• Atmosphere
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• v.25 no.2
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• pp.271-281
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• 2015

The objectives of this study are to develop a hydrological drought outlook system using GloSea5 (Global Seasonal forecasting system 5) which has recently been used by KMA (Korea Meteorological Association) and to evaluate the forecasting capability. For drought analysis, the bilinear interpolation method was applied to spatially downscale the low-resolution outputs of GloSea5 and PR (Predicted Runoff) was produced for different lead times (i.e., 1-, 2-, 3-month) running LSM (Land Surface Model). The behavior of PR anomaly was similar to that of HR (Historical Runoff) and the estimated values were negative up to lead times of 1- and 2-month. For the evaluation of drought outlook, SRI (Standardized Runoff Index) was selected and PR_SRI estimated using PR. ROC score was 0.83, 0.71, 0.60 for 1-, 2- and 3-month lead times, respectively. It also showed the hit rate is high and false alarm rate is low as shorter lead time. The temporal Correlation Coefficient (CC) was 0.82, 0.60, 0.31 and Root Mean Square Error (RMSE) was 0.52, 0.86, 1.20 for 1-, 2-, 3-month lead time, respectively. The accuracy of PR_SRI was high up to 1- and 2-month lead time on local regions except the Gyeonggi and Gangwon province. It can be concluded that GloSea5 has high applicability for hydrological drought outlook.

• Health Policy and Management
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• v.30 no.3
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• pp.355-365
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• 2020

Background: This study is aimed to verify individual and regional-level factors affecting the depression of Koreans and to develop social programs for improving the depressive status. Methods: This study used individual-level variables from the Korean Community Health Survey (2018) and used the e-regional index of the Korean Statistical Information Service as the regional-level variable. A multi-level logistic regression was executed to identify individual and regional-level variables that were expected to affect the extent of depressive symptoms and to draw the receiver operating characteristic curve to compare the volume of impact between variables from both levels. Results: The results of the multi-level logistic regression analysis in regards to individual-level factors showed that older age, female gender, a lower income level, a lower education level, not having a spouse, the practice of walking, the consumption of breakfast higher levels of stress, and having high blood pressure or diabetes were associated with a greater increase in depressive symptoms. In terms of regional factors, areas with fewer cultural facilities and fewer car registration had higher levels of depressive symptoms. The comparison of area under the curve showed that individual factors had a greater influence than regional factors. Conclusion: This study showed that while both, individual and regional-level factors affect depression, the influence of the latter was relatively weaker as compared to the first. In this sense, it is necessary to develop programs focused on the individual, such as social prescribing at the local or community-level, rather than the city and nation-level approach that are currently prevalent.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.465-474
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• 2009

The emergence of the digital age has changed the paradigm of education. Recently, the new paradigm needs new digital books that contain more interactive contents. Our goal is to design the digital textbook with convenient interfaces and cutting effects for interactive and effective education. To achieve these goals, we propose interfaces and contents which are designed after a lot of discussion with educational experts. In the implementation step, cutting algorithm is proposed to generate the cut planes of the 3D objects, based on the free strokes specified by the users. In order to test the performance of the contents, the testbed was implemented so that students try our digital book and present their evaluation results on the convenience and the effectiveness.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6443-6447
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• 2014

Background: To determine the predictive value of the baseline stimulated thyroglobulin (STg) level for ablation outcome in patients undergoing adjuvant remnant radioiodine ablation (RRA) for differentiated thyroid carcinoma (DTC). Materials and Methods: This retrospective study accrued 64 patients (23 male and 41 female; mean age of $40{\pm}14$ years) who had total thyroidectomy followed by RRA for DTC from January 2012 till April 2014. Patients with positive anti-Tg antibodies and distant metastasis on post-ablative whole body iodine scans (TWBIS) were excluded. Baseline STg was used to predict successful ablation (follow-up STg <2 ng/ml, negative diagnostic WBIS and negative ultrasound neck) at 7-12 months follow-up. Results: Overall, successful ablation was noted in 37 (58%) patients while ablation failed in 27 (42%). Using the ROC curve, a cut-off level of baseline STg level of ${\leq}14.5ng/ml$ was found to be most sensitive and specific for predicting successful ablation. Successful ablation was thus noted in 25/28 (89%) of patients with baseline STg ${\leq}14.5ng/ml$ and 12/36 (33%) patients with baseline STg >14.5 ng/ml ((p value <0.05). Age >40 years, female gender, PTS >2 cm, papillary histopathology, positive cervical nodes and positive TWBIS were significant predictors of ablation failure. Conclusions: We conclude that in patients with total thyroidectomy followed by I-131 ablation for DTC, the baseline STg level is a good predictor of successful ablation based on a stringent triple negative criteria (i.e. follow-up STg < 2 ng/ml, a negative DWBIS and a negative US neck).

