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

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

• 말소리와 음성과학
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• 제7권4호
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• pp.3-8
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• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).

• 수산해양교육연구
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• 제24권3호
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• pp.436-445
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• 2012

The study aims to analyze of factors influencing on the mentoring participation of college student for low-income children using Cooper's multiple lense. The multidimensional policy analysis model is composed of the normative dimension, structural dimension, constructive dimension, technological dimension. The results of the research are as follows. First, the education difference solution shows the meaningful positive relationship in the category of normative dimension. Second, the budget and support setup shows the meaningful positive relationship in the category of technological dimension. But other factors do not show the meaningful influence.

• 대한기계학회논문집B
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• 제26권1호
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• pp.18-26
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• 2002

With the introduction of Fractal notation, various fields of engineering adopted fractal notation to express characteristics of geometry involved and one of the most frequently applied areas was turbulence. With research on turbulence regarding the surface as fractal geometry, attempts to analyze turbulent premised flame as fractal geometry also attracted attention as a tool for modeling, for the flame surface can be viewed as fractal geometry. Experiments focused on disclosure of flame characteristics by measuring fractal parameters were done by researchers. But robust principle or theory can't be extracted. Only reported modeling efforts using fractal dimension is flame speed model by Gouldin. This model gives good predictions of flame speed in unstrained case but not in highly strained flame condition. In this research, approaches regarding fractal dimension of flame as one representative value is pointed out as a reason for the absence of robust model. And as an extort to establish robust modeling, Presents methods treating fractal dimension as statistical variable. From this approach flame characteristics reported by experiments such as Da effect on flame structure can be seen quantitatively and shows possibility of flame modeling using fractal parameters with statistical method. From this result more quantitative model can be derived.

• 한국농림기상학회지
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• 제14권4호
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• pp.269-276
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• 2012

작물모형 평가에 사용되거나 사용할 수 있는 9가지 지표를 소개하였으며 이들의 특징은 다음과 같다. efficiency of model (EF)와 index of agreement (d)은 dimension이 없고 관측수(n)에 의존적이지 않았으며, dimension에 대해서만 자유로운 것은 relative root mean square error (RRMSE), bias factor (Bf)와 accuracy factor (Af)이다. Root mean sqruar, mean error, mean absolute error들은 관측수와 dimension에 영향을 받기 때문에 판단 시 주의가 필요하다. 따라서 이들의 특징을 파악하여 목적에 맞게 모형의 성능을 파악하여야 한다.

• 전자공학회논문지CI
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• 제41권1호
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• pp.1-8
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• 2004

인터넷의 발달과 데이터베이스의 구축이 보편화됨에 따라 막대한 양의 데이터 속에서 의사 결정에 필요한 지식을 찾아내는 작업은 결코 쉬운 일이 아니다 본 논문에서는 대규모 데이터의 효율적인 분석을 위하여 지식의 탐사 이전에 데이터에 대한 축소 작업을 수행하기 위한 효과적인 차원 축소 전략에 의한 패턴분류 기법을 제안한다. 이를 위해 본 논문에서는 통계적학습 모형인 Support Vector Machine의 VC-dimension에 기반한 RBF 신경망 모형을 제안한다. 기존의 RBF 신경망 모형은 주로 퍼셉트론 모형의 전처리 작업만을 수행하지만 제안하는 신경망 모형은 VD-dimension과 연계한 독자적으로 데이터를 분석할 수 있는 능력을 갖춘 모형을 구축하고 이를 바탕으로 개체들을 정확한 레이블로 분류한다. 기계 학습 데이터를 이용하여 본 논문에서 제안하는 모형의 성능을 비교 평가한 결과 기존의 여러 분류 알고리즘에 비해 우수한 성능을 보임이 실험을 통해 확인되었다.

• Communications for Statistical Applications and Methods
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• 제22권3호
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• pp.305-312
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• 2015

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.

• 한국CDE학회논문집
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• 제11권3호
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• pp.172-182
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• 2006

This paper presents a method to convert 3D CAD model to an appropriate analysis model using wrap-around, smooth-out and thinning operators that have been originally developed to realize the multi-resolution modeling. Wrap-around and smooth-out operators are used to simplify 3D model, and thinning operator is to reduce the dimension of a target object with simultaneously decomposing the simplified 3D model to 1D or 2D shapes. By using the simplification and dimension-reduction operations in an appropriate way, the user can generate an analysis model that matches specific applications. The advantage of this method is that the user can create optimized analysis models of various simplification levels by selecting appropriate number of detailed features and removing them.

• 한국CDE학회논문집
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• 제1권2호
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• pp.97-106
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• 1996

Existing CAD systems do not provide advanced functions for automatic checking design and drafting errors in mechanical drawings. If the knowledge of checking in mechanical ddrsfting can be implemented into computers, CAD systems could automatically check for design and drafting errors. This paper describes a method for systematic checking of dimension errors. such as deficiency and/or redundancy of dimension input-errors in dimension figures and symbols, etc. The logic for finding dimensional errors is written by using a proccedural language. A geometric model and a topological-graph model are used in this method. Checking for deficiency and redundancy of dimensions is based upon graph Theory.