• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.201-214
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• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.5
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• pp.31-41
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• 2010

This research aims to present a methodological approach for repurposing small pockets of national/public lands, which can be constructed as parks, through an investigation of the present status of these areas of national/public lands that are scattered around Busan Megalopolis as well as the suitability of their construction. In order to attain this, this study looked at the present status of these small areas of national/public lands by utilizing a national land, city land list (lot number), land registration map and satellite image of Busan Megalopolis, and evaluating their suitability as parks through GIS analysis and classification. As a result, these small areas of lands with the potential to be turned into parks include 516 spots($375,934m^2$). Geographically, 39% of these areas are located on flat land and are the most scattered. 260 places met the requirements for optimal placement for conversion, while convenience included 305 places, and availability 267 places. The most optimal of the places meeting such standards include 61 spots. The characteristics of these areas of national/public lands include being below $500m^2$, with flatlands and open areas above a 5' grade occupy the highest ratio, accounting for 25.4% of the land studied. These results have offered a methodology for a GIS DB, which can visualize the data for a positive utilization be yond the simple level of the maintenance/preservation of national/public lands and provide basic data for the utilization and management of these types of areas in the future.

• Journal of the Korean Recycled Construction Resources Institute
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• v.8 no.2
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• pp.227-234
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• 2020

Under consideration of construction waste recycling, candidate secondary products applicable for the civil and construction areas can be reliably employed based on the recycling cost analysis. For the validation purpose, probability-based cost analyses were performed to estimate recycling cost profit considering uncertainties. When recycling construction wastes, the costs at each stage are fully dependent on target products to be adopted. To achieve commercialization of each product, its quality has to be improved with economic efficiency through accurate evaluation of input costs. Based on the probabilistic recycling cost analysis, the cost benefit for target products was estimated with waste classification cost, transportation cost to recycling treatment facilities and production cost. All necessary information on the cost analysis were collected from literature, disclosure, and existing recycling companies. In addition, a cost difference between recycled and non-recycled events was made. As a result, a probability-based recycling cost estimate for candidate secondary products was herein presented.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.969-982
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• 2008

As hydrological models have been progressively developed, they are recognized as appropriate tools to manage water resources. Especially, the need to evaluate the effects of landuse and climate change on hydrological phenomena has been increased, which requires powerful validation methods for the hydrological models to be employed. As measured streamflow data at many locations may not be available, or include significant errors in application of hydrological models, streamflow data simulated by models only might be used to conduct hydrological analysis. In many cases, reducing errors in model simulations requires a powerful model validation method. In this research, we demonstrated a validation methodology of SWAT model using observed flow in two basins with different physical characteristics. First, we selected two basins, Gap-cheon basin and Yongdam basin located in the Guem River Basin, showing different hydrological characteristics. Next, the methodology developed to estimate parameter values for the Gap-cheon basin was applied for estimating those for the Yongdam basin without calibration a priori, and sought for validation of the SWAT. Application result with SWAT for Yongdam basin showed $R_{eff}$ ranging from 0.49 to 0.85, and $R^{2}$ from 0.49 to 0.84. As well, comparison of predicted flow and measured flow in each subbasin showed reasonable agreement. Furthermore, the model reproduced the whole trends of measured total flow and low flow, though peak flows were rather underestimated. The results of this study suggest that SWAT can be applied for predicting effects of future climate and landuse changes on flow variability in river basins. However, additional studies are recommended to further verify the validity of the mixed method in other river basins.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.19-24
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• 2013

