• Smart Structures and Systems
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• v.13 no.6
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• pp.975-991
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• 2014

The objective of this study is to develop a reliable method for locating cracks in a beam using data fusion of fractal dimension features of operating deflection shapes. The Katz's fractal dimension curve of an operating deflection shape is used as a basic feature of damage. Like most available damage features, the Katz's fractal dimension curve has a notable limitation in characterizing damage: it is unresponsive to damage near the nodes of structural deformation responses, e.g., operating deflection shapes. To address this limitation, data fusion of Katz's fractal dimension curves of various operating deflection shapes is used to create a sophisticated fractal damage feature, the 'overall Katz's fractal dimension curve'. This overall Katz's fractal dimension curve has the distinctive capability of overcoming the nodal effect of operating deflection shapes so that it maximizes responsiveness to damage and reliability of damage localization. The method is applied to the detection of damage in numerical and experimental cases of cantilever beams with single/multiple cracks, with high-resolution operating deflection shapes acquired by a scanning laser vibrometer. Results show that the overall Katz's fractal dimension curve can locate single/multiple cracks in beams with significantly improved accuracy and reliability in comparison to the existing method. Data fusion of fractal dimension features of operating deflection shapes provides a viable strategy for identifying damage in beam-type structures, with robustness against node effects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1140-1148
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• 1994

In this paper, we carried out some experiments on the Korean vowel recognition using the fractal dimension of the speech signals. We chose the Minkowski-Bouligand dimension as the fractal dimension, and computed it using the morphological covering method. For our experiments, we used both the fractal dimension and the LPC cepstrum which is conventionally known to be one of the best parameters for speech recognition, and examined the usefulness of the fractal dimension. From the vowel recognition experiments under various consonant contexts, we achieved the vowel recognition error rates of 5.6% and 3.2% for the case with only LPC cepstrum and that with both LPC cepstrum and the fractal dimension, respectively. The results indicate that the incorporation of the fractal dimension with LPC cepstrum gives more than 40% reduction in recognition errors, and indicates that the fractal dimension is a useful feature parameter for speech recognition.

• Bulletin of the Korean Mathematical Society
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• v.49 no.5
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• pp.1041-1055
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• 2012

The parameter lower (upper) distribution set corresponds to the cylindrical lower or upper local dimension set for a self-similarmeasure on a self-similar set satisfying the open set condition.

• Communications of the Korean Mathematical Society
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• v.16 no.1
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• pp.95-102
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• 2001

We consider a Cantor set with overlaps Λ in R$^1$. We calculate its correlation dimension with respect to the push-down measure on Λ comparing with its similarity dimension.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.364-367
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• 1994

In this paper, we carried out some experiments on the Korean vowel recognition using the fractal dimension of the speech signals. We chose the Mincowski-Bouligand dimensioni as the fractal dimension, and computed it using the morphological covering method. For our experiments, we used both the fractal dimension and the LPC cepstrum which is conventionally known to be one of the best parameters for speech recognition, and examined the usefulness of the fractal dimension. From the vowel recognition experiments under various consonant contexts, we achieved the vowel recognition error rats of 5.6% and 3.2% for the case with only LPC cepstrum and that with both LPC cepstrum and the fractal dimension, respectively. The results indicate that the incorporation of the fractal dimension with LPC cepstrum gies more than 40% reduction in recognition errors, and indicates that the fractal dimension is a useful feature parameter for speech recognition.

• Korean Journal of Mathematics
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• v.27 no.3
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• pp.563-580
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• 2019

In this paper, we study the strong metric dimension of zero-divisor graph ${\Gamma}(R)$ associated to a ring R. This is done by transforming the problem into a more well-known problem of finding the vertex cover number ${\alpha}(G)$ of a strong resolving graph $G_{sr}$. We find the strong metric dimension of zero-divisor graphs of the ring ${\mathbb{Z}}_n$ of integers modulo n and the ring of Gaussian integers ${\mathbb{Z}}_n$[i] modulo n. We obtain the bounds for strong metric dimension of zero-divisor graphs and we also discuss the strong metric dimension of the Cartesian product of graphs.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.533-535
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• 2006

In this paper, we experiment a face recognition rate of the shaded faces except to low dimension feature vectors; first, second, third dimension. It is known to robust the face recognition against illumination. But, it isn't obvious what is effect to recognition in terms of low dimension. We are analysis to the effect of low dimension(first, second, third dimension, and combination of these) under the shaded faces.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.105-117
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• 2016

In the paper, as a sequence of the first tutorial, we discuss sufficient dimension reduction methodologies used to estimate central subspace (sliced inverse regression, sliced average variance estimation), central mean subspace (ordinary least square, principal Hessian direction, iterative Hessian transformation), and central $k^{th}$-moment subspace (covariance method). Large-sample tests to determine the structural dimensions of the three target subspaces are well derived in most of the methodologies; however, a permutation test (which does not require large-sample distributions) is introduced. The test can be applied to the methodologies discussed in the paper. Theoretical relationships among the sufficient dimension reduction methodologies are also investigated and real data analysis is presented for illustration purposes. A seeded dimension reduction approach is then introduced for the methodologies to apply to large p small n regressions.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.267-279
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• 2021

A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.

• The Research Journal of the Costume Culture
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• v.11 no.2
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• pp.219-230
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• 2003

The purpose of this study was to analyze the structure of consumption emotions that consumers experienced in the process of consuming apparel products. Data was collected from 144 female college students living in Busan, and analyzed by salience, diversity, H-index, Clamor's V, and multi-dimensional scaling. The results showed as following; 1. The consumption emotions related to apparel products appeared three dimensions; ‘Relaxed-tense’ dimension, ‘Pleasant-unpleasant’ dimension, and ‘Outward-inward’ dimension. Considering elements of consumption system, the dimensions of consumption emotions in relation to apparel performances were 'Pleasant-unpleasant' and ‘Outward-inward’. The dimensions of consumption emotions experienced in usage situations were ‘Relaxed-tense’ and ‘pleasant-unpleasant’. The consumption emotions related to specific products were composed of ‘Pleasant-unpleasant’ dimension and ‘Outward-inward’ dimension. 2. As the multi-dimension map of this study has much space, it suggested that the scope of consumption emotions related to apparel products was more limited than those related to general situations and products. 3. The structure of consumption emotions in relation to apparel performances appeared to be bisected, while those related to usage situations showed relatively to be dispersed. 4. Although Pleasant-unpleasant dimension was consistent with results of prestudies, the dimensions of ‘Relaxed-tense’ and ‘Outward-inward’ were newly confirmed as the dimensions of consumption emotions related to apparel products. Therefore, consumer's consumption emotions of apparel products were composed of three dimensions, tended to be more limited than those of general consumption situations and products, and differentiated across apparel performances, usage situation, and specific products.