• 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.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.123-131
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• 2009

The study decided the improvement depth of soft ground of deep mixing method through 2 and 3 dimension finite element method and following results were acquired. 1. 2 dimension analysis shows settlement 10% more estimated than 3 dimension analysis. 2. When the rate of replacement is under 5%, the settlement sharply increased. 3. The most economical design for the levee was decided 3.0m for width direction, 6.0m for length direction and 8.0m for improvement depth. 4. When the soft ground is developed through deep mixing method, the decision of improvement should be decided through 3 dimension analysis than 2 dimension analysis.

• Journal of the Korean Mathematical Society
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• v.36 no.3
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• pp.633-648
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• 1999

In this paper, for any two bipartite ordered sets P and Q, we define the convolution P * Q of P and Q. For dim(P)=s and dim(Q)=t, we prove that s+t-(U+V)-2 dim(P*Q) s+t-(U+V)+2, where U+V is the max-mn integer of the certain realizers. In particular, we also prove that dim(P)=n+k- {{{{ { n+k} over {3 } }}}} for 2 k n<2k and dim(Pn ,k)=n for n 2k, where Pn,k=Sn*Sk is the convolution of two standard ordered sets Sn and Sk.

• Journal of Fashion Business
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• v.22 no.1
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• pp.135-147
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• 2018

Since the 20th century, there has been a growing interest in the new concept of fractals, a combination of mathematics and art, and the attempt to study the creative spatial aspects of the concept is being made. The purpose of this research is to examine artistic characteristics of fractal dimension and then analyze the types of fractal dimensions expressed in the fashion. Previous literature on fractals and dimension, and visual data on art and fashion collected over the Internet were used for analysis. Fractal dimension refers to the spatial concept of structural dimension of geometrical self-similarity. An analysis of the types of fractals seen in fashion revealed spatial expansion, the repetition in continual figures, superposition accordant to different sizes, and shades of different shapes. The aesthetic characteristics of fractal dimension appearing in fashions were examined based on analyses of fractal dimension types; the inherent characteristics of self-similarity, superimposition, and atypicality were found. Results obtained from this study are expected to be used as basic materials for the application of the design of fractal dimension into various perspectives of fashion.

• The Journal of Korean Academy of Prosthodontics
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• v.30 no.1
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• pp.43-54
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• 1992

The purpose of this study was to obtain the guides of establishing vertical dimension by analyzing the facial and oral landmarks of Korean adults. The following conclusions were obtained from this study. 1. The ratio of bizygomatic width to dental arch width was 3.26 : 1 in male and 3.21 : 1 in female, and the ratio of vertical dimension to dental arch length was 2.49 : 1 in male and 2.39 : 1 in female. 2. It was obtained by analysis of multiple regression that the corelative formula, vertical dimension$=23.37+0.24{\times}bizygomatic$ $width+0.29{\times}dental$ arch length. 3. There was no statistically significant difference between the results of vertical dimension acquired by means of Hayakawa's prediction($68.04{\pm}3.16mm$ in male, $64.38{\pm}3.00mm$ in female) and that of vertical dimension by this study.

• Journal of Korea Water Resources Association
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• v.31 no.5
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• pp.587-597
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• 1998

Researchers have suggested that the fractal dimension of the stream length is uniform in all the streams of the basin and the estimates of the fractal dimension are in between 1.09 and 1.13 which may be considerably large values. In this study, the fractal dimension for the Ieemokjung subbasin streams in the Pyungchang River basin which is one of the IHP representative basins in Korea are estimated for each stream order using three scale maps of a 1/50,000, 1/25,000, and 1/5,000. As a result, the fractal dimension of the stream length is different by stream order and the fractal dimension of all streams shows a lower value in comparison to that of the previous studies. As a result of the fractal dimension estimation for the Ieemokjung subbasin streams, we found that the fractal dimension of the stream length shows different estimates in stream orders. The fractal dimension of 1st and 2nd order stream is 1.033, and the fractal dimension of 3rd and 4th order stream is 1.014. This result is different from the previous studies that the fractal dimension of the stream length is uniform in all streams of the basin. The fractal dimension for a whole stream length is about 1.027. Therefore, the previous estimates of 1.09 and 1.13 suggested as the fractal dimension of the stream length may be overestimated in comparison with estimated value in this study.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.30 no.2
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• pp.37-42
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• 2016

This paper proposes a method to develop a high-precision dimension measurement system using a linear variable differential transformer sensor. The Dimension targets for measurement is carbon material vanes of key element in the rotating parts within vehicle circulating pump. Data acquisition system for dimension measurement is designed using the NI Compact RIO. And the program applying the dimension measurement algorithm is built using NI LabVIEW. The dimension measuring program is composed of a FPGA program, Real Time program and Host program. The method of the experiment compares master vane with target vane for measure the length of the carbon material vane. The experimental results confirmed the usefulness of the accuracy within ${\pm}4um$.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.205-215
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• 2021

Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.