• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.205-210
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• 1996

The bootstrap procedure is suggested as a useful method for point and interval estimation of system availability. Its validity and robustness has been shown in special, but representative case, by various sampling experiments. Alternative to the bootstrap suggest themselves e.g. a variation of the 'F'technique, but remain to be evaluated, as do variations on the bootstrap itself.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.185-189
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• 1987

The controller with robustness described in this paper is designed for processes with variable time-delays. This adaptive mechanism includes servo and stabilizing compensators. In the proposed multivariable controller. knowledge of the system time-delay is not required.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1996.10a
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• pp.205-210
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• 1996

The bootstrap procedure is suggested as a useful method for point and interval estimation of system availability . Its validity and robustness has been shown in special , but representative case, by various sampling experiments. Alternative to the bootstrap suggest themselves (e.g. a variation of the 'F' technique, but remain to be evaluated, as do variations on the bootstrap itself.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.92-101
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• 2010

Recently, the magnetic bearings which have many advantages such as no noise, less mechanical friction are widely applied to the suspension of rotors on the rotary machineries. However, the magnetic bearing system is inherently unstable, nonlinear and MIMO(multi-input-multi-output) system as well. In this paper, we design a state feedback controller using linear matrix inequality(LMI) to the multi-objective synthesis, for the magnetic bearing system with integral type servo system. The design objectives include $H_{\infty}$ performance, asymptotic disturbance rejection, and time-domain constraints on the closed-loop pole location. The results of computer simulation show the validity of the designed controller.

• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.10-26
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• 1987

If the forecasts from different, sources are combined in some way, the resulting forecasts may be more accurate than any of the individual components. In this paper, the established procedures of combining forecasts are reviewed and the alternative procedures are suggested. By the results of empirical analysis from survey data, the method of combining forecasts using the restricted regression weights, the restricted robust regression weights, and mixed regression weights are robust. We can not find the most efficient combined forecasts in any case if we select the corresponding decision by preliminary analysis for the statistical properties of individual dorecasts, our results of combined forecast can became useful.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.463-470
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• 2016

In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.549-559
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• 2015

The MDPDE is an attractive alternative to maximum likelihood estimator because of the strong robustness properties that it inherently possess. The characteristics of MDPDE can be varied with the tuning parameter, in general, there is a trade-off between robustness and asymptotic efficiency. Hence, selection of optimal tuning parameter is important but complicated task. In this study, we introduce two optimal tuning parameter selection methods proposed by Fujisawa and Eguchi (2005) and Warwick (2006). Through simulation study, we found out that Warwick's method yields excessively small optimal tuning parameter in certain cases while Fujisawa and Eguchi's method performs well. Therefore, we think Fujisawa and Eguchi's method can be used commonly for finding optimal tuning parameter of MDPDE.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.347-357
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• 2004

It is very important to select a split variable in constructing the classification tree. The efficiency of a classification tree algorithm can be evaluated by the variable selection bias and the variable selection power. The C4.5 has largely biased variable selection due to the influence of many distinct values in variable selection and the QUEST has low variable selection power when a continuous predictor variable doesn't deviate from normal distribution. In this thesis, we propose the SRT algorithm which overcomes the drawback of the C4.5 and the QUEST. Simulations were performed to compare the SRT with the C4.5 and the QUEST. As a result, the SRT is characterized with low biased variable selection and robust variable selection power.

• Journal of Digital Contents Society
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• v.17 no.3
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• pp.173-180
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• 2016

In this paper, a design of transceiver module for real-time wire and wireless robust integrated security system to solve the problem of conventional security system and its transceiver module is proposed. The presented robust integrated security system is designed with RF control unit and wireless transceiver module. A RF controller in transceiver module works as a low-power RF transceiver system. It is designed to use specific bandwidth stored in registers and manipulate RF power of transceiver by accessing the random values of registers. Operation algorithm for RF transceiver module is also presented. The designed transceiver module and the operation algorithm are implemented and verified by experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.513-515
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• 2022

Manufacturing data collected using IoT devices in a smart factory environment is generally reliable except for noises caused by external factors. However, unlike manufacturing data that is collected mechanically, process data manually recorded by field-workers can cause problems such as the misspelled entries or the missing entries. Therefore, process data must be validated before being used as training data for artificial intelligence models. In this paper, based on the fact that which is a linear relationship between the power consumption of the MCT machine and the production of the product recorded by the field-workers, we detect anomalous records of the workers using robust linear regression.