• 한국농공학회논문집
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• 제61권5호
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• pp.69-77
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• 2019

The objective of this study was to calibrate and validate the HSPF (Hydrological Simulation Program-Fortran) model for estimating the runoff of the Hapcheon dam watershed. Spatial data, such as watershed, stream, land use, and a digital elevation map, were used as input data for the HSPF model. Observed runoff data from 2000 to 2016 in study watershed were used for calibration and validation. Hydrologic parameters for runoff calibration were selected based on the user's manual and references, and trial and error method was used for parameter calibration. The $R^2$, RMSE (root-mean-square error), RMAE (relative mean absolute error), and NSE (Nash-Sutcliffe efficiency coefficient) were used to evaluate the model's performance. Calibration and validation results showed that annual mean runoff was within ${\pm}4%$ error. The model performance criteria for calibration and validation showed that $R^2$ was in the rang of 0.78 to 0.83, RMSE was 2.55 to 2.76 mm/day, RMAE was 0.46 to 0.48 mm/day, and NSE was 0.81 to 0.82 for daily runoff. The amount of inflow to Hapcheon Dam was calculated from the calibrated HSPF model and the result was compared with observed inflow, which was -0.9% error. As a result of analyzing the relation between inflow and storage capacity, it was found that as the inflow increases, the storage increases, and when the inflow decreases, the storage also decreases. As a result of correlation between inflow and storage, $R^2$ of the measured inflow and storage was 0.67, and the simulated inflow and storage was 0.61.

• 한국측량학회지
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• 제35권1호
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• pp.1-10
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• 2017

An UAV (Unmanned Aerial Vehicle) is a flight system that is designed to conduct missions without a pilot. Compared to traditional airborne-based photogrammetry, UAV-based photogrammetry is inexpensive and can obtain high-spatial resolution data quickly. In this study, we aimed to classify the land cover using high-spatial resolution images obtained using a UAV. An RGB camera was used to obtain high-spatial resolution orthoimage. For accurate classification, multispectral image about same areas were obtained using a multispectral sensor. A DSM (Digital Surface Model) and a modified NDVI (Normalized Difference Vegetation Index) were generated using images obtained using the RGB camera and multispectral sensor. Pixel-based classification was performed for twelve classes by using the RF (Random Forest) method. The classification accuracy was evaluated based on the error matrix, and it was confirmed that the proposed method effectively classified the area compared to supervised classification using only the RGB image.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.359-363
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• 2008

In this paper, the capacity of MIMO fading channel in the OFDM-based spatial multiplexing systems is analyzed when there is scattering at both transmitter and receiver. The employed MIMO channel model is spanning from the correlated low-rank case to uncorrelated high-rank case at both transmitter and receiver. The effects of spatial fading correlation on the capacity of MIMO channel is examined when the channel is known and unknown at the transmitter. We also evaluate the impacts of a channel estimation error at the transmitter on the MIMO channel capacity.

• 대한공간정보학회지
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• 제24권2호
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• pp.79-87
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• 2016

As 3D geospatial information is demanded, terrestrial laser scanners which can obtain 3D model of objects have been applied in various fields such as Building Information Modeling (BIM), structural analysis, and disaster management. To acquire precise data, performance evaluation of a terrestrial laser scanner must be conducted. While existing 3D surveying equipment like a total station has a standard method for performance evaluation, a terrestrial laser scanner evaluation technique for users is not established. This paper categorizes and analyzes error sources which generally occur in terrestrial laser scanning. In addition to the prior researches about categorizing error sources of terrestrial Laser scanning, this paper evaluates the error sources by the actual field tests for the smooth in-situ applications.The error factors in terrestrial laser scanning are categorized into interior error caused by mechanical errors in a terrestrial laser scanner and exterior errors affected by scanning geometry and target property. Each error sources were evaluated by simulation and actual experiments. The 3D coordinates of observed target can be distortedby the biases in distance and rotation measurement in scanning system. In particular, the exterior factors caused significant geometric errors in observed point cloud. The noise points can be generated by steep incidence angle, mixed-pixel and crosstalk. In using terrestrial laser scanner, elaborate scanning plan and proper post processing are required to obtain valid and accurate 3D spatial information.

• 한국대기환경학회지
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• 제18권6호
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• pp.453-463
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• 2002

Prognostic meteorological model, MM5V3 (Mesoscale Model 5 Version 3) was used to assess the effects of the land-use modifications on spatial variations of temperature and wind fields in Busan during the selected period of summer season in 2000. We first examined sensitivity analysis for temperature between MM5V3 predictions and meteorological data observed at 4 AWS (Automatic Weather System) stations in Busan, which exhibited low structural and accurate errors (Mean Bias Error, MBE: 0.73, Root Mean Square Error, RMSE: 1.18 on maximum). The second part of this paper, MMSV3 simulations for the modification of land-use was performed with 1 km resolution in target domain, 46$\times$46 $\textrm{km}^2$ area around city of Busan. It was found that modification result from change of surface land-use in central urban area altered spatial distributions of temperature and wind. In particular, heat island core moved slightly to the seaward at 1300 LST. This results may imply that modification of surface land-use leads to change the thermal environments; in addition, it has a significant effect on local wind circulations and dispersions of air pollutants.

