• 한국의류학회지
• /
• 제29권6호
• /
• pp.793-804
• /
• 2005

The new paradigm of the 21st century demand an openly different world of formative ideologies in respect to art and design. The purpose of this study is focused on trying to comprehend aesthetic essence of clothing as an, with the investigation of artistic theories manifested by art philosophers. Art to Wear was categorized into style to understand its artistic meaning as well as to analyze its character. Upon the foundation of semiotics theory, the feature of Art to Wear and its analysis category were argued in the context of Charles Morris three dimension of semiotics analysis. The conclusion to the research is like so. The feature and analysis category of Art to Wear upon a semiotics perspective was divided into syntactic dimension, semantic dimension and pragmatic dimension. The analytical categorization upon the perspective of syntactic dimension fell into the category of topology, shape and color. The semantic dimension of Art to Wear was divided into categories of denotation and connotation. In addition, the pragmatic dimension of Art to Wear analytical categorization was divided into a delivering function and common function.

• 한국주조공학회지
• /
• 제5권4호
• /
• pp.283-288
• /
• 1985

The melt of highly purified Zn was poured by top pouring process into the open green sand mold, that was made by using the 3-plane L-sections pattern. After skin was formed, the unsolified melt was poured out by rolling-over. The thicknesses of skin for each different of castings were investigated with one dimension. The results obtained and could be summerzed as follows: 1) While the 3-plant L-sections castings were solidifying in the mold, solidification blocks of different section modulus in the castings were formed, i.e. 1-dimension divergency block, 2-dimension heat divergency block, 3-dimension heat divergency block, 2-dimension heat convergency block, and 3-dimension heat convergency block. 2) When the chill plate was set up to the mold in order to change section modulus artificially, heat divergency blocks and heat convergency blocks according to the shape of chill plate were revealed.

• 한국정보기술응용학회:학술대회논문집
• /
• 한국정보기술응용학회 2005년도 6th 2005 International Conference on Computers, Communications and System
• /
• pp.69-72
• /
• 2005

As life style becomes to be complicated, lots of support technologies were developed. The bar code technology is one of them. It was renovating approach to goods industry. However, data storage ability in one dimension bar code came in limit because of industry growth. Two-dimension bar code was proposed to overcome one-dimension bar code. PDF417 bar code most commonly used in standard two-dimension bar codes is well defined at data decoding and error correction area. More works could be done in bar code image acquisition process. Applying various image enhancement algorithms, the recognition rate of PDF417 bar code is improved.

• Communications for Statistical Applications and Methods
• /
• 제22권3호
• /
• pp.285-294
• /
• 2015

A permutation test is the popular and attractive alternative to derive asymptotic distributions of dimension test statistics in sufficient dimension reduction methodologies; however, recent studies show that a bootstrapping technique also can be used. We consider two types of bootstrapping dimension determination, which are partial and whole bootstrapping procedures. Numerical studies compare the permutation test and the two bootstrapping procedures; subsequently, real data application is presented. Considering two additional bootstrapping procedures to the existing permutation test, one has more supporting evidence for the dimension estimation of the central subspace that allow it to be determined more convincingly.

• Tribology and Lubricants
• /
• 제18권2호
• /
• pp.147-152
• /
• 2002

The morphological analysis of wear particle is a very effective means fur machine condition monitoring and fault diagnosis. In order to describe morphology of various wear particle, the wear test was carried out under friction experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing. These descriptors to analyze shape and surface of wear particle are shape fractal dimension and surface fractal dimension. The boundary fractal dimension can be derived from the boundary profile and surface fractal dimension can be determined by sum of intensity difference of surface pixel. The morphology of wear particles can be effectively obtained by two fractal parameter.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
• /
• pp.857-860
• /
• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• 한국윤활학회:학술대회논문집
• /
• 한국윤활학회 2001년도 제33회 춘계학술대회 개최
• /
• pp.30-35
• /
• 2001

The morphological analysis of wear particle is a very effective means for machine condition monitoring and fault diagnosis. In order to describe morphology of various wear particle, the wear test was carried oui under friction experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing system. These descriptors to analyze shape and surface wear particle are share fractal dimension and surface fractal dimension. The boundry fractal dimension can be derived from the boundary profile and surface fractal dimension can be determined b)r sum of intensity difference of surface pixel. The morphology of wear particles can be effectively obtained by two fractal dimensions.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.721-733
• /
• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

• 한국수자원학회논문집
• /
• 제31권5호
• /
• pp.587-597
• /
• 1998

기존의 연구자들은 하천길이의 프랙탈 차원은 유역내 전체 하천에 대해 균일하며 그 수치도 1.09~1.13로 상당히 크게 보았다. 그렇지만 국제수문개발계획의 대표유역중 하나인 평창강수계내 이목정 소유역을 대상으로 1/50,000, 1/25,000, 1/5,000의 3개 축척 지형도를 이용하여 프랙탈 차원을 산정한 결과 하천차수별로 서로 다른 프랙탈 차원을 갖는 것을 발견하였고, 또한 전체 하천으로 보아도 기존 연구자들이 제안한 수치보다 작은 수치를 보였다. 이목적 소유역내 하천의 프랙탈 차원을 산정한 결과에 의하면 기존의 국내외의 연구가 전체 하천을 균일한 프랙탈 차원을 갖는 것으로 보는 것과 달리 1차, 2차 하천은 1.033, 이보다 하천차주가 높은 3차, 4차 하천을 1.014의 값을 보이는 등 하천차수별로 프랙탈 차원이 다르게 산정되었다. 또한 전체적인 하천길이에 대한 프랙탈 차원도 1.027로서 국내외에서 제시된 기존의 하천길이에 대한 프랙탈 차원인 1.09~1.13 사이의 수치는 실제보다 너무 과대평가된 것으로 추정된다.

• Communications for Statistical Applications and Methods
• /
• 제31권2호
• /
• pp.213-234
• /
• 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.