• Journal of Digital Contents Society
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• v.19 no.2
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• pp.229-236
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• 2018

The background fort this study is related to the concept of modern time and space which has been extended from physical space to virtual space in the present times witnessing the advent of the Fourth Industrial Revolution. Based on visual tactility and illusion in virtual reality, we intended to present the possibility of the interactions as realistic as the interactions occurring in reality. Regarding the scope of this study, we analyzed the relationship between illusion in game space and immersion in interactions based on the games used frequently among current virtual reality contents. The results of the study showed that the interactions in virtual reality occurred within the space converging the time and space and that the illusion of digital images had realistic position of reality without original. Research which investigates the interaction in virtual space and media projection method for real space and virtual space will provide useful basis for promoting technological development based on novel methods differentiated from existing image grammar and for identifying the content utilization methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.21-31
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• 2020

This study analyzed the spatio-temporal change pattern of greenhouse areas for Sangnam-myeon and Samrangjin-eup of Miryang-si in Gyeongnam, which is one of major greenhouse area. First, in order to overcome the lack of statistical data of the distribution of greenhouses, aerial and satellite images were interpreted from 1987 to 2018, and the spatial distribution of the greenhouse parcels which has continuously increased was mapped based on the digital cadastral map. Next, through the emerging hot spot tool in ArcGIS Desktop, the spatio-temporal change in transition from open-field to greenhouse cultivation was classified into 9 clusters. About 67.7% of the target area was categorized as a hot spot, and the pattern of New hot spot, which were recently converted to greenhouse parcels, covered about 34.1%. While, about 11.3% of parcels were expected to keep the existing open-field cultivation practice for a while. Overall, the greenhouse parcels have been densely developed along a river and were lately expanding even to the far neighbor. It implied that, in the future, the competition of water intake among farms would be more serious and the environmental responsibility in consideration of water quality as well as quantity would be getting strengthened due to increasing pollution loads and river intake.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.197-201
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• 2018

인공위성 기반의 원격탐사자료는 홍수, 가뭄 등 자연재해에 대한 모니터링 및 예측에 활용되어 왔으며, 특히 인공위성을 이용한 광역적 강수량 추정 자료는 지형적 제약을 받는 지상관측자료와 비교하여 시공간적으로 연속적이고 균질한 강수량 자료 취득이 가능하다는 장점이 있다. 우리나라의 경우 상대적으로 조밀한 지상관측망이 구축되어 있어 공간적으로 상세한 강수량 정보를 생산할 수 있는 여건을 갖추고 있지만, 북한 지역의 경우 기상, 수문, 통계자료에 관한 자료의 접근 및 품질의 제한성으로 인해 미계측 지역에 대한 강수량의 추정에 한계가 있다. CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) 데이터는 1999년부터 미국국제개발처 (U.S. Agency for International Development, USAID), 미국항공우주국 (National Aeronautics and Space Administration, NASA), 미국해양대기청 (National Oceanic and Atmospheric Administration, NOAA)의 지원으로 개발된 전지구 강우데이터 자료이다. CHIRPS는 1981년부터 현재까지 전지구 강우자료를 0.05도 격자 해상도로 제공하고 있으며, 강수량의 추세 분석 및 가뭄 모니터링을 위해 활용되고 있다. 본 연구에서는 CHG (Climate Hazards Group)에서 제공하고 있는 인공위성을 이용한 광역적 강수량 추정 자료인 CHIRPS와 남한 및 북한의 지상관측 강수량 자료와의 비교를 통해 위성으로부터 유도된 격자 강수량자료의 정확도 및 지역적인 강수추정의 불확실성을 평가하고, 수자원 및 재해 분야 이용 가능성을 검토하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.175-184
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• 2023

Korean Wave is now internationalized through the internet by social media, which have no space-time restrictions. This research examine the continuance use of K-Pop promotion using social media in Indonesia. In this study we apply the Expectation-Confirmation Model to analyze the effects of individual self-efficacy and perceived enjoyment on perceived usefulness, confirmation, and satisfaction of Social Media. As a research method for that purpose, the conformity of the model and the research hypothesis were verified using the structural equation model. As a result, it was found that the perceived enjoyment positively influences perceived usefulness, self-efficacy has a positive influence on perceived usefulness. We also found that confirmation positively affects both perceived usefulness and satisfaction, and that perceived usefulness positively affects satisfaction. Finally, satisfaction was found to always have a positive effect on intention to use.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.104-105
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• 2023