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.31-39
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• 2021

In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of class-imbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.

• Food Science of Animal Resources
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• v.41 no.2
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• pp.312-323
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• 2021

The purpose of this study was to use 1H nuclear magnetic resonance (1H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using 1H-13C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R2=0.945; Q2=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both 1H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.53-53
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• 2023

본 연구에서는 토양의 동결-융해 상태 구분을 위해 영상레이더(Synthetic Aperture Radar) 자료를 활용해 지표상태인자(Surface State Factor, SSF)를 산정하고, 관측 토양수분 자료 및 지표면 온도(Land Surface Temperature, LST) 자료와의 비교를 통해 SSF의 정확도를 분석하였다. SSF 산정은 용담댐 유역을 포함한 인근 40×50 km²의 영역(N35°35'~36°00', E127°20'~127°45')에 대한 9개의 토양수분 관측지점(계북, 천천, 상전, 안천, 부귀, 주천, 장수읍, 진안읍, 무주읍)을 대상으로 연구를 수행하였으며, 이를 위해 2015년부터 2019년까지의 해당 지점의 토양수분 관측자료와 Sentinel-1A Interferometric Wide swath (IW) 모드의 Ground Range Detected (GRD) product를 구축하여 활용하였다. SSF 자료의 정확도 분석을 위한 토양수분 관측지점에 대한 LST 자료는 인근 7개 기상관측소 지점(전주, 금산, 임실, 남원, 장수, 함양군, 거창)의 관측자료로부터 역거리가중법을 통해 산정하였다. Receiver Operating Characteristic (ROC) 분석을 통한 겨울철(12-2월)의 SSF 산정 정확도를 평가한 결과, 지표면 온도 자료와의 평균 정확도는 0.75(0.48-0.87)로 나타났다. 그러나, 지표면 온도가 0℃ 이상일 때 SSF가 동결 상태로 나타나는 오차가 관측되었으며, 이는 여름철 후방산란계수의 평균값과 겨울철 후방산란계수의 평균값을 통해 산정하는 SSF 산정 수식의 특성 때문으로 이 값의 조정을 통해 오차를 개선할 수 있음을 보였다.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3407-3412
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• 2015

Background: The aim of this study was to evaluate and compare the accuracy of diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) value, and time-intensity curve (TIC) type analysis derived from dynamic contrast-enhanced MR imaging (DCE-MRI) in differentiating benign from malignant adnexal masses. Materials and Methods: 47 patients with 56 adnexal masses (27 malignant and 29 benign) underwent DWI and DCE-MRI examinations, prior to surgery. DWI signal intensity, mean ADC value, and TIC type were determined for all the masses. Results: High signal intensity on DWI and type 3 TIC were helpful in differentiating benign from malignant adnexal masses (p<0.001). The mean ADC value was significantly lower in malignant adnexal masses (p<0.001). An ADC value< $1.20{\times}10^{-3}mm^2/s$ may be the optimal cutoff for differentiating between benign and malignant tumors. The negative predictive value for low signal intensity on DWI, and type 1 TIC were 100%. The pairwise comparison among the receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of TIC was significantly larger than the AUCs of DWI and ADC (p<0.001 for comparison of TIC and DWI, p<0.02 for comparison of TIC and ADC value). Conclusions: DWI, ADC value and TIC type derived from DCE-MRI are all sensitive and relatively specific methods for differentiating benign from malignant adnexal masses. By comparing these functional MR techniques, TIC was found to be more accurate than DWI and ADC.

