• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.1-7
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• 2022

The study was conducted to determine the effectiveness of the triage training program utilizing pre-and post-training experiments designed for 119 emergency medical services teams. Objectives: This study evaluated the effectiveness of triage training programs on the accuracy of triage performed by 119 emergency medical services team staff who participated in the triage training program. Behavior: Participants in this study included 119 of the 166 EMS staff. In this program, a modified START triage consisting of a 20-minute theoretical presentation was presented to the participants. Data were analyzed using SPSS 21.0. Results: A significant increase in triage accuracy for 119 EMS teams(p<.001). And undertriage showed a significant decrease(p<.001). In addition, overtriage showed a decrease but was not statistically significant. Conclusions: The results obtained from this study showed that the triage training program was effective in improving the accuracy of the triage of multiple injury patients or disaster victims when presented to the 119 emergency medical services team. Therefore, these results suggest that it would be helpful to add triage training to the fire department's formal training program.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.357-371
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• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1265-1271
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• 2009

본 연구에서는 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우-유출예측모형을 위해 인공신경망(Artificial Neural Networks: ANNs)의 기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론과 역전파 학습 알고리즘(Back Propagation Algorithm: BPA) 이론을 복합적으로 이용하였다. 기존의 인공신경망 연구에서 야기된 저 갈수기의 유출량에 대한 과대평가, 홍수기의 유출량에 대한 과소평가, 예측값이 연속적으로 선행 유출량을 나타내는 Persistence 현상을 해결하기 위하여 패턴분류 성능을 지닌 SOM 이론을 예측모형의 전처리 과정으로 이용하였다. 먼저, 본 연구에서 제안한 방법은 SOM에 의해 강우-유출 관계를 분류하고, SOM에 의한 분류에 따라 각각의 모형을 구성한다. 개별적으로 구축된 모형은 유출량의 예측을 위해 각각의 양상에 따라 분류된 자료를 이용한다. 결과적으로 본 연구에서 제안한 방법은 과거의 인공신경망의 일반적인 적용에 의한 결과보다 더 나은 예측능력을 보여주었으며, 더불어 유출량의 과소 및 과대추정과 Persistence 현상과 같은 문제점이 나타나지 않았다. 또한 강우량 및 유출량의 범위에 제한을 받지 않는 강우-유출예측 모형의 개발 및 홍수기로부터 갈수기까지의 보다 넓은 범위의 유출량의 예측에 기여할 것으로 기대된다.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.98-103
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• 2020

The purpose of this study was to determine whether the results of KTAS(Korean Triage and Acuity Scale) triage classifier differ according to the occupations. We analyzed a total of 10,960,359 cases of data sent to the NEDIS from January 1st, 2016 to December 31th, 2017. The triage classifier were MD(Medical Doctor), R(Resident), INT(Intern), GP(General Practitioner), RN(Registered Nurses) and EMT(Emergency Medical Technician). The consistency between the initial triage and final triage results was the highest GP(98.9%) and the lowest INT(80.2%). The results of over-triage classification was the lowest by GP(0.6%) and the highest for INT(16.0%). Also, the results of under-triage classification was the lowest by MD, EMT(0.4%) and the highest for INT(3.8%). The results of KTAS triage classifier significantly differ from according to the occupations(p<0.001). Triage classification should not differ from according to occupations and skill. It is necessary to strengthen the classifier's capacity for accurate triage classifications.

• Journal of The Geomorphological Association of Korea
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• v.19 no.3
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• pp.1-22
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• 2012

