• 동력기계공학회지
• /
• 제4권2호
• /
• pp.40-45
• /
• 2000

Some engineering materials are often known to have considerable spatial variation in their resisting strength and other properties. The objective of this study is to investigate the averaging effect and the applicability of extremal statistic for the statistical size effect. In the present study, it is assumed that the material property is a stationary random process in space. The theoretical autocorrelation function of the material strength are discussed for several correlation lengths. And, in order to investigate the statistical size effect, the material properties was simulated by using the non-Gaussian random process method. The material properties were plotted on the Weibull probability papers. The main results are summarized as follows: The autocorrelation function of the material properties are almost independent of the averaging length. The variance decreases with increasing the averaging length. As correlation length is smaller, the slope is larger. And also, it was found that Weibull statistics based on the weakest-link model could not explain the spatial variation of material properties with respect to the size effect satisfactory.

• 한국멀티미디어학회논문지
• /
• 제14권12호
• /
• pp.1544-1548
• /
• 2011

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

• 천문학논총
• /
• 제22권3호
• /
• pp.75-81
• /
• 2007

In this study we present basic principles and features of RVI2CELL, a precise RV (radial velocity) estimation program to process stellar spectra obtained through iodine cell. RVI2CELL is very robust and fast program. The instrument profile can be modeled as a sum of Gaussian functions or a non-parametric arbitrary shape. The RV accuracy estimated by observation of a RV standard star Tau Ceti indicates about 9 m/s.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 추계종합학술대회 논문집
• /
• pp.368-371
• /
• 1999

In this paper, we propose a neural network processor architecture with on-chip learning and with reconfigurability according to the data dependencies of the algorithm applied. For the neural network model applied, the proposed architecture can be configured into either SIMD or SRA(Systolic Ring Array) without my changing of on-chip configuration so as to obtain a high throughput. However, changing of system configuration can be controlled by user program. To process activation function, which needs amount of cycles to get its value, we design it by using PWL(Piece-Wise Linear) function approximation method. This unit has only single latency and the processing ability of non-linear function such as sigmoid gaussian function etc. And we verified the processing mechanism with EBP(Error Back-Propagation) model.

• 경영과학
• /
• 제13권3호
• /
• pp.105-113
• /
• 1996

Constructing an initial basis is an important process in the simplex method. An initial basis greatly affects the number of iterations of iterations and the execution time in the simplex method. The purpose of this paper is to construct a good initial basis. First, to avoid linear dependency among the chosen columns, an enhanced Gaussian elimination method and a method using non-duplicated nonzero elements are developed. Second, for an order to choose variables, the sparsity of the column is used. Experimenal results show that the proposed method can reduce the number of iterations and the execution time compared with Bixby's method by 12%.

• 수산해양기술연구
• /
• 제29권2호
• /
• pp.152-161
• /
• 1993

This paper deals with the effect of spatial distribution of material properties on its statistical characteristics and numerical estimation method of reliability of fatigue sensitive structures with respect to the fatigue crack growth. A method is proposed to determine experimentally the probability distribution functions of material parameters of Paris law. da/dN=C(ΔK/K sub(0) ) super(m), using stress intensity factor controlled fatigue tests. The result with a high tensile strength steel shows that the distribution of the parameter m is approximately normal and that of 1/C, is a 3-parameter Weibull. The main result obtained are : (1) The theoretical autocorrelation of the resistance, 1/C, to fatigue crack growth are almost same for different lengths. (2) The variance decreases with the increasing a averaging length. When spatial correlation length is very small. the variane decreases significantly were the averaging length. (3) The probability distribution of load cycles or the number for a crack to reach a certain length can be estimated using these functions by simulation of non-Gaussian(expecially Weibull) Stochastic Process.

• Communications for Statistical Applications and Methods
• /
• 제29권2호
• /
• pp.277-286
• /
• 2022

Due to the uniquely slow convergence speed of the EM algorithm, it suffers form a lot of processing time until the desired deconvolution image is obtained when the image is large. To cope with the problem, in this paper, an immediate solution of the EM algorithm is provided under the Gaussian image model. It is derived by finding the recurrent formular of the EM algorithm and then substituting the results repeatedly. In this paper, two types of immediate soultion of image deconboution by EM algorithm are provided, and both methods have been shown to work well. It is expected that it free the processing time of image deconvolution because it no longer requires an iterative process. Based on this, we can find the statistical properties of the restored image at specific iterates. We demonstrate the effectiveness of the proposed method through a simple experiment, and discuss future concerns.

• 천문학회보
• /
• 제44권2호
• /
• pp.59.2-59.2
• /
• 2019

In this talk, I will explain the implications of a rapid appearance of dark energy between the redshifts ($z$) of one and two on the expansion rate and growth of perturbations. Using both Gaussian process regression and a parametric model, I show that this is the preferred solution to the current set of low-redshift ($z<3$) distance measurements if $H_0=73~\rm km\,s^{-1}\,Mpc^{-1}$ to within 1\% and the high-redshift expansion history is unchanged from the $\Lambda$CDM inference by the Planck satellite. Dark energy was effectively non-existent around $z=2$, but its density is close to the $\Lambda$CDM model value today, with an equation of state greater than $-1$ at $z<0.5$. If sources of clustering other than matter are negligible, we show that this expansion history leads to slower growth of perturbations at $z<1$, compared to $\Lambda$CDM, that is measurable by upcoming surveys and can alleviate the $\sigma_8$ tension between the Planck CMB temperature and low-redshift probes of the large-scale structure.

• Journal of information and communication convergence engineering
• /
• 제11권2호
• /
• pp.95-102
• /
• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

• Korean Chemical Engineering Research
• /
• 제46권2호
• /
• pp.233-247
• /
• 2008

공정 모니터링 기술은 공정 내에서 일어나는 예상치 못한 조업변화 및 이상을 조기에 감지하고 조업 이상에 영향을 끼친 근본 원인을 밝혀내어 제거해 줌으로써 공정의 안정적인 조업과 양질의 제품생산의 기반을 제공하여 준다. 데이터에 기반한 통계적 공정 모니터링 방법은 양질의 공정 데이터만 주어진다면 통계적 처리를 접목하여 비교적 쉽게 모니터링을 할 수 있고 공정의 데이터 분석에 이용할 수 있는 도구를 얻을 수 있다는 장점이 있다. 그러나 실제 공정에서는 비선형성, non-Gaussianity, 다중 운전모드, 공정상태변화로 인해 기존의 다변량 통계적 방법을 이용한 공정 모니터링 기법은 비효율적이거나, 공정 감시 성능의 저하, 종종 신뢰할 수 없는 결과를 야기한다. 이러한 경우 기존의 방법으로는 더이상 공정을 정확히 감시할 수 없기 때문에 최근에 많은 새로운 방법들이 개발 되었다. 본 총설에서는 이러한 단점을 보안하기 위해 최근 주목할 만한 연구결과인 공정 비선형성을 고려한 커널주성분분석(kernel principle component analysis) 모니터링 기법, 주성분분석 모델 조합을 이용한 다중모델(mixture model) 모니터링 기법, 공정 변화를 고려한 적응모델(adaptive model) 모니터링 기법, 그리고 센서 이상진단과 보정의 이론과 응용결과에 대하여 소개한다.