• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.505-515
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• 2008

This paper introduces six single and sequential acceptance sampling plans based on OC( Operating Characteristic) curve. Revised KS standards for acceptance sampling by attribute and by variable such as KSA 3102 : 1996, 3103 : 2005, KSA ISO 14560 : 2006, 8422 : 2001, 8423 : 2001 are presented.

• Journal of Korean Society on Water Environment
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• v.33 no.2
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• pp.130-139
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• 2017

With the need to evaluate accuracy of real-time measurement of cyanobacterial fluorescence to determine cyanobacterial blooms, this research examined 357 paired data (2013-2016) comprising both microscopic toxic cyanobacterial cell counts and concurrent real-time cyanobacterial concentrations at 2 sites (YS1 and YS2) in Yeongsan river. The increase in real-time cyanobacterial concentration was closely associated with the exceedance of 5,000 cyanobacterial cells/ml (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.03-1.12) and 10,000 cells/ml (OR 1.08, 95% CI 1.04-1.12) at YS2 site. The area under the receiver operating characteristic (ROC) curve for the real-time cyanobacterial measurement at the YS2 site was 0.93, which indicates the measurement provides a high accurate detection of cyanobacterial blooms. On the ROC curve, the early alert levels of real-time cyanobacteria ranging $16-23{\mu}g$ chl-a/L would produce acceptable sensitivity of 79% and specificities greater than 90%. The real-time fluorescence measurement was found to be an accurate indicator of cyanobacteria and can serve as a tool for detecting toxic cyanobacterial bloom events in Youngsan river.

• Physical Therapy Rehabilitation Science
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• v.7 no.2
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• pp.54-60
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• 2018

Objective: Persons with chronic stroke fall more often than healthy elderly individuals. The Timed Up and Go test (TUG) is used as a fall prediction tool, but only provides a result for the total measurement time. This study aimed to determine the optimal cut-off values for each of the 6 components of the TUG. Design: Retrospective study. Methods: Thirty persons with chronic stroke participated in the study. TUG evaluation was performed using a wearable miniaturized inertial sensor. Sensitivity, specificity, and predictive values were calculated using the Receiver Operating Characteristic (ROC) curve analysis for the measured values in each section. Optimal values for fall risk classification were determined. Logistic regression analysis was used to investigate the risk of future falls based on TUG. Results: The cut-off values of the 6 sections of the TUG were determined, as follows: sit-to-stand >2.00 seconds (p<0.05), forward gait >4.68 seconds (p<0.05), mid-turn >3.82 seconds (p<0.05), return gait >4.81 seconds (p<0.05), end-turn >2.95 seconds (p<0.05), and stand-to-sit >2.13 seconds (p<0.05). The risk of falling increased by 2.278 times when the mid-turn value was >3.82 seconds (p<0.05). Conclusions: The risk of falls increased by 2.28 times when the value of the mid-turn interval exceeded 3.82 seconds. Therefore, when interpreting TUG results, the predictive accuracy for falls will be higher when the measurement time for each section is analyzed, together with the total time for TUG.

• Dementia and Neurocognitive Disorders
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• v.23 no.1
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• pp.11-21
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• 2024

Background and Purpose: Although dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia, its clinical prevalence is low. We developed a short and easy-to-complete DLB screening questionnaire (DLBSQ) to raise diagnostic sensitivity in routine clinical settings. Methods: A total of 501 participants were retrospectively enrolled, including 71 controls, 184 patients without DLB, and 246 patients with probable DLB. All patients underwent clinical evaluation, including core features of DLB, the DLBSQ, brain magnetic resonance imaging, and detailed neuropsychological assessments. The diagnostic performance of the DLBSQ for probable DLB was investigated using a receiver operating characteristic curve analysis. Results: Total DLBSQ score was associated with visuospatial and frontal/executive dysfunction and the diagnosis of probable DLB. The area under the receiver operating characteristic curve for total DLBSQ score was 0.727. Youden's method revealed an optimal cutoff value of 3. The sensitivity and specificity of the DLBSQ were 68.7% and 62.4%, respectively. Its discriminating performance improved when cognitive test profiles were additionally considered (area under the curve: 0.822, sensitivity: 80.6%, and specificity: 70.4%). Conclusions: The DLBSQ might be a useful screening tool for DLB in routine clinical practice with good sensitivity and specificity.

