• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.143-155
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• 2016

Recently, a massive loss of life and property is occurring in Korea due to traffic accidents, with the rapid increase in cars. For improvement of traffic safety, the Korea Transportation Safety Authority intensively analyzes accident data in local governments with low traffic safety index, performs a field investigation to extract problems and offers local governments improvements for problems, by conducting the 'Special Survey of Actual Conditions of Traffic Safety' each year, starting 2008. But local governments cannot strongly push forward the improvement projects due to the limited budget and the uncertainty of the improvement plan effects. Therefore, this study suggested a model which applied the Utility concept to the AHP theory, in order to efficiently decide a priority of the improvement plans in accident black spots in consideration of the limited budget of local governments. The number of accidents in each spot for improvement and accident severity, traffic volume, pedestrian volume, the improvement project cost and the accident reduction effect were chosen as evaluation factors for deciding a priority, and data about the improvement plan costs and the accident reduction effects, traffic accidents and traffic volume in the spots to undergo the special research on the real condition of traffic accident in the past were collected from the existing studies. Then, regression analysis was carried out and the Utility Curve of each evaluation factor was computed. Based on the AHP analysis findings, this study devised a priority decision method which calculated the weight and the utility function of each evaluation factor and compared the total utility values. The AHP analysis findings showed that among the evaluation factors, accident severity had the biggest importance and it was followed by the improvement plan cost, the number of accidents, the improvement effect, traffic volume and pedestrian volume. The calculated utility function shows a rise in utility, as the variables of the 5 evaluation factors; the number of accidents, accident severity, the improvement plan effect, traffic volume and pedestrian volume increase and a fall in utility, as the variables of the improvement plan cost increase, since the improvement plan cost is included in the budget spent by a local government.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.221-232
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• 2009

The geographical distribution of freeze risk determines the latitudinal and altitudinal limits and the maximum acreage suitable for fruit production. Any changes in its pattern can affect the policy for climate change adaptation in fruit industry. High-definition digital maps for such applications are not available yet due to uncertainty in the combined responses of temperature and dormancy depth under the future climate scenarios. We applied an empirical freeze risk index, which was derived from the combination of the dormancy depth and threshold temperature inducing freeze damage to dormant buds of 'Changhowon Hwangdo' peach trees, to the high-definition digital climate maps prepared for the current (1971-2000), the near future (2011-2040) and the far future (2071-2100) climate scenarios. According to the geospatial analysis at a landscape scale, both the safe and risky areas will be expanded in the future and some of the major peach cultivation areas may encounter difficulty in safe overwintering due to weakening cold tolerance resulting from insufficient chilling. Our test of this method for the two counties representing the major peach cultivation areas in South Korea demonstrated that the migration of risky areas could be detected at a sub-grid scale. The method presented in this study can contribute significantly to climate change adaptation planning in agriculture as a decision aids tool.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.769-779
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• 2017

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.104-112
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• 2019

This study has empirically analyzed how far corporate governance and CSR(Corporate Social Responsibility) fit are related based on prior research indicating that corporate governance is one of the primary factors. Previous research suggested that there may be different types of CSR fit, but there have been only limited number of empirical studies. This study filled this gap by categorizing CSR fit into three types (functional fit, target fit, and size fit) and investigating whether different types have different effects. We used data from the Corporate Social Responsibility White Paper for the 2009-2012 period, as well as the Korea Corporate Governance Service (KCGS) index. As a result, we found that there is a negative (-) relationship between corporate governance and CSR fit(${\beta}=-.023$, p<.05). This can be interpreted that companies with weak corporate governance are attempting to increase the trust level of stakeholders and to reduce the uncertainty of CSR through high-CSR-fitted programs. The test results showed that functional fit and target fit both had negative (-) relationships with corporate governance (${\beta}=-.021$, p<.05; ${\beta}=-.016$, p<.1), while size fit did not have a significant correlation with corporate governance (${\beta}=-.005$, p=.511). The results of this study supported the previous studies' suggestions that CSR fit has different effects on each type, indicating a need for further reflection on the relationship between corporate governance and CSR fit. Also, the results of this study showed that corporations should take a strategic approach to operating CSR fit.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.35-47
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• 2022

Expansion of potato production areas can improve the food security in North Korea because the given crop has less requirements for agricultural materials and facilities. The Global Agro-Ecological Zones (GAEZ) model, which was developed to evaluate climate suitability under different cultivation conditions, was used to identify potential areas for the potato production. The spatial estimates of crop suitability under low and high input management conditions were downloaded from the GAEZ data portal. The values of suitability were obtained at the potato occurrence sites retrieved from the Global Biodiversity Information Facility (GBIF) database. The suitable areas for the potato production were identified using a threshold value derived from the suitability estimates at the occurrence sites. It was found that 90% of the occurrence sites had the suitability index value >3,333, which was set to be the threshold value. The suitable areas in North Korea were summarized by province and county. Rice cultivation areas were excluded from the analysis. The reported relative acreage of potato production was better represented by the suitable areas under the low input management options than the high input conditions. The suitable areas also had a similar distribution to the reported acreage of potato production by county. These results indicated that the GAEZ model would be useful to identify the candidate production areas, which would facilitate the increases in potato production especially under future climate conditions. Furthermore, monthly maps of crop suitability can be used to design cropping systems that would improve crop production under the limited use of agricultural materials and facilities.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.161-170
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• 2010

