• IEIE Transactions on Smart Processing and Computing
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• 제3권5호
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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.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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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.

• 대한수의학회지
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• 제46권2호
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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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• 제24권4호
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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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• 제54권7호
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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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• 제56권2호
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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.

• 대한의용생체공학회:의공학회지
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• 제31권2호
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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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• 제19권6호
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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.

• 대한임상검사과학회지
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• 제50권1호
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• pp.20-28
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• 2018

결핵(TB)은 Mycobacterium tuberculosis (MTB) 복합체의 구성원에 의한 세균 감염 질병이다. 결핵은 전 세계 인구의 1/3이 감염된 것으로 알려져 있으며, 한국에서는 매년 약 4만 명의 새로운 결핵환자가 발생한다. 또한, 비결핵 항상균 감염이 증가하고 있는 추세이다. 전통적인 결핵 및 비결핵 항상균 진단방법은 세균 배양으로 3~4주 이상이 소요된다. 따라서, 신속하고 정확한 결핵균(TB) 및 비결핵 항상균(NTM) 진단법의 필요성이 요구되고 있다. 결핵균 및 비결핵 항상균을 구분하기 위하여, 전 세계적으로 다양한 진단 방법이 개발되고 있다. 특히, 결핵균과 비결핵 항상균을 신속하고 정확한 동정의 요구가 증가함에 따라, 정확하고 신속하게 진단하기 위한 체외 진단 방법이 개발 되고 있다. 그러나 현재 결핵과 비결핵 항상균에 대한 체외 진단 시약의 성능 평가는 부족한 실정이다. 최근 식약청은 결핵균 및 비결핵 항상균 체외 진단 시약에 대한 가이드 라인을 발표했다. 본 연구에서는, 미국 FDA에 승인을 받은 결핵균 및 비결핵 항산균에 대한 체외 진단 시약의 성능을 검토하였다. 이 검토는 결핵균 및 비결핵 항상균 체외 진단 시약 평가에 유용한 참고 자료가 될것으로 사료된다.

• 대한기계학회논문집
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• 제16권10호
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• pp.1833-1840
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• 1992

본 연구에서는 자동차의 발달에 따른 정비 전문가의 상대적인 능력 감퇴를 보 완하고 진단의 정확성을 높일 수 있도록 소음계측에 의한 인공 지능적 뉴로-퍼지 진단 기법을 연구하였다. 이를 위하여 진단결과에 영향을 미치는 많은 작용변수와 다양한 차량상태 등을 고려함으로서 보다 신뢰성 있는 결과를 산출해내기 위한 퍼지(fuzzy) 추론 방식의 판단법을 도입하였으며, 진단이 실패했을 경우나 입력된 데이터가 충분하 지 못할 경우에 시스템 자체의 지식을 확장시켜 나갈 수 있도록 해밍네트(hamming net )에 의한 패턴인식 기법을 적용하였다. 그리고 컴퓨터 시뮬레이션과 자동차를 대상 으로 고장진단 실험을 실시하여 기존의 진단기법과의 비교를 통한 뉴로-퍼지 진단기법 의 효율성과 알고리즘의 타당성을 검증하였다.