The classification scheme of estuaries can be divided into two categories: qualitative classification based on geomorphic characteristics and quantitative classification based upon the physical properties of water body. While simple and intuitive scheme of the former is difficult to quantify, the latter is not easy to apply due to the lack of data. A classification scheme based on morphological convergence is very promising because it only requires easily accessible data such as width and depth of channels, as well as it can characterize estuaries in terms of tidal propagation. Thus, this paper examines the classification scheme based on estuarine morphological convergence using depth and width data obtained from 19 major Korean estuaries. Morphological convergence for each estuary was estimated with the estuarine length, width and depth data to get the convergence parameters, which includes the degree of funneling ${\nu}$ and the dimensionless estuarine length $y_0$. The transfer function ${\xi}({\nu},ky)$ is then deduced analytically from 1D depth-integrated hydrodynamic momentum equation and continuity equation for estuarine shapes. Tidal response of each estuary is finally calculated using ${\nu}$, $y_0$ and ${\xi}({\nu},ky)$ for comparison and classification. The 19 Korean estuaries were classified into three groups: tidal amplitude-dominated estuaries with standing wave-like tidal response (group 1), current-dominated estuaries with progressive wave-like tidal response (group 2), and the intermediate group (group 3) between groups 1 and 2. The sensitivity analysis revealed that uncertainties in determining the estuarine length can have a critical effect upon the results of classification, which indicates that the reasonable determination of the estuarine length is of critical importance. Once the estuarine length is feasibly determined, depth-convergence can be neglected without any negative effect on the classification scheme, which has an important ramification on the wide applicability of the classification scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5492-5500
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• 2015

Low-density lipoprotein cholesterol (LDL-C) is a major modifiable risk factor for cardio- cerebrovascular disease. In clinical practice, however, it is primarily calculated using the Friedewald formula as a cost-effective method. The aim of this study was to compare Friedewald-estimated and directly measured LDL-C values and assess the concordance in guideline LDL-C risk classification between the two methods. The data were derived from the 2009 and 2010 Korea National Health and Nutrition Survey (KNHANES). Analysis was done for 4,319 subjects with lipid panels-total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), directly measured LDL-C using an enzymatic homogeneous assay, and triglycerides (TG). For subjects with TG lower than 400 mg/dL, Friedewald-estimated and directly measured LDL-C were highly correlated (r = 0.958, p < 0.001) and overall concordance was 82.7%. As TG increased, overall concordance decreased. Overall concordance was 85.4% at TG lower than 150 mg/dL; 78.2% at TG of 150-199 mg/dL; and 71.4% at TG of 200-399 mg/dL. The Friedewld equation tended to overestimate LDL-C when TG are of < 150 mg/dL; however, underestimate LDL-C when TG are of ${\geq}150mg/dL$. As a result, Friedewald estimation misclassified 382 subjects (9.1%) in a higher category versus 348 subjects (8.3%) in a lower category. Our findings suggest that overestimation of LDL-C by the Friedewald formula can be a great problem as well as underestimation.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.31-40
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• 1998

본 연구의 목적은 기존의 Cell Transmission(1994, Daganzo) 교통류 이론을 기반으로 동적통행배정 모형을 개발하는 것이다. 이 모형은 동적 O-D 발생모듈, HOV 차선모듈, 분류부 분할모델, 링크비용함수 모듈, 최단경로 탐색 모듈등으로 구성된다. 이 모델에서 적용하는 교통류 모델은 각 링크를 동일한 특성을 가지는 셀로 구분하여 셀내의 진입시간과 진출시간을 계산하여 링크비용을 계산하는데 이것은 비용의 과대·과소 추정을 피할 수 있으며 교통지체 현상을 현실적으로 표현해 줄 수 있는 장점이 있다. 또한 HOV 차선 모듈에 의해 수단별 교통류 진행 및 비용고려가 가능하며 HOV 차선의 평가 및 분석이 가능하다. 기존의 동적통행배정모형은 매 시간대별 출발지에서 균형상태를 추구하는 통행배정기법을 사용하고 있지만 이 모델은 분류되는 노드를 가상의 출발점이라고 가정하여 각 시간대별로 최단경로를 탐색하여 균형상태를 추구해나가는 기법을 적용하고 있다. 각 셀별 차량을 목적지별, 차종별, 대기시간별로 추적하여 진행시키며 분류부에서는 최단경로를 탐색하여 배분된다. 또한 진행하고자 하는 셀의 용량과 현재 셀의 밀도를 고려함으로서 용량제약 하에서의 동적통행배정모형을 적용하고 있다. 이 모형은 고속로의 합류부 및 분류부의 교통특성을 세밀히 분석할 수 있으며, TCS 및 램프미터링과 접목하여 고속도로 운영에 이용될 수 있으며, 고속도로 중·장기적인 계획에 이용될 수 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.225-231
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• 1999

