spatial relevance is not detected between tweet and housing lease price index data.

• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.29-34
• /
• 2016

Nowadays, real-time devices that provide health care for a companion animal is being developed by IoT technology and its demand such as smart puppy tag is increasing. However, it is difficult for IoT devices of companion animals to process complex nature due to miniaturized hardware and constructive nature. There is a clear limit to custom advanced features like health care implementation. This paper designs an integrated platform with statistical analysis which makes it possible to customized services such as feed production, pharmaceutical production, and health care for each companion animal. Middleware that collects sensor information, customer's spending pattern and information from Social Network Service is also designed by making use of IoT devices which companion animals wear. Furthermore, the paper designed data analyzer which analyzes and refines data from collected information that can be applied to personalized services.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.1
• /
• pp.261-271
• /
• 2014

Construction industry becomes large and complicated as that occurred in the construction processes and operations, and the growing social demand for more systematic and efficient methods of integrated management increases. Current facility maintenance system has been regarded as large labor-intensive and fairly stagnant, and faced on several issues including inefficiency in cost and time to collect and utilize various field information. Thus, this paper presents a maintenance prototype utilizing NFC technology and smart devices to efficiently collect and manage the field information in the course of the construction processes. By choosing dam facilities as a case study, construction data for dam inspection and maintenance were classified according to dam maintenance guideline, and database structure was constructed to incorporate with the NFC tags and smart device interface. The system prototype presented in this paper would improve the quality of the dam facilities maintenance system and provide business convenience and usability by contributing increased efficiency of field information management and sharing.

• Journal of Korean Society of societal Security
• /
• v.2 no.2
• /
• pp.49-54
• /
• 2009

High incident of accidents in construction jobsite became a social problem. According to the International Labour Organization (ILO), more than 60,000 fatal accidents occur each year in construction workplace worldwide. This number of accidents accounts for about 17 percent of all fatal workplace accidents. Especially, accidents from struck-by and falls comprise of over 60 percent of construction fatalities. This paper introduces a prototype of a received signal strength index (RSSI)-based safety monitoring to mitigate the potential accidents caused by falls and struck-by. Correlation between signal strength and noise index is examined to create the distance profile between a transmitter and a receiver. Throughout the distributed sensor nodes attached on potential hazardous objects, the proposed prototype envisions that construction workers with a tracker-tag can identify and monitor their current working environment in construction workplace, and early warning system can reduce the incidents of fatal accident in construction job site.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1117-1125
• /
• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.252-255
• /
• 2011

Growing demand for smart phones and data communication diminishes the constraints of Twitter and Facebook than a smartphone has become a subject of interest. On the other hand facebook users in their relationships to obtain the consent of the other, twitter is a relatively simple procedure for the information ripple effect is excellent. Twitter is beyond a simple social networking services(SNS) located in one of the popular media and powerful have the upper retweet. Retweet to the top of his sympathy with the ability th send tweets to their subscriber information can spread quickly. In this paper, we propose the smart retweet that system actively extend the existing retweet. In order to realize the smart retweet and additional criteria for determining the destination of the information is required. Based on tweet generated regional or an local information mentioned to tweet, to determine the destination. Smart retweet of the speed and scope of information transmission through the scale is expected.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.493-504
• /
• 2017

Recently, tremendous amounts of unstructured text data that is distributed through news, blogs, and social media has gained much attention from many researchers and practitioners as this data contains abundant information about various consumers' opinions. However, as the usefulness of text data is increasing, more and more attempts to gain profits by distorting text data maliciously or nonmaliciously are also increasing. This increase in spam text data not only burdens users who want to obtain useful information with a large amount of inappropriate information, but also damages the reliability of information and information providers. Therefore, efforts must be made to improve the reliability of information and the quality of analysis results by detecting and removing spam data in advance. For this purpose, many studies to detect spam have been actively conducted in areas such as opinion spam detection, spam e-mail detection, and web spam detection. In this study, we introduce core concepts and current research trends of spam detection and propose a methodology to detect the spam tag of a blog as one of the challenging attempts to improve the reliability of blog information.

• Journal of Information Science Theory and Practice
• /
• v.6 no.2
• /
• pp.46-61
• /
• 2018

The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.121-121
• /
• 2019

특정 지역에 집중적으로 비가 내리는 현상인 국지성호우가 빈번히 발생함에 따라 하천 주변 사회기반시설의 침수 위험성이 증가하고 있다. 침수 위험성 판단 여부는 주로 수위정보를 이용하며 수위 예측은 대부분 수치모형을 이용한다. 본 연구에서는 빅데이터 기반의 RNN(Recurrent Neural Networks)기법 알고리즘을 활용하여 수위를 예측하였다. 연구대상지는 조위의 영향을 많이 받는 한강 전역을 대상으로 하였다. 2008년~2018년(10개년)의 실제 침수 피해 실적을 조사한 결과 잠수교, 한강대교, 청담대교 등에서 침수 피해 발생률이 높게 나타났고 SNS(Social Network Services)와 같은 비정형화 자료에서는 청담대교가 가장 많이 태그(Tag)되어 청담대교를 연구범위로 설정하였다. 본 연구에서는 Python에서 제공하는 Tensor flow Library를 이용하여 수위예측 알고리즘을 적용하였다. 데이터는 정형화 데이터와 비정형 데이터를 사용하였으며 정형화 데이터는 한강홍수 통제소나 기상청에서 제공하는 최근 10년간의 (2008~2018) 수위 및 강우량 자료를 수집하였다. 비정형화 데이터는 SNS를 이용하여 민간 정보를 수집하여 정형화된 자료와 함께 전체자료를 구축하였다. 민감도 분석을 통하여 모델의 은닉층(5), 학습률(0.02) 및 반복횟수(100)의 최적값을 설정하였고, 24시간 동안의 데이터를 이용하여 3시간 후의 수위를 예측하였다. 2008년~ 2017년 까지의 데이터는 학습 데이터로 사용하였으며 2018년의 수위를 예측 및 평가하였다. 2018년의 관측수위 자료와 비교한 결과 90% 이상의 데이터가 10% 이내의 오차를 나타내었으며, 첨두수위도 비교적 정확하게 예측되는 것을 확인하였다. 향후 수위와 강우량뿐만 아니라 다양한 인자들도 고려한다면 보다 신속하고 정확한 예측 정보를 얻을 수 있을 것으로 기대된다.

• Journal of Information Technology Services
• /
• v.20 no.5
• /
• pp.11-28
• /
• 2021

As untact travel has become a new trend in leisure culture due to the spread of COVID-19, car camping market is rapidly increasing. The sales of car camping-related goods increased by up to 600 percent, and the sales of SUV in Korea also increased by about four times. Despite the growth of the car camping market, there is a lack of research on the actual condition of the car camping market or research on the user's perspective. Therefore, in this study, a survey of actual camping users was conducted to derive factors that they consider important in camping, and through this, a car camping map was produced. As a result, two types of maps were produced: a map about the car camping site and convenience facilities closest to the car camping site in Gangwon-do, and a hash tag themed map based on keywords for each car camping site. We gathered data on portal sites and social media to obtain information related to camping sites and proceeded with analysis using text mining. In addition, we extracted keywords using network analysis techniques and selected key themes that represent them. This allows the user to choose a car camping site by selecting keywords that suit their taste. We hope that this research will help car camping researchers as a prior study and provide a foundation for leading a clean camping culture through clean camping campaign. Also, we hope that car camping users will be able to do quality trip.