• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.423-423
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• 2023

습지 생태계는 탄소저장고, 대기 온·습도 조절 등의 기능을 수행하는 만큼 면밀한 관리가 요구된다. 습지의 규모와 생태계는 밀접한 연관성을 가지므로 그 규모를 우선적으로 파악할 필요가 있으며, 이를 위해 지표면의 상태를 산지, 습지, 수역 등의 항목으로 구분한 토지피복지도가 고려될 수 있다. 현재, 환경부에서 운영 중인 환경공간정보서비스(https://egis.me.go.kr/)에서는 각각 30 m, 5 m, 1 m의 공간 해상도와 7, 22, 41가지 분류 항목을 갖는 대분류, 중분류, 세분류로 구분된 토지피복지도를 제공하며 이러한 자료들은 모두 1년 이상의 시간 해상도를 갖는다. 습지의 경우, 계절에 따른 환경 변화로 인한 규모의 변동성이 크게 나타날 수 있기 때문에 1년 이하의 시간 해상도를 갖는 고품질 토지피복 분류 정보가 요구된다. 따라서 본 연구에서는 기존 자료의 낮은 시간 해상도 보완을 목표로, 1개월과 30 m의 시·공간 해상도를 갖는 토지피복지도를 구축하기 위한 방법론을 제안하고자 한다. 이를 위해 Landsat-8 등과 같은 다양한 인공위성 자료를 수집하고, Support Vector Machine 등과 같은 머신러닝 기법을 적용하였다. 최종적으로 습지보전법에서 지정한 습지보호지역 중 내륙습지 26개소를 대상으로, 본 연구로부터 산출된 토지피복지도를 기존 환경공간정보서비스 내 대분류 토지피복지도와 비교·평가하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.155-160
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• 2008

The urban areas have various landuses such as residential, commercial, industrial and official purposes that are highly concerned with human activities. The other landuses are relating to vehicle activities, which are roads, parking lots, bridges, parks etc. The mainly using landuses by human activities are possessing three different areas that are buildings, parking lots/roads and landscapes. Of these areas, the buildings and landscapes can be classified as non-pollution areas. However, the parking lots or roads are classifying as the main pollution areas because of vehicle activities. Therefore, the landuses arising the nonpoint pollution during a storm in urban areas are roads and parking lots. The vehicles are emitting lots of nonpoint pollutants such as metals and particulate matters and it is impacting on water qualities and aqua-ecosystems nearby the city areas. Therefore, this research was conducted for characterizing the pollutant types and determining the EMCs (Event Mean Concentrations) and unit pollutant loads during a storm. The monitoring was performed on 9 locations such as highways, service area, tollgates, parking lot and bridges. All of the landuses selected for monitoring are concerned with transportation. The results can be effectively used to predict the pollutant loading before urban planning and to select the BMPs (Best Management Practices) for reducing the pollution.

• Journal of Animal Science and Technology
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• v.45 no.6
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• pp.997-1006
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• 2003

This study was conducted to investigate the national methane emission from livestock enteric fermentation. For methane emission estimation, livestock were mainly categorized to cattle, swine, poultry, sheep, goats and horses, and cattle were further sub-categorized to calves, fattening cattle, breeding cows in Hanwoo and calves, fattening cattle and lactating cows in dairy cattle. Tier 2 methane emission factors were deduced based on the characteristics of animal performances, live weight, slaughter weight, daily weight gain, and feed digestibility in each category. Tier 2 emission factors of Hanwoo range from 39 to 49 kg/head/year and it is similar to that of Tier 1(47kg/head/year). Tier 2 emission factor of dairy cattle was 107 kg/head/year and it is slightly lower than that of Tier 1(118kg/head/year). Total methane emission from livestock enteric fermentation by Tier 2 method was estimated to be 126.8 tones in 2001. The methane emissions by Hanwoo, dairy cattle, swine, goats, horses and sheep were 61.70, 47.76, 13.08, 2.25, 0.17 and 0.01 tones, respectively. By the use of Tier 2 method instead of Tier 1, the accuracy and reliability of methane emission estimates from livestock enteric fermentation in Korea is considered to be improved.

