• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.911-920
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• 2004

We try receiver operating characteristic(ROC) curves by neural networks of logistic function. The models are shown to arise from model classification for normal (diseased) and abnormal (nondiseased) groups in medical research. A few goodness-of-fit test statistics using normality curves are discussed and the performances using neural networks of logistic function are conducted.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.383-394
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• 2001

This paper is a generalization of single and two-level skip-lot sampling plans to n-level. On every skipping inspection of the n-level skip-lot sampling plan, not only the number of consecutive lots to be accepted but also the fraction of lots to be inspected can be freely choosed. The general formulas of the operating characteristic function, average fraction inspected, average sample number and average outgoing quality in n-level skip-lot sampling plan are derived. The operating characteristic curves, average sample number and average outgoing quality of a reference plan, two-level and five-level skip-lot sampling plans are compared.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.915-927
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• 2003

This paper is the generalization of the modified two-level skip-lot sampling plan(MTSkSP1) to n-level. The general formulas of the operating characteristic(OC) function, average sample number(ASN) and average outgoing quality(AOQ) for the plan are derived using Markov chain properties. The operating characteristic curves, average sample numbers and average outgoing qualities of a reference plan, modified two-level, three-level and five-level skip-lot sampling plans are compared.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.150-153
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• 1994

Receiver operating characteristic (ROC) curves have been frequently used to compare probability models applied to medical problems. Though the curves are a measure of the discriminatory power of a model. they do not reflect the model's accuracy. A supplementary accuracy curve is derived which will be coincident with the ROC curve if the model is reliable. will be above the ROC curve if the model's probabilities are too high or below if they are too low. A clinical example of this new graphical presentation is given.

• Wind and Structures
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• v.32 no.2
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• pp.81-87
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• 2021

The aim of the current study is to compare the performance of large 2 MW and 3 MW wind turbines operating on existing onshore wind farms using Blade Element Momentum (BEM) theory and Angular Momentum (AM) theory and illustrate the performance characteristic curves of the turbines as a function of wind speed (U∞). To achieve this, the measurement data obtained from two different Wind Energy Power Plants (WEPPs) located in the Hatay region of Turkey was used. Two different horizontal-axis wind turbines with capacities of 2 MW and 3 MW were selected for evaluation and comparison. The hub-height wind speed (UD), turbine power output (P), atmospheric air temperature (Tatm) and turbine rotational speed (Ω) data were used in the evaluation of the turbine performance characteristics. Curves of turbine power output (P), axial flow induction factor (a), turbine rotational speed (Ω), turbine power coefficient (CP), blade tip speed ratio (λ), thrust force coefficient (CT) and thrust force (T) as a function of U∞ were obtained for the 2 MW and 3 MW wind turbines and these characteristic curves were compared. Results revealed that, for the same wind speed conditions, the higher-capacity wind turbine (3 MW) was operating at higher turbine power coefficient rates, while rotating at lower rotational speed ratios than the lower-capacity wind turbine (2 MW).

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.541-553
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• 2020

The receiver operating characteristic (ROC) curve is expressed as both sensitivity and specificity; in addition, some optimal thresholds using the ROC curve are also represented with both sensitivity and specificity. In addition to the sensitivity and specificity, the expected usefulness function is considered as disease prevalence and usefulness. In particular, partial the area under the ROC curve (AUC) on a certain range should be compared when the AUCs of the crossing ROC curves have similar values. In this study, partial AUCs representing high sensitivity and specificity are proposed by using sensitivity and specificity lines, respectively. Assume various distribution functions with ROC curves that are crossing and AUCs that have the same value. We propose a method to improve the discriminant power of the classification models while comparing the partial AUCs obtained using sensitivity and specificity lines.

• Imaging Science in Dentistry
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• v.33 no.2
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• pp.91-95
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• 2003

Purpose : To compare the diagnostic accuracy of proximal caries detection between Kodak Insight film and the Biomedisys CDX2000HQ digital (CCD) sensor. Materials and Methods: 156 proximal surfaces of extracted teeth, 78 of which had chemical artificial caries, were used in this study. Four observers interpreted the radiographs using a five-point confidence rating scale to record their diagnoses. The results were analyzed by receiver operating characteristic curves, ANOVA and Kappa values. Result: Analysis using receiver operating characteristic curves revealed the areas under each curve which indicated a diagnostic accuracy of 0.951 in Insight and 0.952 in CDX2000HQ digital sensor. ANOVA revealed no significant differences between the two images with respect to caries detection. Kappa values indicated that the mean intra-observer agreement was 0.85 and inter-observer agreement 0.71 in conventional radiography. In digital radiography, the mean intra-observer agreement was 0.84 and inter-observer agreement 0.72. Conclusion: The results suggest that no significant difference exists between the two modalities for artificial caries detection and that CDX2000HQ was as good as Insight film for this purpose.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.2
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• pp.126-131
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• 2001

One of the major obstacles in testing or evaluating precisely the thermal behavior of a machine tool is the difficulty in measuring the heat transfer coefficients on the surfaces by a conventional method. This paper presents a new approach based on the inverse method to identify the values of heat transfer coefficients by using temperature changes measured on the surfaces of a machine tool during a short period in its operating. In the present method, a machine tool structure is modeled by the finite element method and the characteristic curves of the temperature change at several points on machine tool surfaces are theoretically derived in the form that they contain the heat transfer coefficient as an unfixed heat source are approximated so that the theoretical characteristic curves of temperature change fit the measured ones as closely as possible.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• Sleep Medicine and Psychophysiology
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• v.6 no.1
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• pp.26-31
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• 1999

"Attention" is not defined sufficiently. This term incorporates several dimensions or complex information processes such as alertness, spatial distribution, focused attention, sustained attention, divided attention and supervisory attentional control. In practice, however, various aspects of attention cannot be assessed separately with a single test. Moreover, a particular test is never assessing attention only, because the several intervening variables may influence the attentional component. Therefore, one can only assess a certain aspect of human behavior with special interest for its attentional component. This paper attempted to clarify various concepts of attention, reviewed signal detection theories with receiver operating characteristic(ROC) curves, and listed practical methods for assessment of attention.