• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.257-262
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• 1996

When we are estimate the smoothing parameter in spline regression model, we deal the diagnostic of influence observations as posteriori analysis. When we use Generalized Maximum Likelihood Function as the estimation method of smoothing parameter, we propose the diagnostic measure for influencial observations in the obtained estimate, and we introduce the finding method of the proper smoothing parameter estimate.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.266-276
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• 1995

We have considered the study of local influence for smoothing parameter estimates in nonparametric regression model. Practically, generalized cross validation(GCV) does not work well in the presence of data perturbation. Thus we have proposed local influence measures for GCV estimates and examined effects of diagnostic by above measures.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.63-71
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• 1995

We have consider the study of local influence for smoothing parameter estimates in spline regression model with heteroscedasticity. Practically, generalized cross-validation does not work well in the presence of heteroscedasticity. Thus we have proposed the local influence measure for generalized cross-validation estimates when errors are heteroscedastic. And we have examined effects of diagnostic by above measures through Hyperinflation data.

• Journal of radiological science and technology
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• v.16 no.1
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• pp.101-106
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• 1993

In order to assure safety of both patient and operator, and to provide uniform quality radiographs, it is necessary to perform periodic calibration of diagnostic X-ray equipment. A basic parameter of diagnostic equipment's and its image sharpness is the size(and shape the energy distribution) of the focal spot as viewed along the central X-ray beam. This size determines the resolution possible with the equipment and also determines the heat characteristics of an anode. A fine focus tube gives high resolution but causes high local heating of target. In past, the pin-hole and star pattern image measurement for evaluation of resolution have been widely used, but it produced blurring and inaccuracy of image. So newly inverted Ug-meter has advantage in more convenient measurement method and less out-put bias than other image measurement. The authors intended to compare measured focal size between Ug-meter and focal spot test tool, changed state from setting to now of units.

• Journal of Veterinary Clinics
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• v.32 no.4
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• pp.319-323
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• 2015

The performance of diagnostic test accuracy is usually summarized by a variety of statistics such as sensitivity, specificity, predictive value, likelihood ratio, and kappa. These indices are most commonly presented when evaluations of competing diagnostic tests are reported, and it is of utmost importance to compare the accuracies of diagnostic tests to decide on the best available test for certain medical disorder. However, it is important to emphasize that specific point values of these indices are merely estimates. If parameter estimates are reported without a measure of uncertainty (precision), knowledgeable readers cannot know the range within which the true values of the indices are likely to lie. Therefore, when evaluations of diagnostic accuracy are reported the precision of estimates should be stated in parallel. To reflect the precision of any estimate of a diagnostic performance characteristic or of the difference between performance characteristics, the computation of confidential interval (CI), an indicator of precision, is widely used in medical literatures in that CIs are more informative to interpret test results than the simple point estimates. The majority of peer-reviewed journals usually require CIs to be specified for descriptive estimates, whereas domestic veterinary journals seem less vigilant on this issues. This paper describes how to calculate the indices and associated CIs using practical examples when assessing diagnostic test performance.

• International Journal of Air-Conditioning and Refrigeration
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• v.9 no.3
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• pp.63-71
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• 2001

This paper presents a model-based method for detecting and diagnosing some faults in the cooling tower of healing, ventilating, and air-conditioning systems. A simple model for the cooling tower is employed. Faults in cooling tower operation are detected through the deviations in the values of system characteristic parameters such as the heat transfer coefficient-area product, the tower approach, the tower effectiveness, and fan power. Three distinct faults are considered: cooling tower inlet water temperature sensor fault, cooling tower pump fault, and cooling tower fan fault. As a result, most values of the system characteristics parameter variations due to a fault are much higher or lower than the values without faults. This allows the faults in a cooling tower to be detected easily using above methods. The diagnostic rules for the faults were also developed through investigating the changes in the different parameter due to each faults.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• Journal of Periodontal and Implant Science
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• v.22 no.1
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• pp.198-198
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• 1992

• Imaging Science in Dentistry
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• v.46 no.4
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• pp.245-249
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• 2016

Purpose: This study was designed to evaluate differences in the required visibility of anatomic structures according to the diagnostic tasks of implant planning and periapical diagnosis. Materials and Methods: Images of a real skull phantom were acquired under 24 combinations of different exposure conditions in a cone-beam computed tomography scanner (60, 70, 80, 90, 100, and 110 kV and 4, 6, 8, and 10 mA). Five radiologists evaluated the visibility of anatomic structures and the image quality for diagnostic tasks using a 6-point scale. results: The visibility of the periodontal ligament space showed the closest association with the ability to use an image for periapical diagnosis in both jaws. The visibility of the sinus floor and canal wall showed the closest association with the ability to use an image for implant planning. Variations in tube voltage were associated with significant differences in image quality for all diagnostic tasks. However, tube current did not show significant associations with the ability to use an image for implant planning. conclusion: The required visibility of anatomic structures varied depending on the diagnostic task. Tube voltage was a more important exposure parameter for image quality than tube current. Different settings should be used for optimization and image quality evaluation depending on the diagnostic task.