• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.313-318
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• 2014

Recently, Ethernet-based Diagnostic Over Internet Protocol (DoIP) was applied to automotive systems, and in-vehicle gateways have been introduced to integrate Ethernet with traditional in-vehicle networks, such as the local interconnect network (LIN), controller area network (CAN) and FlexRay. The introduction of in-vehicle gateways and of Ethernet based diagnostic protocols not only decreases the complexity of the networks, but also reduces the update time for ECU software reprogramming while enabling the use of a range of services, including remote diagnostics. In this paper, a diagnostic gateway was implement for an automotive system, and the performance measurements are presented. In addition, a range of applications provided by the diagnostic gateway are proposed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Korean Journal of Veterinary Research
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• v.46 no.2
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• pp.159-164
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• 2006

A stochastic simulation model incorporated with Reed-Frost approach was derived for evaluating diagnostic performance of a test used for a screening program of an infectious disease. The Reed-Frost model was used to characterize the within-herd spread of the disease using a hypothetical example. Specifically, simulation model was aimed to estimate the number infected animals in an infected herd, in which imperfect serologic tests are performed on samples taken from herds and to illustrate better interpreting survey results at herd-level when uncertainty inevitably exists. From a risk analysis point of view, model output could be appropriate in developing economic impact assessment models requiring probabilistic estimates of herd-level performance in susceptible populations. The authors emphasize the importance of knowing the herd-level diagnostic performance, especially in performing emergency surveys in which immediate control measures should be taken following the survey. In this context this model could be used in evaluating efficacy of a survey program and monitoring infection status in the area concerned.

• Journal of Hospice and Palliative Care
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• v.24 no.4
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• pp.214-225
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• 2021

Purpose: This study reviewed screening and assessment tools that are used to measure delirium in patients with cancer in hospice and palliative care settings and examined their psychometric properties. Methods: Four databases were searched for studies using related search terms (delirium, tools, palliative care, cancer, and others). The inclusion criteria were a) studies that included screening/assessment tools for measuring delirium in cancer patients receiving hospice/palliative care, and b) studies published in English or Korean. The exclusion criteria were a) studies that were conducted in an intensive care setting, and b) case studies, qualitative studies, systematic reviews, or meta-analyses. Results: Out of the 81 studies identified, only 10 examined the psychometric properties of tools for measuring delirium, and 8 tools were ultimately identified. The psychometric properties of the Memorial Delirium Assessment Scale (MDAS) were the most frequently examined (n=5), and the MDAS showed good reliability, concurrent validity, and diagnostic accuracy. The Delirium Rating Scale had good reliability and diagnostic accuracy. The Delirium Rating Scale-Revised 98 also showed good reliability and structural validity, but its diagnostic performance was not examined in hospice/palliative care settings. The Nursing Delirium Screening Scale showed relatively low diagnostic accuracy. Conclusion: The MDAS showed evidence of being a valid assessment tool for assessing delirium in patients with cancer in palliative care. Few studies examined the diagnostic performance of delirium tools. Therefore, further studies are needed to examine the diagnostic performance of screening/assessment tools for the optimal detection of delirium in patients with cancer in hospice/palliative care.

• Korean Circulation Journal
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• v.54 no.7
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• pp.382-394
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• 2024

Background and Objectives: Fractional flow reserve (FFR) is an invasive standard method to identify ischemia-causing coronary artery disease (CAD). With the advancement of technology, FFR can be noninvasively computed from coronary computed tomography angiography (CCTA). Recently, a novel simpler method has been developed to calculate onsite CCTA-derived FFR (CT-FFR) with a commercially available workstation. Methods: A total of 319 CAD patients who underwent CCTA, invasive coronary angiography, and FFR measurement were included. The primary outcome was the accuracy of CT-FFR for defining myocardial ischemia evaluated with an invasive FFR as a reference. The presence of ischemia was defined as FFR ≤0.80. Anatomical obstructive stenosis was defined as diameter stenosis on CCTA ≥50%, and the diagnostic performance of CT-FFR and CCTA stenosis for ischemia was compared. Results: Among participants (mean age 64.7±9.4 years, male 77.7%), mean FFR was 0.82±0.10, and 126 (39.5%) patients had an invasive FFR value of ≤0.80. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of CT-FFR were 80.6% (95% confidence interval [CI], 80.5-80.7%), 88.1% (95% CI, 82.4-93.7%), 75.6% (95% CI, 69.6-81.7%), 70.3% (95% CI, 63.1-77.4%), and 90.7% (95% CI, 86.2-95.2%), respectively. CT-FFR had higher diagnostic accuracy (80.6% vs. 59.1%, p<0.001) and discriminant ability (area under the curve from receiver operating characteristic curve 0.86 vs. 0.64, p<0.001), compared with anatomical obstructive stenosis on CCTA. Conclusions: This novel CT-FFR obtained from an on-site workstation demonstrated clinically acceptable diagnostic performance and provided better diagnostic accuracy and discriminant ability for identifying hemodynamically significant lesions than CCTA alone.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.123-128
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• 2010

