• Clinical and Experimental Pediatrics
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• 제63권3호
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• pp.98-99
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• 2020

• Clinical and Experimental Pediatrics
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• 제62권12호
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• pp.442-443
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• 2019

• Structural Engineering and Mechanics
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• 제16권5호
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• pp.581-595
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• 2003

Since the conventional direct approaches are hard to be applied for damage diagnosis of complex large-scale structures, a two-step approach for diagnosing the joint damage of framed structures is presented in this paper by using artificial neural networks. The first step is to judge the damaged areas of a structure, which is divided into several sub-areas, using probabilistic neural networks with natural Frequencies Shift Ratio inputs. The next step is to diagnose the exact damage locations and extents by using the Radial Basis Function (RBF) neural network with the second Element End Strain Mode of the damaged sub-area input. The results of numerical simulation show that the proposed approach could diagnose the joint damage of framed structures induced by earthquake action effectively and has reliable anti-jamming abilities.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제17권2호
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• pp.74-79
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• 2014

Food protein-induced enterocolitis syndrome (FPIES) is an under-recognized non-IgE-mediated gastrointestinal food allergy. The diagnosis of FPIES is based on clinical history, sequential symptoms and the timing, after excluding other possible causes. It is definitively diagnosed by an oral food challenge test. Unfortunately, the diagnosis of FPIES is frequently delayed because of non-specific symptoms and insufficient definitive diagnostic biomarkers. FPIES is not well recognized by clinicians; the affected infants are often mismanaged as having viral gastroenteritis, food poisoning, sepsis, or a surgical disease. Familiarity with the clinical features of FPIES and awareness of the indexes of suspicion for FPIES are important to diagnose FPIES. Understanding the recently defined clinical terms and types of FPIES is mandatory to suspect and correctly diagnose FPIES. The aim of this review is to provide a case-driven presentation as a guide of how to recognize the clinical features of FPIES to improve diagnosis and management of patients with FPIES.

• International journal of advanced smart convergence
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• 제2권2호
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• pp.36-39
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• 2013

It is becoming more important as the growing of health information and increasing in cancer patients diagnose over the time gradually. Among the various types of cancer, we focuses on breast cancer diagnosis. The accuracy of breast cancer diagnosis is increasing when the diagnosis is based on evidence and statistics. To do this we use the weka data mining tools and analysis algorithms significantly associated with the decision tree uses rules. In addition, the data pre-processing and cross-validation are used to increase the reliability of the results. The number and cause of the disease becomes important to increase evidence-based medical doctors. As the evidence-based medical, the data obtained from patients in the past through the disease by calculating the probability for future patients to diagnose and predict disease and treatment plan. It can be found by improving the survival rate plays an important role.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.110-115
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• 2004

The hybrid fault diagnosis method based on a combination of the signed digraph (SDG) and the partial least-squares (PLS) has the advantage of improving the diagnosis resolution, accuracy and reliability, compared to those of previous qualitative methods, and of enhancing the ability to diagnose multiple fault. In this study, the method is applied for the multiple fault diagnosis of the Tennessee Eastman challenge process, which is a realistic industrial process for evaluating process contol and monitoring methods. The process is decomposed using the local qualitative relationships of each measured variable. Dynamic PLS (DPLS) model is built to estimate each measured variable, which is then compared with the estimated value in order to diagnose the fault. Through case studies of 15 single faults and 44 double faults, the proposed method demonstrated a good diagnosis capability compared with previous statistical methods.

• 한국생산제조학회지
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• 제21권4호
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• pp.666-671
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• 2012

It was diagnosed customer transformers and other equipment by using non-contact infrared equipment. Examiners can identify not only abnormalities in real time in the field of the transformer immediately, as well as they were able to safely perform the scan. Thanks to successful transformer diagnosis, we can easily diagnose abnormalities of transformer itself which can be caused by deterioration of the oil used as overlaod or refrigerant and we can also diagnose abnomalities from low voltage bushing which can be result by external environmental factors and physical factors and abnomalities from its connections at the same time we found it is very useful at proactive diagnostics.

• 센서학회지
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• 제26권2호
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• pp.128-134
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• 2017

Until now, neuropsychological tests can diagnose the brain dysfunction, however, cannot distinguish the objective data of experiment enough to distinguish the relationships between brain dysfunction and cerebropathia. In this paper, an automatic cognitive test equipment system with 6-axis motion sensors was proposed for the automation of neuropsychological tests. Fist-Edge-Palm(FEP) test and Go-no go test were used to evaluate motor programming of frontal lobe. The motion data from the specially designed motion glove are transmitted wirelessly to a computer to detect the gestures automatically. The healthy 20 and 11 persons are investigated for the FEP and Go-No go test, respectively. The recognition rates of gestures of FEP and Go-No go test are min. 91.38% and 89.09%. In conclusion, the automations of cognitive tests are successful to diagnose the brain diagnostics quantitatively.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.1386-1390
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• 2007

This paper presents the equipment condition monitoring technology for the process or the equipment using statistical techniques. The equipment condition monitoring system consists of an empirical model to estimate the expected sensor values of process variables and a diagnose model to detect the abnormal condition and to identify the root source of the problem. The empirical model is constructed by the analysis of historic data. The diagnose model uses the sequential probability ratio test (SPRT) technique. The monitoring system was tested with real operating data acquired from the Reactor Coolant Pump Seal in the Nuclear Power Plant. It can detect the system degradation or failure at the early stage since it is able to catch the subtle deviation of process variables from normal condition.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.770-773
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• 2007

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, it presents a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.