• Journal of Environmental Impact Assessment
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• v.22 no.3
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• pp.219-230
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• 2013

Species distribution modeling is one of the most effective habitat analysis methods for wildlife conservation. This study was for evaluating the suitability of species distribution to distance between forest patches in Seoul city using tits. We analyzed the distribution of the four species of tits: varied tit (Parus varius), marsh tit (P. palustris), great tit (P. major) and coal tit (P. ater), using the landscape indexes and connectivity indexes, and compared the resulting suitability indexes from 100m to 1,000m. As factors affecting to the distribution of tits, we calculated landscape indices by separating them into intra-patch indices (i.e. logged patch area (PA), area-weighted mean patch shape index (PSI), tree rate (TR)) and inter-patch indices (i.e. patch degree (PD), patch betweenness (PB), difference probability of connectivity (DPC)), to analyze the internal properties of the patches and their connectivity by tits occurrence data using logistic regression modeling. The models were evaluated by AICc (Akaike Information Criteria with a correction for finite sample sizes) and AUC (Area Under Curve of ROC). The results of AICc and AUC showed DPC, PA, PSI, and TR were important factors of the habitat models for great tit and marsh tit at the level of distance 500~800m. In contrast, habitat models for coal tit and varied tit, which are known as forest interior species, reflected PA, PSI, and TR as intra-patch indices rather than connectivity. These mean that coal tit and varied tit are more likely to find a large circular forest patch than a small and long-shaped forest patch, which are higher rate of forest. Therefore, different strategies are required in order to enhance the habitats of the forest birds, tits, in a region that has fragmented forest patches such as Seoul city. It is important to manage forest interior areas for coal tit and varied tit, which are known as forest interior species and to manage not only forest interior areas but also connectivity of the forest patches in the threshold distance for great tit and marsh tit as adapted species to the urban ecosystem for sustainable ecosystem management.

• Journal of Environmental Impact Assessment
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• v.23 no.5
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• pp.379-392
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• 2014

This study was performed to predict the future climate envelope of Pinus pumila, a subalpine plant and a Climate-sensitive Biological Indicator Species (CBIS) of Korea. P. pumila is distributed at Mt. seorak in South Korea. Suitable habitat were predicted under two alternative RCPscenarios (IPCC AR5). The SDM used for future prediction was a Maxent model, and the total number of environmental variables for Maxent was 8. It was found that the distribution range of P. pumila in the South Korean was $38^{\circ}7^{\prime}8^{{\prime}{\prime}}N{\sim}38^{\circ}7^{\prime}14^{{\prime}{\prime}}N$ and $128^{\circ}28^{\prime}2^{{\prime}{\prime}}E{\sim}128^{\circ}27^{\prime}38^{{\prime}{\prime}}E$ and 1,586m~1,688m in altitude. The variables that contribute the most to define the climate envelope are altitude. Climate envelope simulation accuracy was evaluated using the ROC's AUC. The P. pumila model's 5-cv AUC was found to be 0.99966. which showed that model accuracy was very high. Under both the RCP4.5 and RCP8.5 scenarios, the climate envelope for P. pumila is predicted to decrease in South Korea. According to the results of the maxent model has been applied in the current climate, suitable habitat is $790.78km^2$. The suitable habitats, are distributed in the region of over 1,400m. Further, in comparison with the suitable habitat of applying RCP4.5 and RCP8.5 suitable habitat current, reduction of area RCP8.5 was greater than RCP4.5. Thus, climate change will affect the distribution of P. pumila. Therefore, governmental measures to conserve this species will be necessary. Additionally, for CBIS vulnerability analysis and studies using sampling techniques to monitor areas based on the outcomes of this study, future study designs should incorporate the use of climatic predictions derived from multiple GCMs, especially GCMs that were not the one used in this study. Furthermore, if environmental variables directly relevant to CBIS distribution other than climate variables, such as the Bioclim parameters, are ever identified, more accurate prediction than in this study will be possible.