• 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.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.210-218
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• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• The Journal of the Korean life insurance medical association
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• v.7 no.1
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• pp.53-62
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• 1988

• Korean Journal of Radiology
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• v.25 no.1
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.563-570
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• 1998

The purpose of this study was to investigate the effects of scintillator and collimator parameters that tradeoff between system sensitivity and spatial resolution. The parameters simulated using Monte Carlo program were scintillator thickness, colimator hole shape, septal thickness, and hole length. The results show that the sensitivity increases exponentially upto about 1 cm of scintillator thickness as the thickness increases. However the sensitivity is almost constant when the scintiallator is thicker than about 1 cm. The simulation of collimator hole shape shows that the hexagonal hole gives the best spatial resolution for the same system sensitivity. The system statical resolution is improved, as both collimator septal thickness and hole length increase, however that system sensitivity is rapidly decreased. In conclusion, The optimization of scintillator and collimator parameters using monte carlo simulation may be useful to develop a high-resolution miniature gamma probe.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Progress in Superconductivity
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• v.8 no.1
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• pp.46-53
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• 2006

Magnetocardiography(MCG) has been proposed as a novel and non-invasive diagnostic tool for the detection of cardiac electrical abnormality associated with myocardial ischemia. In our previous study, we have proposed a new classification method of MCG parameters, based on the different populations of the parameters between coronary artery disease(CAD) patients, symptomatic patients and healthy volunteers. We used four parameters, representing the directional changes of the electrical activity in the period of an R-ST-T interval. In patients with chest pain and without ST-segment elevation, who were selected consecutively from all patients admitted to the hospital in 2004, the patients with CAD could be classified with a higher sensitivity than conventional methods, showing that the proposed method can be useful for the diagnosis of CAD with MCG. In this study, we examined the validity of the algorithm with the prior probability distribution in diagnosis of new patients admitted to the hospital in 2005. In the results, presence of CAD could be found with sensitivity and specificity of 81.3% and 71.4%, respectively, in patients with chest pain and non-diagnostic ECG findings.

• International journal of thyroidology
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• v.10 no.1
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• pp.14-23
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• 2017

Background and Objectives: To evaluate and compare the diagnostic performances of grayscale ultrasound (US) and quantitative parameters obtained from texture analysis of grayscale US and elastography images in evaluating patients with diffuse thyroid disease (DTD). Materials and Methods: From September to December 2012, 113 patients (mean age, $43.4{\pm}10.7years$) who had undergone preoperative staging US and elastography were included in this study. Assessment of the thyroid parenchyma for the diagnosis of DTD was made if US features suggestive of DTD were present. Nine histogram parameters were obtained from the grayscale US and elastography images, from which 'grayscale index' and 'elastography index' were calculated. Diagnostic performances of grayscale US, texture analysis using grayscale US and elastography were calculated and compared. Results: Of the 113 patients, 85 (75.2%) patients were negative for DTD and 28 (24.8%) were positive for DTD on pathology. The presence of US features suggestive of DTD showed significantly higher rates of DTD on pathology, 60.7% to 8.2% (p<0.001). Specificity, accuracy, and positive predictive value was highest in US features, 91.8%, 84.1%, and 87.6%, respectively (all ps<0.05). Grayscale index showed higher sensitivity and negative predictive value (NPV) than US features. All diagnostic performances were higher for grayscale index than the elastography index. Area under the curve of US features was the highest, 0.762, but without significant differences to grayscale index or mean of elastography (all ps>0.05). Conclusion: Diagnostic performances were the highest for grayscale US features in diagnosis of DTD. Grayscale index may be used as a complementary tool to US features for improving sensitivity and NPV.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1680-1689
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• 2021

Objective: To investigate the diagnostic yield of diffusion-weighted imaging (DWI) in patients with transient global amnesia (TGA) and identify significant parameters affecting diagnostic yield. Materials and Methods: A systematic literature search of the MEDLINE and EMBASE databases was conducted to identify studies that assessed the diagnostic yield of DWI in patients with TGA. The pooled diagnostic yield of DWI in patients with TGA was calculated using the DerSimonian-Laird random-effects model. Subgroup analyses were also performed of slice thickness, magnetic field strength, and interval between symptom onset and DWI. Results: Twenty-two original articles (1732 patients) were included. The pooled incidence of right, left, and bilateral hippocampal lesions was 37% (95% confidence interval [CI], 30-44%), 42% (95% CI, 39-46%), and 25% (95% CI, 20-30%) of all lesions, respectively. The pooled diagnostic yield of DWI in patients with TGA was 39% (95% CI, 27-52%). The Higgins I² statistic showed significant heterogeneity (I² = 95%). DWI with a slice thickness ≤ 3 mm showed a higher diagnostic yield than DWI with a slice thickness > 3 mm (pooled diagnostic yield: 63% [95% CI, 53-72%] vs. 26% [95% CI, 16-40%], p < 0.01). DWI performed at an interval between 24 and 96 hours after symptom onset showed a higher diagnostic yield (68% [95% CI, 57-78%], p < 0.01) than DWI performed within 24 hours (16% [95% CI, 7-34%]) or later than 96 hours (15% [95% CI, 8-26%]). There was no difference in the diagnostic yield between DWI performed using 3T vs. 1.5T (pooled diagnostic yield, 31% [95% CI, 25-38%] vs. 24% [95% CI, 14-37%], p = 0.31). Conclusion: The pooled diagnostic yield of DWI in TGA patients was 39%. DWI obtained with a slice thickness ≤ 3 mm or an interval between symptom onset and DWI of > 24 to 96 hours could increase the diagnostic yield.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.189-198
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• 1987

Digital Subtraction Angiography(DSA) technique has been widely used to detect vascular diseases and hemodynamic parameters noninvasively. However, there factors in fluencing the resultant DSA image quality. In this paper, several important factors are suggested to improve the DSA image quality based on mathematicical analysis. Experimental DSA images for different filters are shown and also dicussed the difference between original and processed image qualities.