• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.209-210
• /
• 2016

우리나라의 연평균강수량은 약 1362 mm이며, 총강수량의 약 30% 이상이 증발산을 통해 손실되고 있다고 추정되어지고 있다. 증발산은 물 수지 분석에 있어 매우 중요한 성분이며, 많은 부분을 차지하지만 다른 요인들에 비해 직접적인 관측이 어려워 과거에는 경험식을 사용하거나 단순하게 가정에 의해 결정해 왔다. 또한 기상자료로부터 증발산량을 추정하거나 증발접시나 추정식으로 잠재증발산을 추정하고 있다. 또한 최근 기후변화의 가속화에 따른 홍수의 가뭄의 강도와 빈도가 높아지고 있으며, 이에 따라 수자원 관리에 있어서 기초수문조사 항목에 많은 변화를 요구하고 있다. 그 결과 2007년 4월 하천법 개정으로 증발산량 및 토양수분량이 기초수문조사 항목으로 추가되었으며, K-water 연구원에서는 용담시험유역에 플럭스타워를 설치하였고 현재 운영 중에 있다. 덕유산 플럭스타워는 용담시험유역 내에 위치한 금강 수계 구량천 상류부의 덕곡제 유역 내에 설치하였으며, 2011년 4월부터 실제 증발산량을 관측하고 있다. 동경 $127^{\circ}$42'23" ~ $127^{\circ}$44'53", 북위 $35^{\circ}$50'47" ~ $35^{\circ}$52'50"사이로 중부지방에 위치한 유일한 증발산관측 타워이다. 유역 면적은 9.27 km2으로 유로연장 3.48 km, 유역 평균폭 2.66 km, 형상계수는 0.77이며, 덕곡제플럭스 타워 주변의 토지이용은 대부분 산림으로 구성되어 있으며, 침활 혼효림과 낙엽송림으로 임상 분포가 이루어져 있다. 주요 관측기기로는 3차원 풍향 풍속계, $CO_2/H_2O$ 기체분석기, 순복사 측정 센서, 지중열플럭스 측정 센서 등이 있다. 2011년부터 측정된 자료를 바탕으로 에디공분산 방법을 이용하여 증발산량을 측정하였으며, 30분간의 데이터 18,000개 중 취득률 90 % 이상의 데이터를 대상을 분석을 실시하였다. 2011 ~ 2015년도 증발산량 분석 결과는 아래의 표와 같다. 증발산의 패턴은 1월부터 서서히 증가하지만 활발하지는 않고, 4월부터 매우 활발해져 8월에 최대치에 이른다. 10월부터 증발산량은 급격히 감소하기 시작하며 11, 12월에는 증발산이 거의 발생하지 않는 공통적인 경향을 보였다. 2013년 8, 9월은 다른 해와 다른 경향을 보이고 있는데, 이는 2013년 8, 9월에 강우가 많이 발생하여 증발산량이 감소하였기 때문으로 판단된다. 2015년 8월은 다른 년도와 비교했을 때, 매우 높은 증발산량을 보이는데 이는 2015년 8월에 많은 강우에도 식생이 활발하게 작용하였기 때문으로 판단된다.

• Proceedings of the KSR Conference
• /
• 2008.11b
• /
• pp.30-36
• /
• 2008

The Spatial Image contents of Geomorphology 3-D environment is focused by the requirement and importance in the fields such as, national land development plan, telecommunication facility management, railway construction, general construction engineering, Ubiquitous city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we tested of the railway facilities using laser surveying system, then we propose data a generation of spatial images for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation. As the results, We confirmed the solutions of varieties application for railway facilities management using 3-D spatial image contents.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.224-229
• /
• 2018

Korea is experiencing a rapid increase in electricity consumption due to rapid economic development, and many power transmission towers are installed to provide smooth power supply. The high-voltage transmission line is mainly made of aluminum stranded wire, and the wire is loosely guided so that some deflection is maintained. The degree of deflection has a great influence on the quality of the construction and the life of the cable. As the time passes, the shrinkage and expansion occur repeatedly due to the weight of the cable and the surrounding environment. Therefore, periodic monitoring is essential for the management of the power transmission line. In this study, the power transmission lines were monitored using 3D laser scanning technology. The data of the power transmission line of the study area was acquired and the point cloud type 3D geospatial information of the transmission line was extracted through data processing. The length of the transmission line and deflection amount were calculated using the 3D geospatial information of the transmission line, and the distance from the surrounding obstacles could be calculated effectively. The result of study shows the utilization of 3D laser scanning technology for transmission line management. Future research will contribute to the efficiency of transmission line management if a transmission line monitoring system using 3D laser scanning technology is developed.

