• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.84-84
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• 2020

새만금은 농업용지 확보 등 다양한 용도의 토지이용을 목적으로 방조제 공사를 수행하였으며, 새만금 종합개발계획(Master Plan, OPC, 2011)에 의하여 호내의 준설을 계획하고 있다. 그러나 방조제 준공 후 새만금 호내의 수질오염 문제가 지속적으로 발생되어 왔다. 수질오염의 문제를 해결하기위해서 강변저류지 설치, 하수종말 처리장 등 다양한 구조적 대책이 수립되고 시행되었지만, 수질개선 효과는 상대적으로 미비하였다(Kim et al, 2016; KRCC, 2016). 본 연구에서는 새만금 호내의 수질개선을 위하여, 다양한 배수갑문 운영에 따른 수질인자의 잔존률 및 하상의 변화를 SCHISM-CoSiNE(Semi-implicit Cross-scale Hydroscience Integrated System Model and Carbon, Silicate, Nitrogen Ecosystem model) 모형을 통하여 검토하고자 한다. 수치모형의 하상변동 및 수질변화에 대한 정확한 수치계산 여부를 판단하기 위하여 van Rijn(1987) 실험 및 새만금호내의 수질 관측자료(DO, T-N, T-P, 온도, 염도, 새만금유역 통합환경관리시스템)를 이용하여 수치모형 검증을 수행하였고 10% 이내의 오차를 나타내었다. 배수갑문 운영기록(Jeong et al., 2018)을 참조하여 배수갑문 운영을 재현하였으며, 지형은 기 수립된 MP 내 새만금 종합개발계획이 완료된 시점인 2030년을 기준으로 하였다. 수치모의를 통하여, 배수갑문 운영 및 계절의 변화에 따른 최심 하상변동고 변화 및 하상변동량을 확인하여 침식 퇴적 구간을 구분하였고, 호내의 잔존하는 수질인자의 농도를 통하여 수질개선 효과를 평가하여 수질측면에서 최적의 배수갑문 운영방안을 제시하였다. 본 연구는 배수갑문 운영이 새만금 호내의 수질 및 지형에 미치는 영향에 대한 기초적인 연구이지만 향후에는 다양한 수질인자 및 시나리오를 고려한다면 보다 근본적인 수질오염 해결방안으로 활용이 가능할 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.233-242
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• 2010

SEBAL (Surface Energy Balance Algorithm for Land) developed by Bastiaanssen (1995) is an image-processing model comprisedof twenty-five sub models that calculates spatial evapotranspiration (ET) and other energy exchanges at the surface. SEBAL uses image data from Landsat or other satellites measuring thermal infrared radiation, visible and near infrared. In this study, the model was applied to Gyeongancheon watershed, the main tributary of Han river Basin. ET was computed on apixel-by-pixel basis from an energy balance using 4 years (2001-2004) Landsat and MODIS images. The scale effect between Landsat (30 m) and MODIS (1 km) was evaluated. The results both from Landsat and MODIS were compared with FAO Penman-Monteith ET. The absolute errors between satellite ETs and Penman-Monteith ET were within 12%. The spatial and temporal characteristics of ET distribution within the watershed were also analyzed.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.419-419
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• 2022

최근 전 지구적 기후변화의 발생으로 수문현상의 규모와 빈도가 예측하기 어려운 수준으로 변화되고 있다. 이에 따라 정밀한 데이터를 활용한 수공구조물 운영 및 관리의 중요성이 대두되고 있다. 이 중에서도 다목적댐은 이·치수 측면에서 모두 활용되기 때문에 정밀한 댐 운영을 위한 댐 유입량 자료의 수집 및 관리가 필요하지만 현실적 한계로 인해 간접적으로 측정되고 있다. 현재 국내 다목적댐 저수지의 유입량은 댐시설 유지관리 기준(MW, 1994)에서 제시한 저수지 수위 변동량과 댐 방류량의 추정치로부터 계산하는 간접측정방법을 통해 산정되고 있다. 그러나 이와 같은 방법은 태풍이나 집중호우 등 대규모 홍수 발생 시 저수지 수위의 불균일성으로 인한 오차가 나타나며, 음유입량 및 톱니바퀴 형태의 자료가 발생하는 등 정확도 측면에서 한계가 있다. 따라서 본 연구에서는 한국건설기술연구원에서 2008년 개발한 물리적 기반의 분포형 유출해석 모형인 GRM(Grid based Rainfall-Runoff Model)을 활용하여 상류 유량관측소(옥동교 관측소, 영춘 관측소) 관측유량과 충주댐 지점 모의유량간의 경험공식을 도출하였으며, 이를 통해 상류 유량 관측소의 유량자료를 활용한 댐 유입량 직접산정이 가능하도록 하였다. 또한 다중 관측소 활용 시 댐 유입량 모의 성능이 개선되는지 여부를 확인하기 위해 3가지 경우(옥동교 관측소 단일, 영춘 관측소 단일, 옥동교·영춘 관측소 다중)로 구분하고 각 공식의 성능을 비교 평가하였다. 분석 결과 상류 관측소 관측유량과 댐 본체 지점의 모의유량이 비교적 높은 상관관계(0.79~0.96)를 보였으며, 단일 관측소를 활용한 공식 대비 다중 관측소를 활용한 공식이 더 높은 결정계수를 보였다.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.522-527
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• 2022

Object tracking is a field of signal processing that sequentially tracks the location of an object based on the previous-time location estimations and the present-time observation data. In this paper, we propose an adaptive scaling neural network that can track and adjust the scale of the input data with three recursive neural network (RNN) submodules. To evaluate object tracking performance, we compare the proposed system with the Kalman filter and the maximum likelihood object tracking scheme under an one-dimensional object movement model in which the object moves with piecewise constant acceleration. We show that the proposed scheme is generally better, in terms of root mean square error (RMSE) performance, than maximum likelihood scheme and Kalman filter and that the performance gaps grow with increased observation noise.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.103-116
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• 2008

