• Journal of Society of Occupational Therapy for the Aged and Dementia
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• v.12 no.2
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• pp.67-74
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• 2018

Objective : The purpose of this study is to quantitatively analyze the role of occupational therapy in long - term care insurance for the elderly using text mining, one of the big data analysis techniques. Method : For the analysis of newspaper articles, "Long - Term Care Insurance for the Elderly + Occupational Therapy for the Elderly" was collected after the period from 2007 to 208. Naver, which has a high share of the domestic search engine, utilized the database of Naver News by utilizing Textom, a web crawling tool. After collecting the article title and original text of 510 news data from the collection of the elderly long term care insurance + occupational therapy search, we analyzed the article frequency and key words by year. Result : In terms of the frequency of articles published by year, the number of articles published in 2015 and 2017 was the highest with 70 articles (13.7%), and the top 10 terms of the key word analysis showed the highest frequency of 'dementia' (344) In terms of key words, dementia, treatment, hospital, health, service, rehabilitation, facilities, institution, grade, elderly, professional, salary, industrial complex and people are related. Conclusion : In this study, it is meaningful that the textual mining technique was used to more objectively confirm the social needs and the role of the occupational therapist for the dementia and rehabilitation in the related key keywords based on the media reporting trend of the elderly long - term care insurance for 11 years. Based on the results of this study, future research should expand research field and period and supplement the research methodology through various analysis methods according to the year.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.332-342
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• 2018

This study is aimed at suggesting information about technical choices for designing LED camping lights based on emotional lighting contents of integrated IOT and design areas which take a central role in creation and knowledge based industries and the procedure for materializing them. 'i-Light,' a portable LED camping light, is 'connected lighting' connecting men, space and emotion and a smart camping light based on IOT and emotional lighting contents. 'i-Light' has two functions. One is about lighting for adjusting color and color temperature naturally and the other is about safety for detecting harmful gases. 'i-Light' also has various emotional functions for experiencing interaction and taste of light. For the purpose, portable LED camping lights were designed, first of all, and then a highly color rendering/full-color lighting module, a smart sensor module and an IOT device platform were developed. In addition, efforts were made to establish detailed data about emotional lighting contents and to develop a Web application based on them. Finally, prototypes of portable LED camping lights were made to get a test bench and usability evaluation from related organizations. According to the results, all of 12 developed emotional lighting contents and three IOT safety sensors were suitable and prototypes were satisfactory. This paper will suggest a direction about actual technical choices for development of contents and products integrating artificial intelligence and big data and about the procedure for materializing them.

• Journal of IKEEE
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• v.23 no.3
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• pp.1088-1091
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• 2019

In this paper, a real-time monitoring system for environmental safety by living area is proposed. The proposed system is designed to measure radiation, fine dust and basic living information (temperature) using fixed and mobile measuring equipment, and constitutes a web database that stores data received from the equipment. It also develops web programs for displaying received data on PCs and mobile phones. The results of testing the performance of the system by an authorized testing agency showed that the radiation measurement range was measured in the range of $10{\mu}Sv/h$ to 10mSv/h, which is comparable to the world's highest level, and that the accuracy was measured between ${\pm}6.7$ and ${\pm}8.7$ percent of the measurement uncertainty was measured and normal operation at or below the international standard of ${\pm}15$ percent. In addition, the temperature test was conducted on a section of $-20^{\circ}C$ to $50^{\circ}C$ and normal operation was confirmed in response to the temperature change. Stability of radiated electromagnetic waves was ensured by a suitable judgment. The product's testing in general and high and low temperature environments for about four months after the prototype was made confirmed to be more than five years of durability. The measurement range and accuracy of fine dust sensors are compared with those of companies that measure the air environment, and the performance level is similar through the air quality measurement register.

• Journal of the Korean Society for information Management
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• v.36 no.3
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• pp.149-174
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• 2019

This study analyzes the actual use data of the websites of university libraries, analyzes the users' usage behavior, and proposes improvement measures for the websites. The study analyzed users' traffic and analyzed their usage behavior from January 2018 to December 2018 on the C University website. The website's analysis tool used 'Google Analytics'. The web traffic variables were analyzed in five categories: user general characteristics, user environment analysis, visit analysis, inflow analysis, site analysis, and site analysis based on the metrics of sessions, users, page views, pages per session, average session time, and bounce rate. As a result, 1) In the analysis results of general characteristics of users, there was some access to the website not only in Korea but also in China. 2) In the user experience analysis, the main browser type appeared as Internet Explorer. The next place was Chrome, with a bounce rate of Safari, third and fourth, double that of the Explore or Chrome. In terms of screen resolution, 1920x1080 resolution accounted for the largest percentage, with access in a variety of other environments. 3) Direct inflow was the highest in the inflow media analysis. 4) The site analysis showed the most page views out of 4,534,084 pages, followed by the main page, followed by the lending/extension/history/booking page, the academic DB page, and the collection page.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.49-63
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• 2008

RDF is the base ontology model which is used in Semantic Web defined by W3C. OWL expands the RDF base model by providing various vocabularies for defining much more ontology relationships. Recently Jain and Farkas have suggested an RDF access control model based on RDF triple. Their research point is to introduce an authorization conflict problem by RDF inference which must be considered in RDF ontology data. Due to the problem, we cannot adopt XML access control model for RDF, although RDF is represented by XML. However, Jain and Farkas did not define the authorization propagation over the RDF upper/lower ontology concepts when an RDF authorization is specified. The reason why the authorization specification should be defined clearly is that finally, the authorizatin conflict is the problem between the authorization propagation in specifying an authorization and the authorization propagation in inferencing authorizations. In this article, first we define an RDF access authorization specification based on RDF triple in detail. Next, based on the definition, we analyze the authoriztion conflict problem by RDF inference in detail. Next, we briefly introduce a method which can quickly find an authorization conflict by using graph labeling techniques. This method is especially related with the subsumption relationship based inference. Finally, we present a comparison analysis with Jain and Farkas' study, and some experimental results showing the efficiency of the suggested conflict detection method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of the Korean earth science society
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• v.42 no.1
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• pp.76-87
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• 2021

In this study, temporal trend of researches in earthquake with groundwater level, water quality, radon, remote sensing, electrical resistivity, gravity, and geomagnetism was searched from 2001 to 2020, using the journals indexed in Web of Science, and the number of articles published in international journals was counted in relation to the occurrences of earthquakes (≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0). The number of articles shows an increasing trend over the studied period. This is explained by that studies on earthquake precursor and seismic monitoring becomes active in various fields with integrated data analysis through the development of remote sensing technology, progress of measurement equipment, and big data. According to Mann-Kendall and Sen's tests, gravity-related articles exhibit an increasing trend of 1.30 articles/yr, radon-related articles (0.60 articles/yr), groundwater-related articles (0.70 articles/yr), electrical resistivity-related articles (0.25 articles/yr), and remote-sensing-related articles (0.67 articles/yr). By cross-correlation analysis of the number of articles in each field with removing trend effect and the number of earthquakes of ≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0, radon and remote sensing fields exhibit a high cross-correlation with a delay time of one year. In addition, large-scale earthquakes such as the 2004 and 2005 Sumatra earthquake, the 2008 Sichuan earthquake, the 2010 Haiti earthquake, and the 2010 Chile earthquake are estimated to be related with the increase in the number of articles in the corresponding periods.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.