• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.6
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• pp.1002-1015
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• 2018

In this study, we analyzed the results of virtual clothing simulation according to the difference in the lateral neck point as well as the front and back shoulder inclination angles of the bodice foundation. Lim's (2016) (S) and Lee's (1999) method (L) were selected as the different setting for the lateral neck point. S1, S2, L1 and L2 were developed by changing the shoulder inclination angles. The SND and LND were developed by removing the darts in the S and L, respectively; in addition, the SND1, SND2, LND1, and LND2 were developed with different shoulder inclination angles. The results of S and L were similar with only slight differences observed in the armhole shape. However, the results of SND and LND were very different. The patterns of the S series were similar to each other, but the patterns of the L series were different. In addition, the patterns of the SND and LND series could not find a similar trend.

• Bulletin of the Korean Mathematical Society
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• v.21 no.2
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• pp.127-135
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• 1984

In this paper, we find the Fourier series of X(t, .omega.).mem. $L^{2}$$_{s.a.p.}$ and the Parseval relation of X(t, .omega.).mem. $L^{2}$$_{s.a.p.}$. In section 2, we investigate some basic properties of X(t, .omega.).mem. $L^{2}$$_{s.a.p.}$ In section 3, we show that the mean of X(t, .omega.).mem. $L^{2}$$_{s.a.p.}$ exists and in section 4, after showing the existence of Fourier exponents and Fourier coefficients of X(t, .omega.).mem. $L^{2}$$_{s.a.p.}$. we give the Parseval relation of X(t, .omega.).mem. $L^{2}$$_{s.a.p.}$. For convenience we will denote X(t, .omega.) as X(t) in what follows.hat follows.

• Journal of the Korean Mathematical Society
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• v.54 no.4
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• pp.1301-1316
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• 2017

It is well known that every $x{\in}(0,1]$ can be expanded to an infinite $L{\ddot{u}}roth$ series with the form of $$x={\frac{1}{d_1(x)}}+{\cdots}+{\frac{1}{d_1(x)(d_1(x)-1){\cdots}d_{n-1}(x)(d_{n-1}(x)-1)d_n(x)}}+{{\cdots}}$$, where $d_n(x){\geq}2$ for all $n{\geq}1$. In this paper, the set of points with some restrictions on the digits in $L{\ddot{u}}roth$ series expansions are considered. Namely, the Hausdorff dimension of following the set $$F_{\phi}=\{x{\in}(0,1]\;:\;d_n(x){\geq}{\phi}(n),\;i.o.n}$$ is determined, where ${\phi}$ is an integer-valued function defined on ${\mathbb{N}}$, and ${\phi}(n){\rightarrow}{\infty}$ as $n{\rightarrow}{\infty}$.

• Elastomers and Composites
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• v.51 no.4
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• pp.269-274
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• 2016

Two different reactive silicone oligomers, hydroxylpropyl terminated PDMS (PDMS-L) and 1-(2,3-dihydroxy-propoxy)propyl terminated PDMS (PDMS-B), were synthesized through hydrosilylation reaction under Karstedt's catalyst. Using the reactive silicone oligomers, two different silicone-modified gel-coat series were prepared to investigate their thermal and mechanical properties as well as surface characteristics according to reactive silicone oligomer contents. In case of thermal stabilities and mechanical properties, the gel-coat series containing PDMS-B showed higher values than that of the gel-coat series containing the PDMS-L. The contact angle for the gel-coat containing both silicone oligomers/was increased with reactive silicone oligomer contents but there was similar surface tension between PDMS-L and PDMS-B.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.357-368
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• 2006

This paper discusses the way of processing time-series subsequence matching under time warping. Time warping enables finding sequences with similar patterns even when they are of different lengths. The prefix-querying method is the first index-based approach that performs time-series subsequence matching under time warping without false dismissals. This method employs the $L_{\infty}$ as a base distance function for allowing users to issue queries conveniently. In this paper, we extend the prefix-querying method for absorbing $L_1$, which is the most-widely used as a base distance function in time-series subsequence matching under time warping, instead of $L_{\infty}$. We also formally prove that the proposed method does not incur any false dismissals in the subsequence matching. To show the superiority of our method, we conduct performance evaluation via a variety of experiments. The results reveal that our method achieves significant performance improvement in orders of magnitude compared with previous methods.

• Journal for History of Mathematics
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• v.27 no.4
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• pp.285-297
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• 2014

G. H. Hardy laid the foundation of classical studies on double Fourier series at the beginning of the 20th century. In this paper we are concerned not only with Fourier series but more generally with trigonometric series. We consider Norlund means and Cesaro summation method for double Fourier Series. In section 2, we investigate the classical results on the summability and the convergence of double Fourier series from G. H. Hardy to P. Sjolin in the mid-20th century. This study concerns with the $L^1(T^2)$-convergence of double Fourier series fundamentally. In conclusion, there are the features of the classical results by comparing and reinterpreting the theorems about double Fourier series mutually.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.125-137
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• 1996

Time series data are often contaminated with outliers due to influence of unusal and non-responsitive events. The effect of the outliers is larger in the time series analysis than in the other statistical analysis, because the time series data have dependent structure over time. This paper focuses on the effect of patchy outliers on forecasting. Especially, the increase of the mean square of the l-step-ahead forecast error is derived and used to evaluate the impact of those outliers on the forecast. We fine, in general, that this increase is rather small, provided that the patchy outliers does not occur too close to the forecast origin.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.