• Journal of electromagnetic engineering and science
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• v.3 no.1
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• pp.57-61
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• 2003

The effects of high frequency noises on a perturbational inversion technique for a stratified dispersive medium are investigated in this paper. It is shown that the perturbational solution becomes unstable under high frequency noises. The physical origin of this instability is described. In order to enhance the robustness of the perturbational inverse scattering solution, a parametric inversion technique is introduced. The examples for the 2-pole and the 3-pole reflection coefficients are compared and contrasted, and improvement of the robustness of the solutions is shown.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.95-104
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• 2008

This paper addresses issues of robustness in Bayesian optimal design. We may have difficulty applying Bayesian optimal design principles because of the uncertainty of prior distribution. When there are several plausible prior distributions and the efficiency of a design depends on the unknown prior distribution, robustness with respect to misspecification of prior distribution is required. We suggest a new optimal design criterion which has relatively high efficiencies across the class of plausible prior distributions. The criterion is applied to the problem of estimating the turning point of a quadratic regression, and both analytic and numerical results are shown to demonstrate its robustness.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.213-217
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• 2003

In this paper, we analysize the noise robustness of MLPs(Multilayer perceptrons). Also, as a preprocessing stage of MLPs to improve noise robustness, we consider the ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise redunction effect using PCA or ICA, we verify the noise robustness of MLPs through handwritten-digit recognition simulations.

• Journal of the Korean Statistical Society
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• v.27 no.3
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• pp.331-348
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• 1998

In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

• Smart Structures and Systems
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• v.20 no.5
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• pp.637-640
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• 2017

The adoption of cladding panels as dissipation device is a sort of passive control "ante litteram" for residential and commercial buildings. This paper gives details on the current technology outlining the difference between buffer panels and cladding panels. The discussion of robustness and resilience of the resulting system is afforded. It is shown that the strength of such solution, originally related to economy and light weight, is mainly associated with the respect of the main robustness requisites, as well as the short time it requires for removal and replacement (resilience).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.997-1008
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• 2022

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.446-450
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• 1991

PID controller is popular but have defect inversing following reference input and noise elimination. Therefor, this paper focus on reducing rise time and robustness against noise. The result that is simulated with feedforward method and sliding mode show that rise time decrease and robustness increase.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.63-73
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• 1995

We consider some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean. The proposed estimators are compared with the sample mean and subjective Bayes estimators in terms of "posterior robustness" and "procedure robustness".re robustness".uot;.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.689-698
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• 2018

This study focuses on a novel procedure for the robustness assessment of reinforced concrete (RC) framed structures under threat-independent damage scenarios. The procedure is derived from coupled dynamic and non-linear static analyses. Two robustness indicators are defined and the method is applied to two RC frame buildings. The first building was designed for gravity load and earthquake resistance in accordance with Eurocode 8. The second was designed according to the tie force (TF) method, one of the design quantitative procedures for enhancing resistance to progressive collapse. In addition, in order to demonstrate the suitability and applicability of the TF method, the structural robustness and resistance to progressive collapse of the two designs is compared.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.363-368
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• 2004

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.