The thermometers is widely used in diagnostic medical devices, and the safety and accurate performance of these devices are important in the diagnosis and monitoring of personal health. Especially, the accuracy of infra-red thermometer is highly emphasized. Here two typical thermometers are utilized for this purpose: the electronic thermometers measure body temperature by contacting to subject while infra-red thermometers measure by no contacting to subject. Therefore, the evaluating items of each thermometer are different, and the standard for each temperature is highly needed. But, there have been no international standards of each thermometer such as IEC. In this paper, we developed the standards of electronic and infra-red thermometer based on national standards such as KS, ASTM, EN, JIS and FDA guidance. The new standards focused on the safety and suitable performance for health care. This standards were applied to enact and revise the electronic medical device standards. So it can be applied to evaluate the safety and performance on technical file review. We predict that this standard will improve the quality of diagnostic medical devices (thermometers) and increase the international competitive power of domestic product.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1007-1020
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• 2018

Objective: To compare the diagnostic performance of cardiovascular magnetic resonance (CMR) myocardial perfusion at 1.5- and 3-tesla (T) for detecting significant coronary artery disease (CAD), with invasive coronary angiography (ICA) as the reference method. Materials and Methods: We prospectively enrolled 281 patients (age $62.4{\pm}8.3$ years, 193 men) with suspected or known CAD who had undergone 1.5T or 3T CMR and ICA. Two independent radiologists interpreted perfusion defects. With ICA as the reference standard, the diagnostic performance of 1.5T and 3T CMR for identifying significant CAD (${\geq}50%$ diameter reduction of the left main and ${\geq}70%$ diameter reduction of other epicardial arteries) was determined. Results: No differences were observed in baseline characteristics or prevalence of CAD and old myocardial infarction (MI) using 1.5T (n = 135) or 3T (n = 146) systems. Sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curve (AUC) for detecting significant CAD were similar between the 1.5T (84%, 64%, 74%, 76%, and 0.75 per patient and 68%, 83%, 66%, 84%, and 0.76 per vessel) and 3T (80%, 71%, 71%, 80%, and 0.76 per patient and 75%, 86%, 64%, 91%, and 0.81 per vessel) systems. In patients with multi-vessel CAD without old MI, the sensitivity, specificity, and AUC with 3T were greater than those with 1.5T on a per-vessel basis (71% vs. 36%, 92% vs. 69%, and 0.82 vs. 0.53, respectively). Conclusion: 3T CMR has similar diagnostic performance to 1.5T CMR in detecting significant CAD, except for higher diagnostic performance in patients with multi-vessel CAD without old MI.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.1
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• pp.20-28
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• 2018

Tuberculosis (TB) is a bacterial infection disease caused by members of the species Mycobacterium tuberculosis (MTB) complex. Approximately one third of the world's population is infected with TB. In Korea, approximately 40,000 new patients are identified each year. Moreover, infections from non-tuberculous mycobacteria (NTM) have also increased. In the diagnosis of TB and NTM, traditional bacterial cultures are required for 3 to 4 weeks. Therefore, rapid and accurate diagnostic tests for TB and NTM are needed. To distinguish between TB and NTM, a range of diagnostic methods have been developed worldwide. In vitro diagnostic assays are constantly being developed to meet the increasing need for the rapid and accurate identification for TB and NTM. On the other hand, the performance evaluations of in vitro diagnostic reagents for TB and NTM are lacking. Recently, the Korea Food and Drug Administration (KFDA) issued a guideline for in vitro diagnostic reagents for MTB and NTM. Here, this study analyzed the performance of currently developed in vitro diagnostic reagents for TB and NTM in the US FDA. This analysis of US FDA approved molecular assays could serve as a useful reference for an evaluation of the reagent performance of TB and NTM.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.10
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• pp.1833-1840
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• 1992

In the diagnostic process for automobile, Neuro-Fuzzy technique was compared with the conventional diagnostic method for the verification of performance, and proto-type system was developed. For the utilities of the system, 1/3 octave filter(band-pass filter) and A/D converter were used for data acquisition and then data were analyzed using octave band processing and pattern recognition using hamming network algorithm. In order to raise the reliability of the diagnostic results by considering many operating variables and condition of automobile to be diagnosed, fuzzy inference technique was applied in combining several information. The validation of this diagnostic system was examined through computer simulation and experiment, and it showed an acceptable performance for diagnostic process.