• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• 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 Internet Computing and Services
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• v.17 no.2
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• pp.11-17
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• 2016

In this paper, the optimum weight of the algorithm based on the cross information-potential with the delta functions (CIPD) is derived and its robustness against impulsive noise is studied. From the analysis of the behavior of optimum weight, it is revealed that the magnitude controlling operation for input plays the main role of keeping optimum weight of CIPD stable from the impulsive noise. The simulation results show that the steady state weight of CIPD is equivalent to that of MSE criterion. Also in the simulation environment of impulsive noise, unlike the LMS algorithm based on MSE, the steady state weight of CIPD is shown to be kept stable.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.716-723
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• 2019

Fingerprint matching accuracy is essential in deploying a music search service. This paper deals with a method to improve fingerprint matching accuracy by utilizing an auxiliary information which is called power weight. Power weight is an expected robustness of each hash bit. While the previous power mask binarizes the expected robustness into strong and weak bits, the proposed method utilizes a real-valued function of the expected robustness as weights for fingerprint matching. As a countermeasure to the increased storage cost, we propose a compression method for the power weight which has strong temporal correlation. Experiments on the publicly-available music datasets confirmed that the proposed power weight is effective in improving fingerprint matching performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2073-2081
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• 2002

In product design and manufacturing, axiomatic design provides a systematic approach for the decision-making process. Two axioms have been defined such as the Independence Axiom and the Information Axiom. The Information Axiom states that the best design among those that satisfy the independence axiom is the one with the least information content. In other words, the best design is the one that has the highest probability of success. On the other hand, the Taguchi robust design is used in the two-step process; one is "reduce variability," and the other is "adjust the mean on the target." The two-step can be interpreted as a problem that has two FRs (functional requirements). Therefore, the Taguchi method should be used based on the satisfaction of the Independence Axiom. Common aspects exist between the Taguchi method and Axiomatic Design in that a robust design is induced. However, different characteristics are found as well. The Taguchi method does not have the design range, and the probability of success may not be enough to express robustness. Our purpose is to find the one that has the highest probability of success and the smallest variation. A new index is proposed to satisfy these conditions. The index is defined by multiplication of the robustness weight function and the probability density function. The robustness weight function has the maximum at the target value and zero at the boundary of the design range. The validity of the index is proved through various examples.gh various examples.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.486-489
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• 2002

This paper proposes a tabu search (TS) algorithm to optimal weight selection in design of robust H$_{\infty}$ power system stabilize. (PSS), In H$_{\infty}$ control design, the weight selection and the representation of system uncertainties are the major difficulties. To cope with these problems, TS is employed to automatically search for the optimal weights. On the other hand, the normalized coprime factorization (NCF) is used. The H$_{\infty}$ controller can be directly developed without ${\gamma}$-iteration. Also, the pole-zero cancellation phenomena are prevented. The performance and robustness of the proposed PSS under different loading conditions are investigated in comparison with a robust tuned PSS by examining the case of a single machine infinite bus (SMIB) system. The simulation results illustrate the effectiveness and robustness of the proposed PSS.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.153-160
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• 2004

In recent years, the size of data set which we usually handle is enormous, so a lot of outliers could be included in data set. Therefore the robust procedures that automatically handle outliers become very importance issue. We consider the robust estimation problem of location parameter in the univariate case. In this paper, we propose a new method for defining robustness weights for the weighted mean based on the median distance of observations and compare its performance with several existing robust estimators by a simulation study. It turns out that the proposed method is very competitive.

• ETRI Journal
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• v.16 no.1
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• pp.35-43
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• 1994

In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.