• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.93-109
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• 2018

The Ministry of Strategy and Finance of South Korea accesses public organizations annually for increasing outcomes with effective and clear managements. The management assessment is categorized into the business management applied all organizations and the main business assessed by purposes and characters of organizations. This study analyzes metrical indexes of main businesses with using the logic model and suggests the direction of developing metrical indexes of organizations. Metrical indexes of the Korea Land and Geospatial Informatix Corporation in 2017 were analyzed to develop indexes. The results indicates that metrical indexes of the Korea Land and Geospatial Informatix Corporation are mainly consisted of the commitment and process. The logic model was suggested to analyze proprieties of metrical indexes of the main business. This study shows that organizations develop metrical indexes of the main business with classifying that these are from calculation indexes or result indexes. Thus, this study suggests that organizations consider result indexes based on the logic model for developing metrical indexes of the main business.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.4
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• pp.348-356
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• 2014

To understand the relationship between road-traffic noise and urban components such as population, building, road-traffic and land-use, the city of Cheongju that already has road-traffic noise maps of daytime and nighttime was selected for this study. The whole area of the city is divided into square cells of a uniform size and for each cell, the urban components are estimated. A spatial representative noise level for each cell is determined by averaging out population-weighted facade noise levels for noise exposure population within the cell during nighttime. The relationship between the representative noise level and the urban components is statistically modeled at the cell level. Specially, we introduce a spatial auto regressive model and a spatial error model that turns out to explain above 85 % of the noise level. These findings and modeling methods can be used as a preliminary tool for environmental planning and urban design in modern cities in consideration of noise exposure.

• Information Systems Review
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• v.12 no.2
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• pp.167-187
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• 2010

The governmental R&D projects have put a great emphasis on returns on R&D investment through development of market- and customer-oriented technologies and their commercialization. A successful commercialization of the technology developed from a R&D project relies on development of the technology intriguing customers, establishment of effective commercialization strategies throughout the stages of R&D planning and development, and interactive communication between the technology developer and a private company that will do a business with the technology. The business modeling helps accomplish the requirements for the successful commercialization. The past literature about technology commercialization does not provide a systematic procedure in developing a business model for the governmental R&D project. In this study, we proposed a systematic procedure developing a business model integrating peculiar characteristics of governmental R&D and viewpoints of technology developers and professionals of technology commercialization. It can be a useful guideline for developing a business model for the governmental R&D project.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2431-2449
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• 2024

As problems such as water pollution and fish species depletion have become serious, a land-based fish farming is receiving a great attention for ensuring stable productivity. In the fish farming, it is important to determine the timing of shipments, as one of key factors to increase net profit on the aquaculture. In this paper, we propose a system for predicting net profit to support decision of timing of shipment using fish farming-related statistical data. The prediction system consists of growth and farm-gate price prediction models, a cost statistics table, and a net profit estimation algorithm. The Gaussian process regression (GPR) model is exploited for weight prediction based on the analysis that represents the characteristics of the weight data of cultured fish under the assumption of Gaussian probability processes. Moreover, the long short-term memory (LSTM) model is applied considering the simple time series characteristics of the farm-gate price data. In the case of GPR model, it allows to cope with data missing problem of the weight data collected from the fish farm in the time and temperature domains. To solve the problem that the data acquired from the fish farm is aperiodic and small in amount, we generate the corresponding data by adopting a data augmentation method based on the Gaussian model. Finally, the estimation method for net profit is proposed by concatenating weight, price, and cost predictions. The performance of the proposed system is analyzed by applying the system to the Korean flounder data.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.127-142
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• 2017

The purpose of this study is to examine the status of urbanization of Namyangju-Si from 2001 till 2008, analyze the correlation of the change of surrounding land use according to the change of accessibility and analyze the correlation of the change of land use. Based on the result from correlation analysis, I simulated from the view point of interactions between agents of land use and transportation accessibility by using MAS. I tried to forecast, based on space, the change of surrounding land use caused by the change of accessibility. I conducted landscape analysis by using a land registration map. Also, I conducted GIS analysis statistical analysis simulation analysis of traffic data, land use data in order to analyze the correlation of the change of surrounding land use according to the change of accessibility. I could find out a certain pattern that new roads and railroads causes the within 500m adjacent land to change into urban land use, which led me to construct a system through which land use changing phenomenon can be expected according to the change of accessibility. It is expected that it can make the best use of selecting the location where new transportation facilities are constructed.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.463-471
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• 2008

