• Journal of Wetlands Research
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• v.14 no.4
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• pp.657-665
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• 2012

The development projects distort the natural water circulation system and increase the non-point source pollution by changing the natural cover type. The low impact development (LID) techniques are considering as new development approach to decrease the ecological- and hydrological impacts from high imperviousness rate. The high imperviousness rate is because of the construction of building, parking lot and road for human activities. Knowing the basic characteristics of rood runoff can give the direction for setting up the water management strategy. The monitoring results show the pollutant EMCs of roof runoff are 3~13 times lower than EMCs of the road and parking lot. The pollutant sources from roof runoff are mainly from leafs, cigarette butts, atmospheric deposition and materials of the roof. The EMC is occurred around 15minutes later after starting runoff and more than 8 storm events are needed to have the average EMCs.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.162-167
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• 2007

수질오염총량관리제에서 단위유역 할당부하량은 지자체의 개발용량과 밀접한 관계를 가지고 있기 때문에 상 하류 지역간의 첨예한 관심거리가 되고 있다. 총량관리제는 기준유량과 목표수질에 대한 기준배출부하량의 달성을 목적으로 하고 있기 때문에 합리적이고 과학적인 기준유량 및 목표수질의 설정이 무엇보다 중요하다. 또한 합리적인 수질모델링을 필요로 하는데, 유량배분은 모델링 과정에서 중요한 영향을 미치며, 지역의 기준배출부하량을 결정하는 결정적인 요소 중의 하나이다. 기존의 유량배분은 대부분 관측지점을 기준으로 한 단순한 면적비 유량배분기법(SAWA; simple area-based water-balance analysis)에 의존해왔다. 그러나 SAWA는 특정유역의 토지피복, 토양, 지형경사 및 강우분포 등의 수문학적 특성을 고려하지 못하는 한계점을 가지고 있다. 즉, 동일한 면적의 유역이라도 이러한 수문 특성인자에 따라 유출되는 유량이 달라지는 현상을 고려하지 못하고 있다. 이는 곧 지역의 기준배출부하량의 신뢰성에 영향을 미치기 때문에 지역간 분쟁의 소지가 될 수 있다. 본 연구는 기존의 유량배분 방법인 SAWA가 가지는 한계점을 극복하고자 강우분포 및 토지피복의 수문학적 특성을 이용한 유량배분기법(HIWA; hydro-infomatical water-balance analysis)의 개발을 목적으로 수행되었다. 강우분포와 토지피복이 하천유량에 미치는 영향을 분석하고 공간정보화 한 후 지형정보체계(GIS)의 수문분석 기법을 이용하여 유량을 배분하였다 ARC/INFO의 KRIGING 보간법을 이용하여 구축한 등강우분포도와 토지피복에 따른 유출특성을 분석하여 강우유출 해석을 위한 가중지형정보를 생성하였다. 연구는 2003년 10월-2004년 3월의 미호천수계 및 수질오염총량관리단위유역 말단지점의 실측자료를 이용하였으며, 연구결과 기존의 SAWA보다 본 연구에서 제안한 HIWA가 유량배분의 정확도를 높일 수 있음이 입증되었다.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.207-224
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• 2015

The purpose of this study is to review the various valuation techniques of intangible assets. The value of intangible asset by the income approach can be measured as the present value of the economic benefit over the intangible asset's remaining useful life. The typical methods used in intangible asset economic income projections include extrapolation method, life cycle analyses, sensitivity analyses, simulation analyses, judgment method, and tabula rasa method. There are several methods available for estimating capitalization rates and discount rates for intangible asset, in which we have discussed market extraction method, capital asset pricing model, built-up method, discounted cash flow model, and weighted average cost of capital method. As the capitalization methods for intangible asset, relief-from-royalty method, excess earnings capitalization method, profit split method, residual from business enterprise method, postulated loss of income method and so on have been reviewed.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.11
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• pp.9-16
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• 2011

We need to establish standard for the ICT based on train control system. In order to solve the ISI problem, this paper evaluate the performance of OFDM and FDE system. We seem that OFDM system is better than FDE system. In order to solve ISI problem, SC System is needed a equalizer. And another method is OFDM System. If system is used SC with a equalizer, It is better than OFDM in terms of PAPR, but this system is not easy to use Multi-Antenna technique, i.e., beam-forming and MIMO-multiplexing. And If system is used high-order modulation, BER performance is worse than OFDM. If we think about in terms of PAPR problem, considerations are considered not significant because the size of relays is not considered in the communication between trains and ground.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.777-783
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• 2008