For the development of load and resistance factor design, reliability analysis is required to calibrate resistance factors in the framework of reliability theory. The distribution of measured-to-predicted pile resistance ratio was obrained based on only the results of load tests conducted to failure for the assessment of uncertainty regarding pile resistance and used in the conventional reliability analysis. In other words, successful pile load test (piles resisted twice their design loads without failure) results were discarded, and therefore, were not reflected in the reliability analysis. In this paper, a new systematic method based on Bayesian theory is used to update reliability indices of driven steel pipe piles by adding more proof pile load test results, even not conducted to failure, to the prior distribution of pile resistance ratio. Fifty seven static pile load tests performed to failure in Korea were compiled for the construction of prior distribution of pile resistance ratio. The empirical method proposed by Meyerhof is used to calculate the predicted pile resistance. Reliability analyses were performed using the updated distribution of pile resistance ratio. The challenge of this study is that the distribution updates of pile resistance ratio are possible using the load test results even not conducted to failure, and that Bayesian updates are most effective when limited data are available for reliability analysis.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Journal of The Korean Society of Grassland and Forage Science
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• v.44 no.2
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• pp.71-82
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• 2024

It would be advantageous to grow legume forage crops in order to increase the productivity and sustainability of sloped croplands in Hamkyongbukdo. In particular, the identification of potential cultivation areas for alfalfa in the given region could aid decision-making on policies and management related to forage crop production in the future. This study aimed to analyze the climate suitability of alfalfa in Hamkyongbukdo under current and future climate conditions using the Fuzzy Union model. The climate suitability predicted by the Fuzzy Union model was compared with the actual alfalfa cultivation area in the northern United States. Climate data obtained from 11 global climate models were used as input data for calculation of climate suitability in the study region to examine the uncertainty of projections under future climate conditions. The area where the climate suitability index was greater than a threshold value (22.6) explained about 44% of the variation in actual alfalfa cultivation areas by state in the northern United States. The climatic suitability of alfalfa was projected to decrease in most areas of Hamkyongbukdo under future climate scenarios. The climatic suitability in Onseong and Gyeongwon County was analyzed to be over 88 in the current climate conditions. However, it was projected to decrease by about 66% in the given areas by the 2090s. Our study illustrated that the impact of climate change on suitable cultivation areas was highly variable when different climate data were used as inputs to the Fuzzy Union model. Still, the ensemble of the climate suitability projections for alfalfa was projected to decrease considerably due to summer depression in Hamkyongbukdo. It would be advantageous to predict suitable cultivation areas by adding soil conditions or to predict the climate suitability of other leguminous crops such as hairy vetch, which merits further studies.

• Progress in Medical Physics
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• v.21 no.4
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• pp.332-339
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• 2010

We aimed to setup an adaptive radiation therapy platform using cone-beam CT (CBCT) and multileaf collimator (MLC) log data and also intended to analyze a trend of dose calculation errors during the procedure based on a phantom study. We took CT and CBCT images of Catphan-600 (The Phantom Laboratory, USA) phantom, and made a simple step-and-shoot intensity-modulated radiation therapy (IMRT) plan based on the CT. Original plan doses were recalculated based on the CT ($CT_{plan}$) and the CBCT ($CBCT_{plan}$). Delivered monitor unit weights and leaves-positions during beam delivery for each MLC segment were extracted from the MLC log data then we reconstructed delivered doses based on the CT ($CT_{recon}$) and CBCT ($CBCT_{recon}$) respectively using the extracted information. Dose calculation errors were evaluated by two-dimensional dose discrepancies ($CT_{plan}$ was the benchmark), gamma index and dose-volume histograms (DVHs). From the dose differences and DVHs, it was estimated that the delivered dose was slightly greater than the planned dose; however, it was insignificant. Gamma index result showed that dose calculation error on CBCT using planned or reconstructed data were relatively greater than CT based calculation. In addition, there were significant discrepancies on the edge of each beam while those were less than errors due to inconsistency of CT and CBCT. $CBCT_{recon}$ showed coupled effects of above two kinds of errors; however, total error was decreased even though overall uncertainty for the evaluation of delivered dose on the CBCT was increased. Therefore, it is necessary to evaluate dose calculation errors separately as a setup error, dose calculation error due to CBCT image quality and reconstructed dose error which is actually what we want to know.