• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.9 s.102
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• pp.1108-1117
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• 2005

A robust damage detection method using short-time Fourier transform and beating phenomena is presented as an estimating tool of the healthiness of large structures. The present technique makes use of beating phenomena that manifest themselves when two signals of similar frequencies are added or subtracted. Unlike most existing methods based on vibration signals, the present approach does not require an analytic model for target structures. Furthermore, the main advantage of the proposed method compared to the competing diagnostic method using vibration data is its robustness. The proposed method is not affected by the amplitude of exciting signals and the location of exciting points. From a measuring view point. the location of sensing point have no influence on the performance of the present method. With a view to verifying the effectiveness of this method. a series of experiments are made and the results show its possibility as a robust damage diagnostic method.

• Proceedings of the Korea Water Resources Association Conference
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• 1996.05a
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• pp.683-688
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• 1996

The purpose of this study is to find seasonal variation of the water circulation in the Chenbuk coastal sea region. Chenbuk coastal sea is investigated with use of a robust diagnostic numerical model. Water circulations in four seasons are calculated diagnostically from the observed water temperature and salinity data and wind data from Kunsan mereorologcal station.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.306-319
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• 2008

Reaching fault tolerance in technological systems requires to detect malfunctions. This paper presents a diagnostic method that is robust with respect to unknown-but-bounded uncertainties of the dynamical model and the measurements. By using models of the faultless and the faulty behaviours, a state-set observer computes polyhedral sets from which the consistency of the models with the interval measurements is determined. The diagnostic result is proven to be complete, i.e., the set of faults obtained by the diagnostic algorithm includes the actual fault. The algorithm is illustrated by an application example.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.1-16
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• 2004

Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis-Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach's effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.130-130
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control lot the actuator and sensor faults in the closed-loop systems affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the residual set generation by using robust Parity space approach. Residual set is evaluated through the threshold test and then fault is isolated according to the decision logic table. Once the fault diagnosis module indicates which actuator or sensor is faulty, the fault magnitude is estimated by using the disturbance-decoupled optimal state estimation and a new additive control law is added to the nominal one to override the fault effect on the system. Simulation results show that the method has definite fault diagnosis and fault tolerant control ability against actuator and sensor faults.

• Nuclear Engineering and Technology
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• v.47 no.5
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• pp.624-632
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• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• Research in Plant Disease
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• v.27 no.1
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• pp.38-44
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• 2021

Xylella fastidiosa is the most damaging pathogen in many parts of the world. To increase diagnostic capability of X. fastidiosa in the field, the loop-mediated isothermal amplification (LAMP) and polymerase chain reaction (PCR) assay were developed to mqsA gene of citrate-synthase (XF 1535) X. fastidiosa and evaluated for specificity and sensitivity. Both assays were more robust than current published tests for detection of X. fastidiosa when screened against 16 isolates representing the four major subgroups of the bacterium from a range of host species. No cross reaction with DNA from healthy hosts or other species of bacteria has been observed. The LAMP and PCR assays could detect 10-4 pmol and 100 copies of the gene, respectively. Hydroxynaphthol blue was evaluated as an endpoint detection method for LAMP. There was a significant color shift that signaled the existence of the bacterium when at least 100 copies of the target template were present.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.127-132
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• 2019

Influenza is an infectious disease caused by an influenza virus with symptoms of high fever and headache. Since influenza especially mutates into multiple subtypes in the carrier's body, it is a serious threat for mankind such as Spanish influenza. The treatment of influenza infection mandates the use of antiviral drugs through rapid diagnostic test. Generally, immunochromatography-based rapid influenza diagnostic tests are used for rapid diagnosis in an emergency. In this paper, we propose an influenza analysis algorithm based on image processing to examine a large number of patients suspected of being infected with influenza. Also, we propose a robust influenza analysis algorithm based on the joint cumulative mass function under varying radiometric conditions such as illuminant and exposure differences. Simulation results show that the proposed algorithm significantly reduces the error of influenza diagnosis under different radiometric conditions.

• Journal of Korean Academy of Nursing
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• v.29 no.5
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• pp.1030-1041
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• 1999

This study examined those problems noticed under the Asymptotically Generalized Least Squares estimator in evaluating a structural model of physical health. The problems were highly correlated parameter estimates and high standard errors of some parameter estimates. Separate analyses of the endogenous part of the model and of the metric of a latent factor revealed a highly skewed and kurtotic measurement indicator as the focal point of the manifested problems. Since the sample sizes are far below that needed to produce adequate AGLS estimates in the given modeling conditions, the adequacy of the Maximum Likelihood estimator is further examined with the robust statistics and the bootstrap method. These methods demonstrated that the ML methods were unbiased and statistical decisions based upon the ML standard errors remained almost the same. Suggestions are made for future studies adopting structural equation modeling technique in terms of selecting of a reference indicator and adopting those statistics corrected for nonormality.