• Journal of Ubiquitous Convergence Technology
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• 제2권2호
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• pp.105-110
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• 2008

The definition of the program control is introduced on the theory of the basis of the first integrals SDE system. That definition allows constructing the program control gives opportunity to stochastic system to remain on the given dynamic variety. The program control is considered in terms of dynamically invariant for stochastic process.

• 대한수학회지
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• 제33권2호
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• pp.227-234
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• 1996

Consider nonlinear autoregressive processes of order 1 defined by the random iteration $$ (1) X_{n + 1} = f(X_n) + \epsilon_{n + 1} (n \geq 0) $$ where f is real-valued Borel measurable functin on $R^1, {\epsilon_n : n \geq 1}$ is an i.i.d.sequence whose common distribution F has a non-zero absolutely continuous component with a positive density, $E$\mid$\epsilon_n$\mid$ < \infty$, and the initial $X_0$ is independent of ${\epsilon_n : n > \geq 1}$.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.95.1-95
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• 2001

This paper is concerned with a novel S-stability (stochastic-stability) concept in linear time-invariant stochastic systems, where a stochastic mode in dynamics depends on both the external disturbance and the inner-parameter variations. This leads to an EAG (eigenstructure assignment gaussian) problem; that is, the problem of associating S-eigenvalues (stochastic-eigenvalues), S-eigenvectors (stochastic-eigenvectors), and their PDFs (probability density functions) with the stochastic information of the systems with the required stochastic specifications. These results explicitly characterize how S-eigenvalues, S-eigenvectors and their PDFs in the complex plane may impose S-stability on stochastic systems.

• 충청수학회지
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• 제22권1호
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• pp.1-5
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• 2009

Let {$X_n,\;n{\geq}1}$ be a sequence of independent identically distributed(i.i.d.) sequence of positive random variables with common absolutely continuous distribution function(cdf) F(x) and probability density function(pdf) f(x) and $E(X^2)<{\infty}$. The random variables $\frac{X_i{\cdot}X_j}{(\Sigma^n_{k=1}X_k)^{2}}$ and $\Sigma^n_{k=1}X_k$ are independent for $1{\leq}i if and only if {$X_n,\;n{\geq}1}$ have gamma distribution.

• 대한수학회보
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• 제33권1호
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• pp.57-64
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• 1996

Let $(X, B, \mu)$ be a probability space. Then we say $\tau : X \to X$ is a measure-preserving transformation if $\mu(\tau^{-1} E) = \mu(E)$. and we call it an ergodic transformation if $\mu(\tau^{-1}E\DeltaE) = 0$ for a measurable subset E implies $\mu(E) = 0$. An equivalent definition is that constant functions are the only $\tau$-invariant functions.

• 제어로봇시스템학회논문지
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• 제6권11호
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• pp.974-980
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• 2000

In this paper, we present a method that has flexibility of exact assignment of eigenstructure with the stochastic robustness for LTI(Linear-Time-Invariant) systems. The stochastic robustness of LTI systems is determined by the probability distributions of closed-loop eigenvalues. The probabilistic stability region is presented stochastically using the Monte Carlo evaluations. The proposed scheme is applied to designing a simple system and a flight control system with stochastic parameter variations to confirm the usefulness of the scheme.

• 대한전자공학회논문지SP
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• 제43권4호
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• pp.87-95
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• 2006

본 논문에서는 얼굴 영상의 에너지 분포 특성을 이용한 새로운 특정추출 방법을 제안한다. 제안된 방법은 얼굴 영상의 에너지 확률과 에너지 랩을 이용해서 데이터 차원이 축소된 유효정보의 추출 및 유효정보의 LDA 해석에 기반을 둔다. 일반적으로, 얼굴 영상은 고유한 에너지 분포 특성을 가지고 있다. 그러나 기존의 많은 DCT 기반 방법들은 이러한 얼굴 영상의 특성을 효과적으로 이용하지 못하는 단점이 있다. 제안된 방법은 이러한 기존 방법의 단점을 개선하기 위해 다음의 3단계 방법을 사용한다. 먼저, DCT 도메인에서 얼굴의 에너지 확률 개념을 정의하고, 이러한 에너지 확률로부터 얼굴의 에너지 맵을 생성한다. 마지막으로, 에너지 확률 지도에 위치한 주파수 계수들에 대한 LDA 적용 및 해석을 통하여 특정 벡터 추출 및 인식을 수행한다. 제안된 방법은 ETRI 데이터베이스에서 96.8%, ORL 데이터베이스에서 100%의 인식률을 보인다. 실험을 통하여 인식 성능의 개선뿐만 아니라, 특정 벡터의 차원 축소에도 효과가 있음을 알 수 있다.

• 충청수학회지
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• 제27권2호
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• pp.157-163
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• 2014

Let {$X_i$, $1{\leq}i{\leq}n$} be a sequence of i.i.d. sequence of positive random variables with common absolutely continuous cumulative distribution function F(x) and probability density function f(x) and $E(X^2)$ < ${\infty}$. The random variables X + Y and $\frac{(X-Y)^2}{(X+Y)^2}$ are independent if and only if X and Y have gamma distributions. In addition, the random variables $S_n$ and $\frac{\sum_{i=1}^{m}(X_i)^2}{(S_n)^2}$ with $S_n=\sum_{i=1}^{n}X_i$ are independent for $1{\leq}m$ < n if and only if $X_i$ has gamma distribution for $i=1,{\cdots},n$.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.265-267
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• 2004

A blind separation problem in a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system with finite-alphabet inputs is considered. A discrete-time matrix equation model is used to describe the input-output relation of the system in order to make full use of the advantages of modern digital signal processing techniques. At first, ambiguity problem is investigated. Then, based on the results of the investigation, a new identifiability condition is proposed for the case of an input-data set which is widely used in digital communication. A probability bound such that an arbitrary input matrix satisfies the identifiability condition is derived. Finally, Monte-Carlo simulation is performed to demonstrate the validity of our suggestions.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.