• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.370-370
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• 2017

최근 기후변화와 이상기후의 영향으로 인하여 홍수재해의 시 공간적 패턴은 보다 복잡해지고, 예측이 어려워지고 있다. 이러한 기상이변에 따른 홍수피해를 예방하기 위한 비구조적 대책으로 홍수위험등급 및 범람범위 등의 정보를 포함하고 있는 홍수위험지도의 작성이 필요하다. 실제로 고정밀도 홍수위험지도를 작성하기 위해서는 지형, 지질, 기상 등의 디지털 정보 및 사회 경제와 관련된 다양한 DB를 필요로 하며, 강우-유출-범람해석 모델링을 통해 범람면적 및 침수깊이 등의 정보를 획득하게 된다. 하지만 일부지역, 특히 개발도상국에서는 이러한 계측 홍수 데이터가 부족하거나 획득할 수가 없어 홍수위험지도 제작이 불가능하거나 그 정확도가 매우 낮은 실정이다. 따라서 본 연구에서는 ASTER 또는 SRTM과 같은 범용 DEM 등 지형자료만을 기반으로 한 선형이진분류법(Liner binary classifiers)과 ROC분석(Receiver Operation Characteristics)을 이용하여 미계측 유역 (DB부재 또는 부족으로 강우-유출-범람해석 모델링이 불가능한 북한지역)의 홍수위험지역을 탐지하고, 적용성을 평가하고자 한다. 5개의 단일 지형학적 지수와 6개의 복합 지형학적 지수를 이용하여 Area Under the Curve (AUC)를 계산하고, Sensitivity (민감도)와 Specificity (특이도)가 가장 높은 지수를 선별하여 홍수위험지도를 작성하고, 실제 홍수범람 영상(2007년 북한 함경남도지역 용흥강 홍수)과 비교 분석하였다. 본 연구에서 제시하는 선형이진분류법과 ROC분석 방법은 홍수범람해석을 위한 다양한 기초정보를 필요로 하지 않고, 지형정보만을 사용하기 때문에 관측 데이터가 없거나 부족한 지역에 대해서 우선적으로 홍수위험지역을 탐지하고, 선별하는데 유용할 것으로 판단된다.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• Imaging Science in Dentistry
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• v.36 no.1
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• pp.1-5
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• 2006

During the diagnostic process of the various oral and maxillofacial lesions, we should consider the following: 'When should we order diagnostic tests? What tests should be ordered? How should we interpret the results clinically? And how should we use this frequently imperfect information to make optimal medical decisions?' For the clinicians to make proper judgement, several decision making tools are suggested. This article discusses the concept of the diagnostic accuracy (sensitivity and specificity values) with several decision making tools such as decision matrix, ROC analysis and Bayesian analysis. The article also explain the introductory concept of ORAD program.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.75-82
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• 1991

This research is concerned with Machine-Part Group Formation(MPGF) methodology for Flexible Manufacturing Systems(FMS). The purpose of the research is to develop a new heuristic algorithm for effectively solving MPGF problem. The new algorithm is proposed and evaluated by 100 machine-part incidence matrices generated. The performance measures are (1) grouping ability of mutually exclusive block-diagonal form. (2) number of unit group and exceptional elements, and (3) grouping time. The new heuristic algorithm has the following characteristics to effectively conduct MPGF : (a) The mathematical model is presented for rapid forming the proper number of unit groups and grouping mutually exclusive block-diagonal form, (b) The simple and effective mathematical analysis method of Rank Order Clustering(ROC) algorithm is applied to minimize intra-group journeys in each group and exceptional elements in the whole group. The results are compared with those from Expert System(ES) algorithm and ROC algorithm. The results show that the new algorithm always gives the group of mutually exclusive block-diagonal form and better results(85%) than ES algorithm and ROC algorithm.

• International Journal of Contents
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• v.10 no.1
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• pp.23-28
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• 2014

When developing a classifier using various objective functions, it is important to compare the performances of the classifiers. Although there are statistical analyses of objective functions for classifiers, simulation results can provide us with direct comparison results and in this case, a comparison criterion is considerably critical. A Receiver Operating Characteristics (ROC) graph is a simulation technique for comparing classifiers and selecting a better one based on a performance. In this paper, we adopt the ROC graph to compare classifiers trained by mean-squared error, cross-entropy error, classification figure of merit, and the n-th order extension of cross-entropy error functions. After the training of feed-forward neural networks using the CEDAR database, the ROC graphs are plotted to help us identify which objective function is better.

• Membrane and Water Treatment
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• v.8 no.6
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• pp.553-562
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• 2017

In the present study, the water desalination across the penta-graphene has been explored by using molecular dynamics simulation. The penta-graphene, a new carbon allotrope, introduced theoretically in 2015. It was shown that this carbon nanostructure is slightly stiffer against buckling in comparison with the graphene nanoribbons. The effect of radius of curvature (ROC) of the membrane, pore size, and applied pressure, on water flow rate, and salt rejection is investigated. It is shown that salt rejection, and the shape of the oxygen density distribution inside the pore can be influenced by the ROC of membrane. Finally, it is shown that the ROC, and pore size of 2D membranes, play an important role in the salt rejection.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1521-1530
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• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.80-89
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• 2019

The purpose of this study is to analyze the relationship between SPI (Standardized Precipitation Index) meteorological drought and RDI (Reservoir Drought Index) agricultural drought for Geum river basin. Drought Indices was calculated by collecting data of precipitation and agricultural reservoir water storage rate from 2014 to 2016. To evaluated the correlation between meteorological and agricultural drought, the Pearson correlation and the Receiver Operation Characteristic (ROC) analysis were conducted to evaluate the correlation between meteorological and agricultural droughts. The SPI-6 and RDI showed the highest relationship with Pearson coefficient 0.606 and ROC hit rates 0.722 respectively, and the spatial occurrence patterns of drought using overlapped SPI-6 and RDI, the big differences between the 2 indices were occurred in the upstream areas of Miho stream and Nonsan stream from August to October 2015. The analysis using reservoirs specifications for areas where reservoir droughts occurred was conducted, and the areas showing severe drought of RDI were the reservoir areas having relatively small value of basin magnifying power (BMP). This means that a reservoir has the reaction capability for agricultural drought mainly depending on the reservoir BMP.

• Progress in Medical Physics
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• v.19 no.4
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• pp.298-304
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• 2008

Current digital radiographic systems are rapidly growing in clinical applications. The purpose of this study was to evaluate the diagnostic performance of computed radiography (CR) and digital radiography (DR) at different tube voltages in the detection of simulated chest lesions. Patterns of simulated interstitial lung disease, incipient infiltration, and nodules were superimposed over an anthropomorphic chest phantom. A simulated chest phantom radiograph was obtained with CR and DR at different tube voltages (70 kV, 90 kV, and 120 kV). A total of 18,000 observations were analyzed using a receiver operating characteristic (ROC) analysis. The detection of all lesions showed higher $A_z$ values at 70 kV than 120 kV with CR. For the DR, mean $A_z$ values at 70 kV were higher than other tube voltages not all lesions but for micro-nodule interstitial lung disease, linear interstitial lung disease, and incipient infiltration. Based on these results, a clinical study should be performed to judge the use of suitable tube voltage according to the type of detector system and lesions.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.691-701
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• 2017

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.