• 한국기계연구소 소보
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• s.18
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• pp.29-35
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• 1988

When low sliding velocities in the boundary lubrication range are operating, irregular movements frequently occur which are a result of the stick-slip phenomenon. Such slide motions are undesirable in precision machine tools, particularly with feed back systems used in numerical and adaptive control machine tools. Accordingly, this paper reports analytical and experimental studies in the stick-slip characteristic of machine tool feeding system. The main conclusions of this study are as follows; The tendency towards stick-slip effects may be reduced by; 1). Reducing the drop in friction coefficient in the Stribeck curve(on the rising part of the friction characteristic at higher sliding speeds, the system is stable all the time) 2). Reducing the transition velocity by the use of a higher viscosity lubricating oil. 3). Increasing the stiffness(Damping)and reducing normal load(Sliding mass) Therefore, the Critical velocity is decided from the above conclusions and in designing of machine tool, feed rates(sliding velocity)must be design the more than critical velocity.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.533-545
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• 2022

Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.19-30
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• 2006

The purpose of this article is to develop a program for teaching Type I and Type II errors in one sample. In this program, the concepts of two errors are visually explained, and the probabilities of two errors are also visually displayed as the rejection region changes or the sample size changes. Also, in this program, the power curve and the operating characteristic curve are visually displayed in terms of the parameter value of interest.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.3
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• pp.191-198
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• 2002

Cooling tower design procedure was set up using conventional Merkel theory, The design data could be different depending on the characteristic curve that the engineer chose. It reveals that the consistent and reasonable criteria are required based on the exact information of the cooling tower Performance. In this study, an off-design performance analysis program for a counterflow-type cooling tower was developed and verified by comparing with experimental data. Also, the off-design performance with various operating conditions was analyzed.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6781-6785
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• 2014

Purpose: This study used receiver operating characteristic curves to analyze Surveillance, Epidemiology and End Results (SEER) medulloblastoma (MB) and primitive neuroectodermal tumor (PNET) outcome data. The aim of this study was to identify and optimize predictive outcome models. Materials and Methods: Patients diagnosed from 1973 to 2009 were selected for analysis of socio-economic, staging and treatment factors available in the SEER database for MB and PNET. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (brain cancer specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A Monte Carlo algorithm was used to estimate the modeling errors. Results: There were 3,702 patients included in this study. The mean follow up time (S.D.) was 73.7 (86.2) months. Some 40% of the patients were female and the mean (S.D.) age was 16.5 (16.6) years. There were more adult MB/PNET patients listed from SEER data than pediatric and young adult patients. Only 12% of patients were staged. The SEER staging has the highest ROC (S.D.) area of 0.55 (0.05) among the factors tested. We simplified the 3-layered risk levels (local, regional, distant) to a simpler non-metastatic (I and II) versus metastatic (III) model. The ROC area (S.D.) of the 2-tiered model was 0.57 (0.04). Conclusions: ROC analysis optimized the most predictive SEER staging model. The high under staging rate may have prevented patients from selecting definitive radiotherapy after surgery.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4143-4145
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• 2014

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) Ewing sarcoma (ES) outcome data. The aim of this study was to identify and optimize ES-specific survival prediction models and sources of survival disparities. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ES. 1844 patients diagnosed between 1973-2009 were used for this study. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome (bone and joint specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: The mean follow up time (S.D.) was 74.48 (89.66) months. 36% of the patients were female. The mean (S.D.) age was 18.7 (12) years. The SEER staging has the highest ROC (S.D.) area of 0.616 (0.032) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged) to a simpler non-metastatic (I and II) versus metastatic (III) versus un-staged model. The ROC area (S.D.) of the 3-tiered model was 0.612 (0.008). Several other biologic factors were also predictive of ES-specific survival, but not the socio-economic factors tested here. Conclusions: ROC analysis measured and optimized the performance of ES survival prediction models. Optimized models will provide a more efficient way to stratify patients for clinical trials.