• 한국지리정보학회지
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• 제12권1호
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• pp.12-25
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• 2009

본 연구는 마산시의 범죄발생 데이터를 이용하여 범죄발생의 특성을 파악하고, 범죄발생의 영향인자를 찾아내어 안전한 도시환경 조성에 도움이 되는 정보를 발견하는데 목적이 있다. 먼저 지리정보시스템을 이용하여 범죄 밀도분석 및 핫스팟(hotspot)분석 등을 통해 지역 범죄 발생패턴을 지도화하고, 공간회귀모델링 등의 공간통계분석을 통해 범죄율과 범죄영향요인간의 관계를 규명하였다. 그 결과 범죄는 생활주기 및 범죄유형에 따라 시계열적으로 일정한 패턴을 가지고 발생할 뿐 아니라, 공간적으로도 인접하여 발생하는 군집적 특성이 있음을 발견할 수 있었다. 모든 범죄유형의 발생에서 공간적 자기상관이 존재하였으며, 강도범죄율이 가장 높은 공간적 자기상관이 있는 것으로 분석되었다. 5대 총범죄율과 절도범죄율의 경우는 노령자인구비, 재산세, 도소매업수, 숙박음식업종수가 범죄율에 유의하고, 폭력범죄의 경우는 인구밀도가 유의미하며 범죄율에 부(-)적 영향을 미치는 것으로 분석되었다. 연구 결과는 범죄로부터 안전한 U-City 구현을 위한 관련 정책수립 등에 기초적인 자료로 활용될 수 있을 것으로 기대된다.

• 한국CDE학회논문집
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• 제2권3호
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• pp.175-185
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• 1997

Given the widespread use of the Finite Element Method in strength analysis, automatic mesh generation is an important component in the computer-aided design of parts and assemblies. For a given resolution of geometric accuracy, the purpose of mesh generators is to discretize the continuous model of a part within this error limit. Sticking to this condition often produces many small elements around small features in spite that these regions are usually of little interest and computer resources are thus wasted. Therefore, it is desirable to selectively suppress small features from the model before discretization. This can be achieved by low-pass filtering a CAD model. A spatial function of one dimension higher than the model of interest is represented using the Fourier basis functions and the region where the function yields a value greater than a prescribed value is considered as the extent of a shape. Subsequently, the spatial function is low-pass filtered, yielding a shape without the small features. As an undesirable effect to this operation, all sharp corners are rounded. Preservation of sharp corners is important since stress concentrations might occur there. This is why the LPF (low-pass filtered) model can not be directly used. Instead, the distances of the boundary elements of the original shape from the LPF model are calculated and those that are far from the LPF model are identified and removed. It is shown that the number of mesh elements generated on the simplified model is much less than that of the original model.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• 한국측량학회지
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• 제38권3호
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• pp.281-293
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• 2020

최근 우리나라는 크고 작은 화재가 지속해서 발생하고 있다. 화재는 우리나라의 도시 내에서 교통사고와 더불어 가장 많이 발생하는 재해 중 하나이며, 화재 발생 빈도는 토지이용의 형태와 시설물의 유형에 따라 밀접한 상관성을 갖고 있다. 따라서 본 연구에서는 진주시를 대상으로 10년간 화재데이터를 사용하여 토지용도별, 시설물 유형별 그리고 인문 사회적 요인을 고려하여 화재 발생의 유의성을 분석하였다. 먼저 진주시 화재 발생의 공간분포 패턴을 파악한 후, 다중 회귀분석을 통해 인문·사회 및 물리적 요인 간의 공간적 종속성 및 비정상성을 확인하였다. 이를 토대로 화재 발생 위치와 각 요인의 위치를 고려하여 공간가중치를 활용한 선형회귀모형, 공간시차모형 그리고 공간오차모형을 비교 분석하였으며 적합도가 높은 통계모형을 제시하였다. 그 결과 진주시 화재 발생의 공간분포 패턴을 확인하기 위해 LISA분석을 실시하였으며 중심상업지역, 공업지역, 주거지역 순으로 화재 발생 빈도가 높은 것으로 나타났고, 인구·사회 및 물리적 변수를 통합하여 다중회귀분석을 실시하였다. 이에 따라 최종 도출된 요인들을 중심으로 공간가중치를 적용하여 세 모형을 비교 분석하였으며 유의성 검정을 실시한 결과 공간오차모형이 가장 유의한 것으로 분석되었다. 화재 발생과 가장 높은 상관성이 있는 시설은 제2종 근린생활시설로 나타났으며 다음으로 단독주택, 제1종 근린생활시설, 가구 수, 판매시설의 순으로 분석되었다. 또한, 표준편차 타원체분석을 통하여 용도지역 중 주거지역, 공업지역, 중심상업지역을 중심으로 시설물별 분포특성을 분석한 결과 주거지역 및 공업지역에서는 네 개 시설물의 특성이 비슷하게 나타났으나 중심상업지역에서는 화재위험도가 가장 높은 제2종 근린생활시설이 중심부에 집중분포하였다. 이러한 연구 결과는 도시지역에서 발생하는 화재에 대해 시설물별 특성을 파악하여 화재안전관리를 하는데 유용한 자료로 활용될 것으로 예상된다.