In this study, in-situ testing method, Dynamic Cone Penetration Test(DCPT) was presented to establish a new compaction control criteria with using mechanical property like elastic modulus instead of unit weight for field compaction control. Soil chamber tests and in-situ tests were carried out to confirm DCPT tests can predict the designed elastic modulus after field compaction, and correlation analysis among the DCPT, CBR and resilient modulus of sub grade were performed. Also, DCPT test spacing criteria in the construction site was proposed from the literature review. In the result of laboratory tests, Livneh's equation was the best in correlation between PR of DCPT and CBR, George and Pradesh's equation was the best in the predicted resilient modulus. In the resilient modulus using FWD, Gudishala's equation estimates little larger than predicted resilient modulus and Chen's equation estimates little smaller. And KICT's equation estimates the modulus smaller than predicted resilient modulus. But using the results of laboratory resilient modulus tests considering the deviatoric and confining stress from the moving vehicle, the KICT's equation was the best. In the results of In-situ DCPT tests, the variation of PR can occur according to size distribution of penetrate points. So DCPT test spacing was proposed to reduce the difference of PR. Also it was shows that average PR was different according to subgrade materials although the subgrade was satisfied the degree of compaction. Especially large sized materials show smaller PR, and it is also found that field water contents have influence a lot of degree of compaction but a little on the average PR of the DCPT tests.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.274-284
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• 2022

Through sample-size-based rarefaction analyses, we tried to suggest the appropriate degree of sample concentration and sub-sample extraction, as a way to estimate more accurate zooplankton species diversity when assessing biodiversity. When we collected zooplankton from three reservoirs with different environmental characteristics, the estimated species richness (S) and Shannon's H' values showed different changing patterns according to the amount of sub-sample extracted from the whole sample by reservoir. However, consequently, their zooplankton diversity indices were estimated the highest values when analyzed by extracting the largest amount of sub-sample. As a result of rarefaction analysis about sample coverage, in the case of deep eutrophic reservoir (Juam) with high zooplankton species and individual numbers, it was analyzed that 99.8% of the whole samples were represented by only 1 mL of sub-sample based on 100 mL of concentrated samples. On the other hand, in Soyang reservoir, which showed very small species and individual numbers, a relatively low representation at 97% when 10 mL of sub-sample was extracted from the same amount of concentrated sample. As such, the representation of sub-sample for the whole zooplankton sample varies depending on the individual density in the sample collected from the field. If the degree of concentration of samples and the amount of sub-sample extraction are adjusted according to the collected individual density, it is believed that errors that occur when comparing the number of species and diversity indices among different water bodies can be minimized.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.427-434
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• 2019

In the present study, we assessed the parallel computing performance of Agisoft Metashape for orthomosaic generation, which can implement aerial triangulation, generate a three-dimensional point cloud, and make an orthomosaic based on SfM (Structure from Motion) technology. Due to the nature of SfM, most of the time is spent on Align photos, which runs as a relative orientation, and Build dense cloud, which generates a three-dimensional point cloud. Metashape can parallelize the two processes by using multi-cores of CPU (Central Processing Unit) and GPU (Graphics Processing Unit). An orthomosaic was created from large UAV (Unmanned Aerial Vehicle) images by six conditions combined by three parallel methods (CPU only, GPU only, and CPU + GPU) and two operating systems (Windows and Linux). To assess the consistency of the results of the conditions, RMSE (Root Mean Square Error) of aerial triangulation was measured using ground control points which were automatically detected on the images without human intervention. The results of orthomosaic generation from 521 UAV images of 42.2 million pixels showed that the combination of CPU and GPU showed the best performance using the present system, and Linux showed better performance than Windows in all conditions. However, the RMSE values of aerial triangulation revealed a slight difference within an error range among the combinations. Therefore, Metashape seems to leave things to be desired so that the consistency is obtained regardless of parallel methods and operating systems.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.3
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• pp.191-204
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• 2017

Concerns on the air pollution problem caused by ambient fine particles have become a big social issue in Korea. Important factors that should be addressed to develop effective and efficient air quality management policy, especially, against fine particles are discussed and research and policy directions to address these factors are suggested. It is suggested that two factors are in high priority; one is scientific understanding of the major formation mechanisms of fine particles and the other is the process of policy decision and implementation. For the scientific understanding, smog chamber measurement, intensive field study, and chemical transport model development that can simulate the characteristics of Northeast Asia are considered to be important. For the policy directions, priority setting of the proposed policies and development and implement of effective communication sytem are considered to be important.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.143-160
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• 2017

Since officially assessed land price system was introduced, it has functioned as the criterion for establishing and implementing real estate policies. However, there is a controversial issue about the adequacy of the officially assessed land price system. The problem is that it is difficult to establish a statistical model due to too many land characteristics. Also, local economy, macroeconomic environments and development plans are not reflected in the land price evaluation model. Considering longitudinal and cross-sectional variables, a two-way error component panel model was used in this study. This analysis model includes variables reflecting land characteristics, macroeconomic volatility, and development project. The Paju LCD Industrial Complex was selected as a analysis area and an empirical analysis was performed. According to the analysis, the number of significant land characteristic variables were 14(31%) under 5% significance level. Macroeconomic volatility has had an influence on the land price and year variable reflecting development project has consistently been significant since the industrial complex was designated. Therefore, this study suggests that the land price evaluation model should be improved by simplifying land characteristic variables and including macroeconomic and regional economic variables.