The quality of orthoimages mainly depends on the elevation information and exterior orientation (EO) parameters. Since LiDAR data directly provides the elevation information over the earth's surface including buildings and trees, the concept of true orthorectification has been rapidly developed and implemented. If a LiDAR-driven digital surface model (DSM) is used for orthorectification, the displacements caused by trees and buildings are effectively removed when compared with the conventional orthoimages processed with a digital elevation model (DEM). This study utilized LiDAR data to generate orthorectified digital aerial images. Experimental orthoimages were produced using digital terrain model (DTM) and DSM. For the preparation of orthorectification, EO components, one of the inputs for orthorectification, were adjusted with the ground control points (GCPs) collected from the LiDAR point data, and the ground points were extracted by a filtering method used in a previous research. The orthoimage generated by DSM corresponded more closely to non-ground LiDAR points than the orthoimage produced by DTM.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.11-19
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• 2014

This study is analyze future climate and land cover change affects behaviors for amount of streamflow and sediment discharge within basin. We used the climate forecast data in RCP 4.5 and 8.5 (2011-2100) which is opposite view for each other among RCP scenarios that are discussed for 5th report for IPCC. Land cover map built based on a social economic storyline in RCP 4.5/8.5 using Logistic Regression model. In this study we set three scenarios: one scenario for climate change only, one for land cover change only, one for Last both climate change and land cover change. It simulated amount of streamflow and sediment discharge and the result showed a very definite change in the seasonal variation both of them. For climate change, spring and winter increased the amount of streamflow while summer and fall decreased them. Sediment showed the same pattern of change steamflow. Land cover change increases the amount of streamflow while it decreases the amount of sediment discharge, which is believed to be caused by increase of impervious Surface due to urbanization. Although land cover change less affects the amount of streamflow than climate change, it may maximize problems related to the amount of streamflow caused by climate change. Therefore, it's required to address potential influence from climate change for effective water resource management and prepare suitable measurement for water resource.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.71-80
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• 2011

A simple and an improved methods for the assessment of flood damage were used in previous studies, and the Multi-Dimensional Flood Damage Assessment (MD-FDA) has been applied since 2004 in Korea. This study evaluated flood damage of dam downstream using considering MD-FDA method based on GIS data. Firstly, flood water level with FLDWAV (Flood Wave routing) model was input into cross section layer based on enforcement drainage algorithm, water depth grid data were created through spatial calculation with DEM data. The value of asset of building and agricultural land according to local government was evaluated using building layer from digital map and agricultural land map from landcover map. Also, itemized flood damage was calculated by unit price to building shape, evaluated value of housewares to urban type, unit cost to crop, tangible and inventory asset of company connected with building, agricultural land, flooding depth layer. Flood damage in rainfall frequency of 200 year showed 1.19, 1.30 and 1.96 times to flood damage in rainfall frequency of 100 year, 50 year and 10 year respectively by flood damage analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.5
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• pp.67-77
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• 2007

The images of MODerate resolution Imaging Spectroradiometer (MODIS) that provide wider swath and shorter revisit frequency than Land Satellite (Landsat) and Satellite Pour I' Observation de la Terre (SPOT) has been used fer land cover classification with better spatial resolution than National Oceanic and Atmosphere Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR)'s images. Due to the advantages of MODIS, several researches have conducted, however the results for the land cover classification using MODIS images have less accuracy of classification in small areas because of low spatial resolution. In this study, uncertainty of paddy fields classification using MODIS images was conducted in the region of Gyeonggi-do and the relation between this uncertainty of estimating paddy fields and topographical factors was also explained. The accuracy of classified paddy fields was compared with the land cover map of Environmental Geographic Information System (EGIS) in 2001 classified using Landsat images. Uncertainty of paddy fields classification was analyzed about the elevation and slope from the 30m resolution Digital Elevation Model (DEM) provided in EGIS. As a result of paddy classification, user's accuracy was about 41.5% and producer's accuracy was 57.6%. About 59% extracted paddy fields represented over 50 uncertainty in one hundred scale and about 18% extracted paddy fields showed 100 uncertainty. It is considered that several land covers mixed in a MODIS pixel influenced on extracted results and most classified paddy fields were distributed through elevation I, II and slope A region.