In statistical analysis of remotely sensed data, means and variances of each classes are used as the basis of statistical similarity determination. Therefore, the overall accuracy of classification is affected by the training results. It is assumed that the ideal distributions of pixel values follow normal distributions, but practically they have some aggregations and biases. non anomalies of distribution can affect the classification results greatly as well as the variances of training results. In this study, relationships between the inferential variances of the training sets and the distributions of pixel values are examined. and the resulting changes of classification results are studied. Furthermore, the training method which minimizes the effect of underestimation of variances is proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.13-29
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• 2020

Automobiles have long been an essential part of daily life, but the social costs of car traffic accidents exceed 9% of the national budget of Korea. Hence, it is necessary to establish prevention and response system for car traffic accidents. In order to present a model that can classify and predict the degree of injury in car traffic accidents, we used big data analysis techniques of K-nearest neighbor, logistic regression analysis, naive bayes classifier, decision tree, and ensemble algorithm. The performances of the models were analyzed by using the data on the nationwide traffic accidents over the past three years. In particular, considering the difference in the number of data among the respective injury severity levels, we used down-sampling methods for the group with a large number of samples to enhance the accuracy of the classification of the models and then verified the statistical significance of the models using ANOVA.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.10a
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• pp.296-304
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• 1995

환경시험의 일부인 진동시험은 그 결과를 가지고 장비가 실제 운용하기 전에 진동에 대한 내구성이 있는가를 판단할 수 있는 단 하나의 방법이다. 이런 진동시험을 성공적으로 이끌어 정확한 결론에 도달하기 위해서는 아래 두 가지 문제에 대한 해결이 필수적이라고 생각한다. 첫번째는 '실제 상황을 정확히 반영할 수 있는 진동시험수준의 결정이다.' 진동시험수준이 적절하지 못하면 전체적으로나 부분적으로 과대진동시험(Overtest)을 실시하거나, 과소진동시험(Undertest)을 실시할 수 밖에 없고, 이에 따른 결과도 신뢰도가 떨어진다. 예를 들어, 과대진동시험으로 시험장비가 고장나거나, 과소진동시험으로 시험장비에 이상이 없다고 해서 실제 상황에서 이 장비가 진동에 대하여 만족할 만한 내구성을 갖는다고 누구도 말할 수 없기 때문이다. 두번째는 '정해진 진동시험수준을 진동시험기로부터 시험장비에 얼마나 정확히 전달할 수 있는가?'라는 문제이다. 실제로 원하는 진동시험수준을 한치의 오차없이 정확히 시험장비에 가하는 것은 거의 불가능하다. 특히 시험장비가 대형화 되면 될수록 문제는 더 심각하다. 결론적으로 위에 지적한 두가지 문제의 해결이 성공적인 진동시험의 열쇠이며, 또한 시험결과에 대한 신뢰성을 보장받을 수 있는 길이기 때문이다. 이번 논문에서 다루고자 하는 것은 두번째 문제인 진동시험수준의 정확한 전달을 위하여 진동시험기와 시험장비 사이를 연결해 주는 진동시험치구(Test Fixture) 개발에 관한 것이다. 실제 개발한 치구는 미군사규격(Military-Standard)과 보잉사 규격(Boeing specification) 그리고 샌디아사 규격(Sandia Corporation Standard)에 근거하여 분류하는 분류기준표에서 치구 중 가장 큰 부류(500 pounds 이상)에 속는 것으로, 현재 우리나라에서 보유하고 있는 것 중에 용량이 가장 큰 진동시험기에서 진동시험을 준비하고 있는, 체계에 전원을 공급하는 대형장비의 치구이다. 또한 실 진동시험을 실시하기 전의 모달시험과 예비시험 그리고 실 시험결과를 기술하므로써 제작된 치구의 실험적 평가를 하고자 한다.