Nowadays wavelet transforms are widely used for the analyses of PIV velocity vector fields. This is bemuse the wavelet provides not only spatial information of the velocity vectors but also of time and frequency domains. In this study, a discrete wavelet trC1f1$form has been applied to real PIV images of bubbly flows. The vector fields obtained by a self-made cross-correlation PIV algorithm were used for the discrete wavelet transform The performances of the discrete wavelet transform is investigated by changing the level of power of discretization. The decomposed images by the wavelet multiresolution showed conspicuous characteristics of the bubbly flows according to the level changes. The high spatial bubble concentrated area could be evaluated by the constructed discrete wavelet transform algorithm, at which high leveled wavelets could play a dominant roles to reveal the flow characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.98-98
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• 2021

동역학파 기반 순간단위도 (Dynamic wave-based Instantaneous Unit Hydrograph)를 이용하여 유역에서의 강우에 의한 유출을 예측하는 기법을 개발하였으며, 국내 실제 자연 유역에 적용하여 기법의 타당성과 적용성을 검증하였다. 본 연구에서 제시한 '동역학파 기반 순간단위도 방법'은 물리기반 수치모형인 동역학파 강우유출모형과 개념적 순간단위도 방법을 결합하여 사용함으로써 물리적으로 정확하면서도 빠르고 안정적으로 강우-유출을 예측하는 것을 목적으로 한다. 유역의 순간단위도는 유역의 지형, 조도계수와 동역학파 강우유출모형인 tRIBS-OFM을 이용하여 계산된 S-수문곡선을 수치적으로 미분함으로써 유도되며, 유도된 순간단위도는 강우강도에 따라 변화하므로 회선적분을 통한 유출수문곡선 예측 시 강우-유출 관계의 비선형성을 고려할 수 있다. 본 연구에서 유도된 순간단위도의 첨두 값과 첨두 발생시간은 강우강도 값과 각각 양과 음의 상관관계를 가졌으며 강우강도 값과 멱 함수 (power function)의 관계를 가졌다. 이는 Paik and Kumar (2004) 등 기존 연구들에서 밝힌 순간단위도의 특성과 일치하였으며, 본 연구에서는 더 나아가 멱함수의 지수를 산정한 후 임의의 강우강도 값에 대응하는 순간단위도를 멱함수 관계를 이용하여 보간할 수 있는 방법을 제시하였다. 실제 유역에 대한 적용은 강원도 인제군에 위치한 내린천 유역을 대상으로 수행하였다. 유역을 여러 개의 소유역으로 분할하여 강우의 공간적 분포를 고려하였으며, 각 소유역에서의 유출량을 동역학파 기반 순간단위도를 이용해 계산한 뒤 물리기반의 하도추적모형을 이용하여 전체 유역에서의 유출수문곡선을 예측했다. 예측된 유출수문곡선을 관측 유출 자료와 비교해본 결과 NSE (Nash-Sutcliffe model efficiency coefficient)가 0.6 이상으로 측정되어 적절히 유출을 예측한 것으로 판단되었다.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of the Korean Institute of Landscape Architecture
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• v.15 no.1
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• pp.39-67
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• 1987

The purpose of this thesis is to suggest objective basic data for the environmental design through the quantitative analysis of the visual quality included in the physical environment of forest landscape. For this, landscape values of forest landscape have been evaluated by using the Iverson method, the images structure of forest landscape's main utilizing space have been analysed by the factor analysis algorithm, degree of visual preferences have been pleasured mainly by questionnaries and SBE method, and finally these thesis can be summarized as fallow LCP with high values of Iverson factors I and IV yield high landscape value. Specifically, Iverson factor IV has been found to play the dominant. For all experimental points, significant seasonal variations in S.D. scale values have been observed. In natural parks, where artificial structures are complementary to the natural landscape, main factors of image are S.D. scales such as the visual sequence, the formal simplicity of structures, the emphasis, the unification of heterogeneous factors and the assimilation. Factors covering the spatial image of natural parks have been found to be the overall evaluation, the individual characteristics, the tidiness, the potentiality, the dignity, the intimacy and the space volume. For all seasons, factors such as the individual characteristics, the dignity, the tidiness, the potentiality, yield high factor scores. As for factors determining the degree of visual preference, variables such as the summit, the skyline, rocks, the water and the degree of natural destruction by artificial structures yield high values for all seasons.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1249-1258
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• 2022

Marine debris is defined as a substance that is intentionally or inadvertently left on the shore or is introduced or discharged into the ocean, which has or is likely to have a harmful effect on the marine environments. In this study, the detection of marine debris and the analysis of the amount of change on marine debris were performed using the object detection method for an efficient method of identifying the quantity of marine debris and analyzing the amount of change. The study area is Yuho Mongdol Beach in the northeastern part of Geoje Island, and the amount of change was analyzed through images collected at 15-minute intervals for 32 days from September 12 to October 14, 2022. Marine debris detection using YOLOv5x, a one-stage object detection model, derived the performance of plastic bottles mAP 0.869 and styrofoam buoys mAP 0.862. As a result, marine debris showed a large decrease at 8-day intervals, and it was found that the quantity of Styrofoam buoys was about three times larger and the range of change was also larger.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.