해양 관측과 위성 원격탐사를 이용하여 시공간적으로 다양하게 변하는 생태 어장 환경 및 선박 관련 자료를 획득할 수 있다. 이번 연구의 주요 목적은 야간 불빛 위성 자료를 이용하여 광범위한 해역에 대한 어선의 위치 분포를 파악하는 딥러닝 기반 모델을 제안하는 것이다. 제안한 모델의 정확성을 평가하기 위해 야간 조업 어선의 위치를 포함하고 있는 AIS(Automatic Identification System) 정보와 상호 비교 평가 하였다. 이를 위해, 먼저 AIS 자료를 획득 및 분석하는 방법을 소개한다. 해양안전종합시스템(General Information Center on Maritime Safety & Security, GICOMS)으로부터 제공받은 AIS 자료는 동적정보와 정적정보로 나뉜다. 동적 정보는 일별 자료로 구분되어있으며, 이 정보에는 해상이동업무식별번호(Maritime Mobile Service Identity, MMSI), 선박의 시간, 위도, 경도, 속력(Speed over Ground, SOG), 실침로(Course over Ground, COG), 선수방향(Heading) 등이 포함되어 있다. 정적정보는 1개의 파일로 구성되어 있으며, 선박명, 선종 코드, IMO Number, 호출부호, 제원(DimA, DimB, DimC, Dim D), 홀수, 추정 톤수 등이 포함되어 있다. 이번 연구에서는 선박의 정보에서 어선의 정보를 추출하여 비교 자료로 사용하였으며, 위성 자료는 구름의 영향이 없는 깨끗한 날짜의 영상 자료를 선별하여 사용하였다. 야간 불빛 위성 자료, 구름 정보 등을 이용하여 야간 조업 어선의 불빛을 감지하는 심층신경망(Deep Neural Network; DNN) 기반 모델을 제안하였다. 본 연구의결과는 야간 어선의 분포를 감시하고 한반도 인근 어장을 보호하는데 기여할 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.293-302
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• 2019

This study systematically analyzed computer-based driving intervention programs for seniors, to provide the academic background for driving intervention for seniors. Articles published from January 2009 till December 2018 were researched and analyzed. 'PubMed, Google Scholar, and Science Direct' were used to search articles published overseas, and 'RISS, KERIS, and KISS' searched for articles published in Korea. Based on the inclusion and exclusion criteria, totally 359 papers were retrieved, and 10 articles were finally analyzed; 8 articles (80%) were evidence level I, and 2 articles (20%) were evidence level III. Amongst the computer-based interventions, driving simulators (70%) were the most common, followed by two video image training (20%) and one Nintendo Wii program (10%). In most studies, driving simulators trained the cognitive and visual abilities of seniors and enhanced their abilities to cope with risk situations under various simulated circumstances. Other interventions were also reported to have a positive effect. For evaluating elderly drivers, the driving performance evaluation using a driving simulator was the most common; in addition, evaluations of attention, space-time ability, cognitive function, risk perception, depression and anxiety were also commonly used. We believe that it is appropriate to employ computer-based driving intervention programs for seniors to train and evaluate various domains. We expect that these interventions can be used as an effective tool for safe driving.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.301-309
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• 2017

The purpose of this study was to explore family caregiver's need for online education for dementia caregiving. Participants in this study were 220 family caregivers from district dementia centers in Seoul. Family caregiver's usability and needs of online education program were assessed using self-administered questionnaires. Descriptive statistics and t-test comparisons were conducted. About 50% of family caregivers answered they could use and have intention to use online education. The results showed that there were the highest demand for the video lectures which give information about dementia and case video about caregiving skills. There were differences in needs of online program according to the gender and age. The use of online program offers users the opportunity to participate support program at their own time and pace. In order to maximize the effects of online support programs, it is important to establish the strategies of the customized programs considering the characteristics of the caregivers.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.395-432
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• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.173-191
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• 2024

Typhoons are significant meteorological phenomena that cause interactions among the ocean, atmosphere, and land within Earth's system. In particular, wind speed, a key characteristic of typhoons, is influenced by various factors such as central pressure, trajectory, and sea surface temperature. Therefore, a comprehensive understanding based on actual observational data is essential. In the 2015 revised secondary school textbooks, typhoon wind speed is presented through text and illustrations; hence, exploratory activities that promote a deeper understanding of wind speed are necessary. In this study, we developed a data visualization program with a graphical user interface (GUI) to facilitate the understanding of typhoon wind speeds with simple operations during the teaching-learning process. The program utilizes red-green-blue (RGB) image data of Typhoons Mawar, Guchol, and Bolaven -which occurred in 2023- from the Korean geostationary satellite GEO-KOMPSAT-2A (GK-2A) as the input data. The program is designed to calculate typhoon wind speeds by inputting cloud movement coordinates around the typhoon and visualizes the wind speed distribution by inputting parameters such as central pressure, storm radius, and maximum wind speed. The GUI-based program developed in this study can be applied to typhoons observed by GK-2A without errors and enables scientific exploration based on actual observations beyond the limitations of textbooks. This allows students and teachers to collect, process, analyze, and visualize real observational data without needing a paid program or professional coding knowledge. This approach is expected to foster digital literacy, an essential competency for the future.