• 보건행정학회지
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• 제30권3호
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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.

• 한국콘텐츠학회논문지
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• 제9권1호
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• pp.465-474
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• 2009

디지털 시대의 개막과 함께 교육의 패러다임은 변화하고 있다. 이렇게 변화해 가는 패러다임은 더 많은 상호 작용이 존재하는 디지털 교과서 콘텐츠를 필요로 한다. 우리의 목표는 편리한 인터페이스를 갖추고, 커팅효과가 추가되어 기존에 비해 상호작용성이 높은 타블렛 PC 기반의 디지털 교과서를 제작하는 것이다. 이러한 목표를 달성하기 위해서 여러 차례 디지털 교과서를 개발해 온 전문가들과의 회의를 토대로 설계한 인터페이스 및 학습내용에 대해서 설명한다. 그리고 사용자가 임의로 입력한 스트로크에 기반하여 다층 구조의 3D 객체의 단면을 생성하기 위해서 구현된 커팅 알고리즘에 대해서 설명한다. 마지막으로 우리가 개발한 콘텐츠를 시범서비스 했을 때의 결과를 설문 조사 내용을 토대로 하여 상호작용성이 높은 디지털 교과서의 교육적 효과에 대해서 토론할 것이다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권15호
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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).

• 한국안전학회지
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• 제36권3호
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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.

• 한국축산식품학회지
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• 제41권2호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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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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• 제16권8호
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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.

• 환경영향평가
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• 제22권3호
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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.

• 환경영향평가
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• 제23권5호
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• pp.379-392
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• 2014

이 연구는 국립생물자원에서 선정한 기후변화생물지표 중에서 남한의 설악산에 제한적으로 분포하는 눈잣나무의 기후변화에 의한 잠재 서식지 예측을 위해 시행되었다. 눈잣나무의 잠재서식지 예측을위해 IPCC(AR5)의 대표농도경로(RCP)를 기후변화 시나리오로 사용하였다. 종 분포 모형은 Maxent를 사용하였고, 환경변수는 고도, 연평균기온 등으로 총 8개이다. 남한이 눈잣나무 분포지역은 설악산이 유일한 지역으로 지리적 범위는 위도 $38^{\circ}7^{\prime}8^{{\prime}{\prime}}N{\sim}38^{\circ}7^{\prime}14^{{\prime}{\prime}}N$ 경도 $128^{\circ}28^{\prime}2^{{\prime}{\prime}}E{\sim}128^{\circ}27^{\prime}38^{{\prime}{\prime}}E$ 범위에 국지적으로 분포하며, 고도는 1,586m~1,688m 범위에 분포한다. 종 분포 모형의 모형 정확도는 0.978으로 매우 우수하였고 잠재서식지 예측에 기여도가 높은 환경변수는 고도로 나타났다. LPT를 기준으로 선정된 현재기후의 잠재 서식지는 $7,345km^2$이며 기후변화 시나리오를 적용한 미래의 잠재 서식지 면적은 감소하였고 감소폭은 RCP 4.5보다 RCP 8.5가 많았다. 설악산의 눈잣나무 개체군 분포 지역은 한반도의 남방 한계선으로 예상되며 기후변화에 의해 개체군의 축소 및 소실이 예상되므로 전략적인 유전자원 확보를 위한 대책이 필요하다.

• 기술혁신학회지
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• 제19권4호
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• pp.799-821
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• 2016

무인항공기는 현대 전장 환경에서 감시정찰을 위한 필수요소이며, 전장의 복잡성과 불확실성이 증가함에 따라 그 중요성은 더욱 커지고 있다. 본 연구에서는 1982년부터 2014년까지 개발된 96대의 군용 무인항공기를 대상으로 비모수적이며 비통계적 기술예측 방법인 TFDEA를 이용한 기술예측을 실시하였다. 2001년에 최초로 소개된 이후 Inman 외(2006) 등은 TFDEA가 SOA 분석에서 회귀분석과 같은 전통적인 계량방법론보다 예측력이 우수함을 실증하였다. 본 연구에서는 무인항공기에 대한 기술예측 결과 연간 평균기술변화율이 4.06%로 향상되었으며, 개발된 대부분의 무인항공기는 첨단기술 프론티어(SOA) 보다 낮은 수준이었다. 이는 무인항공기를 개발하는 대부분의 국가가 기술적으로 중진국이고, 기술적 선진국인 북미와 유럽의 국가들이 세계 무인항공기 시장의 60% 이상을 장악하고 있다는 것에 기인한다고 볼 수 있다. 본 연구는 TFDEA의 적용분야를 미래체계로서 관심의 대상인 무인화 기술개발 분야로 확대하여 기술혁신의 특성을 분석함으로써 미래 무인항공기의 개발과 기술발전에 관한 기술예측의 기법으로서 적용가능성을 확인하였다. 특히 군의 작전요구성능과 연구개발관리에 필요한 정량적 지표를 설정하는데 활용할 수 있을 것으로 평가된다.