• Management & Information Systems Review
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• v.36 no.1
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• pp.181-213
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• 2017

According to the aging, the medical device industry is focused as a future promising industry. However, Korea medical device industry is not enough market competitiveness due to a narrow domestic market and a small company structure. This study aims at evaluating medical device priorities following 3 steps. First, we classify the medical device into three hierarchy categories and AHP survey was conducted on 30 experts in order to extract medical device priorities. Second, priority scores of medical device are analysed using AHP and Fuzzy-AHP. Third, a most important medical device is selected by comparing the volume of medical device manufacture and priority scores. As a result, 'dental implant' is the most import medical device, and we suggest a strategy based on a positioning map. The proposed methodology will provide a inspiration for establish of R&D and support policy in the medical device industry.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1061-1073
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• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.

• The Korean Journal of Financial Management
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• v.21 no.1
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• pp.87-124
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• 2004

Some studies indicate that investors systematically underreact to new information in the stock market and Other studies indicate that investors systematically overreact. If investors irrationally react to the R&D intensity information, The portfolio strategy based on the R&D intensity information will be provided substantial excess returns. This study investigate that investors systematically underreact or overreact to the R&D intensity and whether portfolio strategy based on the R&D intensity is useful or not. Major results we as follows. First, This study indicate that investor systematically underreact to high R&D intensity and overreact low R&D intensity information. Second, after controlling the firm's specific factor such as firm size, BV/MV and past price performance, it is found that the performance of portfolio strategy based on the R&D intensity is not significant.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.367-380
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• 2022

In this study, the periodic and simplified update and production way of the ECVAM (Environmental Conservation Value Assessment Map) was presented through the classification of environmental values using KOMPSAT-3A satellite imagery and land cover map. ECVAM is a map that evaluates the environmental value of the country in five stages based on 62 legal evaluation items and 8 environmental and ecological evaluation items, and is provided on two scales: 1:25000 and 1:5000. However, the 1:5000 scale environmental assessment map is being produced and serviced with a slow renewal cycle of one year due to various constraints such as the absence of reference materials and different production years. Therefore, in this study, one of the deep learning techniques, KOMPSAT-3A satellite image, SI (Spectral Indices), and land cover map were used to conduct this study to confirm the possibility of establishing an environmental assessment map. As a result, the accuracy was calculated to be 87.25% and 85.88%, respectively. Through the results of the study, it was possible to confirm the possibility of constructing an environmental assessment map using satellite imagery, optical index, and land cover classification.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.55-59
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• 2004

유한요소법을 이용하여 전자장을 해석할 경우 전류원이 전 영역에 비해 극히 작은 영역이면, 요소분할 과정에서 소스부분을 세분하여야 하므로 결국 미지수의 증가를 가져오게 된다. 또한, 선전류 문제의 경우 2차원 유한 요소 해석이 용이하지 않다. 이를 보안하기 위해 본 논문에서는 소스가 선전류이고 관심 영역이 선전류원으로부터 떨어져 있는 경우, 소스 영역은 해석해를 적용하여 유한요소법과 결합하는 방법을 제시하였다. 해석적인 해는 원통좌표계에서 반정에 대한 멱함수와 회전각도에 대한 삼각함수의 곱의 형태로 표현된다. 이때 두 종류의 적분 상수가 있는데, 이는 경계상의 포텐셜값과 유한요소법의 경계 적분항을 푸리에급수로 전개한 계수로 표현된다. 제안한 알고리즘의 검증을 위하여 해석해가 존재하는 모델을 설정하여 해석적인 방법, 기존의 유한요소 법 및 결합 방법에 의한 해를 비교 검증하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.275-278
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• 2002

본 논문에서는 한국의 전파환경을 도시 계획법에서 지정한 지역세분을 기초로 4개의 대분류와 8개로 소구분하여 Kor-231 모델을 제안하였다. 전파전파 특성을 측정하기 위해 슬라이딩 코릴레이션 기법을 이용한 대역확산 송${\cdot}$수신 방식으로 구현하여 수신전력, 평균 초과지연 그리고 RMS 지연확산을 가장 도시계획적으로 개발된 공업 지역과 주거지역에서 직선 도로에 마이크로셀을 배치하여 도로를 따라 진행하는 LOS와 건물들로 둘러쌓인 N-LOS에서 측정된 데이터를 분석하여 한국의 전파환경에서 잘 적용됨을 확인 하였다.