• 한국체육학회지인문사회과학편
• /
• v.58 no.6
• /
• pp.449-459
• /
• 2019

The purpose of this study is to identify how postural mechanics affects postural control on balance and stability by using frequency analysis technique from the kinematic data acquired during the one leg standing posture. For this purpose, the experimental group consisted of two groups, the normal group (n=6) and the national Gymnastics group (n=6). Displacement data of CoP were analyzed by frequency analysis of rambling (RM) and trembling (TR) by FFT signal processing. As a results, there was a significant difference in evaluating the stabilization index between the two groups with the eyes open and closed one leg stnading (p <.05). The cause of the difference was found to be the output of the maximum amplitude of RM (f1) and TR (f2) (p <.05). In particular, in the low frequency RM of 8-9 Hz, which is a natural frequency of signal wave involved in postural feedback feedback, the main frequency appeared to be performs the exercise mechanism of stable brain posture control. And in the high frequency TM of 120-135 Hz, it is considered that the adaptation of the reflective muscle response is minimized to minimize posture shaking. In conclusion, this study provides evidence for the intrinsic main frequencies according to the postural control ability which affects the CNS in one leg standing.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.409-418
• /
• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_1
• /
• pp.1349-1365
• /
• 2020

In recent years, in the fields of smart cities and digital twins, research on model generation is increasing due to the advantage of acquiring actual 3D coordinates by using point clouds. In addition, there is an increasing demand for a solid model that can easily modify the shape and texture of the building. In this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. Accordingly, in this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. In the first step, the ground points were removed through the planarity analysis of the point cloud. In the second step, building area was extracted from the ground removed point cloud. In the third step, detailed structural area of the buildings was extracted. In the fourth step, the shape of 3D building models with 3D coordinate information added to the extracted area was created. In the last step, a 3D building solid model was created by giving texture to the building model shape. In order to verify the proposed method, we experimented using point clouds extracted from unmanned aerial vehicle images using commercial software. As a result, 3D building shapes with a position error of about 1m compared to the point cloud was created for all buildings with a certain height or higher. In addition, it was confirmed that 3D models on which texturing was performed having a resolution of less than twice the resolution of the original image was generated.

• The Journal of Engineering Geology
• /
• v.31 no.4
• /
• pp.701-718
• /
• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.483-490
• /
• 2021

Due to the recent increase in various problems from underground development in urbanized areas, accurate underground facility information management is highly needed. Therefore, in this study, in order to utilize the Integration Map of Underground Goespatial Information in real time on-site, the function of comparing the mutual location of the GPR (Ground Penetration Radar) sensing data and the Integration Map of Underground Goespatial Information, and function of analyze underground facilities, and function of converting surveying data into a shape file through position correction & attribute editing in a 3D space, and the function of submitting the shape file to the Integration Map of Underground Goespatial Information mobile center was defined and developed as a program. In addition, for the on-site application test of the development program, scenarios used at the underground facility real-time survey site and GPR exploration site were derived, and four sites in Seoul were tested to confirm that the use scenario worked properly. Through this, the on-site utilization of the program developed in this study could be confirmed, and it would contribute to the confirmation of the quality of Shape-file and the "update automation" of "Integration Map of Underground Goespatial Information". In addition, it is expected that the development program will be further applied to the Underground Facility Map's Accuracy Improvement Diffusion Project' promoted by the MOLIT (Ministry of Land, Infrastructure, and Transport).

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.391-396
• /
• 2021

In this study, items for forest policy and forest resource research in Austria, Japan, New Zealand, and Indonesia, which are major forest advanced countries, were investigated, and the applicability of point cloud data acquired through laser scanning was identified. Through the study, it was found that forest policies in developed countries are being pursued for the purpose of sustainable forest conservation and management, job creation, and timber productivity improvement, and that new technologies are being researched and applied to actual projects. Korea has a high proportion of forests compared to the national land area compared to major forestry developed countries, but the accumulation of trees is relatively low, so it is a time for scientific forest management to improve the accumulation of trees. To understand the applicability of laser scanning technology, a forest resource survey using point cloud data was conducted, and the diameter of breast height, height, number of trees per unit area were calculated, and the shape of the crown was identified. If field experiments and accuracy evaluations applying various laser scanning technologies are carried out in the future, it will be possible to present the quantitative improvement of forest resource survey using foil cloud.

• Journal of the Korean Geotechnical Society
• /
• v.24 no.10
• /
• pp.57-70
• /
• 2008

The advantage of piezocone penetration test is a guarantee of continuous data, which is a source of reliable interpretation of target soil layer. Many researches have been carried out f3r several decades and several classification charts have been developed to classify in-situ soil from the cone penetration test result. Since most present classification charts or methods were developed based on the data which were compiled over the world except Korea, they should be verified to be feasible for Korean soil. Furthermore, sometimes their charts provide different soil classification results according to the different input parameters. However, unfortunately, revision of those charts is quite difficult or almost impossible. In this research a new soil classification model is proposed by using fuzzy C-mean clustering and neuro-fuzzy theory based on the 5371 CPT results and soil logging results compiled from 17 local sites around Korea. Proposed neuro-fuzzy soil classification model was verified by comparing the classification results f3r new data, which were not used during learning process of neuro-fuzzy model, with real soil log. Efficiency of proposed neuro-fuzzy model was compared with other soft computing classification models and Robertson method for new data.