To assess on the biological control potential with the native fishes, Aphyocypris chinensis, Oryzias sinensis, Misgurnus mizolepis, Pseudorasbora parva, Carassius auratus, Odontobutis interrupta were tested in relation to optimal condition controling mosquitoes in Seosan region. As a result of larvivorous activity for predation rate and temperature difference along kinds of native fishes, predation rate of Aphyocypris chinensis was the highest and predation rate of Misgurnus mizolepis was the lowest under the $23^{\circ}C$. Contrastively, predation rate of Aphyocypris chinensis and Odontobutis interrupta was the highest in least time. Predation rate of Misgurnus mizolepis was the lowest under the all temperature among six species fishes. Aphyocypris chinensis and Pseudorasbora parva in low, Carassius auratus, Misgurnus mizolepis and Odontobutis interrupta in bottom appeared high predation rate along the water depth. And predation rate of Oryzias sinensis was high as 95% at surface. As a result of measuring time taken to predate 50 mosquito larvae by 3 species fishes, two Aphyocypris chinensis took 10minutes, two Oryzias sinensis took 21min., two Misgurnus mizolepis took 45min. But one Aphyocypris chinensis and Misgurnus mizolepis took 8min., one Misgurnus mizolepis and Oryzias sinensis took 17min.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.945-952
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• 2018

Recently, bass fishing has become a marine leisure sport in Korea. There are 4 major fishing associations in Korea, and each association holds 10-15 tournaments each year. However, supply of 17 ft bass boats, which are preferred in leagues, depends 100 % on imports. In this study, we have derived the main specifications to develop the initial hull forms of a 18.5ft bass boat through statistical analysis based on mothership data. In addition, CFD numerical analysis was carried out according to deadrise angle and longitudinal center of gravity, which strongly influenced the resistance and planing performance. For numerical analysis, design speed was set to $Fn=3.284 (Re=9.858{\times}10^7)$, the deadrise angle was set from 12 to $20^{\circ}$, and the longitudinal center of gravity was set in the range of 0 to $8%L_{wL}$ from the center of buoyancy to the stern. Based on the numerical results, we first set the range of these factors by resistance performance and immersion keel length. Furthermore, using a correlation graph of Savitsky's Drag-Lift ratio, we derived the deadrise angle ($14-16^{\circ}$) and longitudinal center of gravity ($4-6%L_{wL}$).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1367-1377
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• 2020

The purpose of this study is to assess the seismic vulnerability of buildings in Gyeongju city starting with the earthquake that occurred in the city on September 12, 2016, and produce a seismic vulnerability map. 11 influence factors related to geotechnical, physical, and structural indicators were selected to assess the seismic vulnerability, and these were applied as independent variables. For a dependent variable, location data of the buildings that were actually damaged in the 9.12 Gyeongju Earthquake was used. The assessment model was constructed based on random forest (RF) as a mechanic study method and support vector machine (SVM), and the training and test dataset were randomly selected with a ratio of 70:30. For accuracy verification, the receiver operating characteristic (ROC) curve was used to select an optimum model, and the accuracy of each model appeared to be 1.000 for RF and 0.998 for SVM, respectively. In addition, the prediction accuracy was shown as 0.947 and 0.926 for RF and SVM, respectively. The prediction values of the entire buildings in Gyeongju were derived on the basis of the RF model, and these were graded and used to produce the seismic vulnerability map. As a result of reviewing the distribution of building classes as an administrative unit, Hwangnam, Wolseong, Seondo, and Naenam turned out to be highly vulnerable regions, and Yangbuk, Gangdong, Yangnam, and Gampo turned out to be relatively safer regions.

• Journal of The Geomorphological Association of Korea
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• v.17 no.2
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• pp.87-98
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• 2010

There have been growing concerns for the sea level rise due to global warming in recent years. Sea level rise is a serious problem to densely populated coastal areas, because it may affect the coastal landforms to be damaged. Especially coastal sand deposits like coastal dunes are more sensitive than the other coastal landforms. In this paper, Ground Penetrating Radar (GPR) and Optically Stimulated Luminescence (OSL) dating method were used to identify the Holocene geomorphic changes of coastal dune field in Shinduri located at the western coast. The main results in this study that are the dunefield in the study area may have begun to form at around 6.8 ka and it has grown seaward thereafter. Then, dunefield appears to have extensively developed since 3.7 ka. This result, together with previous works on the sea level and climatic changes in the western coast of Korea suggest that the dunefield has been affected by the sea level regression since the Holocene high stand in the Holocene at around 6 ka and climatic change from warm and humid to cold and dry conditions occurred at 4.5 ka.