• 한국멀티미디어학회논문지
• /
• 제13권4호
• /
• pp.535-546
• /
• 2010

디지털 영상은 조명 조건과 취득 카메라의 고유 특성으로 인해 의도하지 않은 색조를 가질 수 있다. 영상에 색조가 존재하면 일관된 색 정보의 인지 및 표현이 어렵기 때문에 별도의 색 보정 작업이 필요하다. 본 논문은 color by correlation을 사용한 학습 영상 선택, 후보 회색축 영역의 추출, 핵밀도 추정, 색조 제거의 4단계로 이루어진 색조 추출 및 제거 방법을 제안한다. 후보 회색축 영역 중 불명확한 회색축 영역을 핵밀도 추정을 이용하여 제거하였다. 후보 회색축 영역의 색 성분의 분포를 조사하여 색조 유무를 판단하고, 색조가 존재할 경우 색조 제거 작업을 통하여 색 항상성을 유지 시켰다. 실험을 통해 제안하는 방법이 gray world 방법, color by correlation 방법 보다 정확한 색조 추정이 가능함을 확인하였다.

• 전기전자학회논문지
• /
• 제28권1호
• /
• pp.6-13
• /
• 2024

본 논문에서는 비지도학습 모델인 오토인코더와 가우시안 커널 밀도 추정 함수를 이용하여 차량용 CAN 네트워크에서 비정상적인 데이터를 탐지하는 방안을 제안한다. 제안하는 오토인코더 모델은 정상 데이터에서 CAN 프레임의 ID만으로 학습시킨다. 이후 가우시안 커널 밀도 추정 함수를 이용하여 구한 최적의 프레임 개수와 손실 임계값을 가지는 모델을 사용하여 비정상 데이터를 효과적으로 탐지한다. DoS 공격, Gear 스푸핑 공격, RPM 스푸핑 공격, Fuzzy 공격 등 4가지 공격 데이터로 오토인코더 기반 IDS를 검증하였으며 성능을 평가하였다. 기존 비지도학습 기반 모델들과 비교했을 때 우수한 성능을 나타냈으며 모든 평가 지표에서 99% 이상의 성능을 나타냈다.

• International journal of advanced smart convergence
• /
• 제7권2호
• /
• pp.95-100
• /
• 2018

In this paper, we propose a binarized multi-scale module to accelerate the speed of the pose estimating deep neural network. Recently, deep learning is also used for fine-tuned tasks such as pose estimation. One of the best performing pose estimation methods is based on the usage of two neural networks where one computes the heat maps of the body parts and the other computes the part affinity fields between the body parts. However, the convolution filtering with a large kernel filter takes much time in this model. To accelerate the speed in this model, we propose to change the large kernel filters with binarized multi-scale modules. The large receptive field is captured by the multi-scale structure which also prevents the dropdown of the accuracy in the binarized module. The computation cost and number of parameters becomes small which results in increased speed performance.

• International Journal of Control, Automation, and Systems
• /
• 제6권1호
• /
• pp.109-118
• /
• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.408-413
• /
• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• Journal of the Korean Data and Information Science Society
• /
• 제24권3호
• /
• pp.625-636
• /
• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• Communications for Statistical Applications and Methods
• /
• 제21권2호
• /
• pp.115-124
• /
• 2014

In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum (Y) is randomly left truncated and is sampled if $Y{\geq}T$, where T is the truncation random variable with an unknown distribution. This unknown distribution is estimated with the Lynden-Bell estimator. In particular the normal approximation rate, by choice of the bandwidth, is shown to be close to $n^{-1/6}$ modulo logarithmic term. We have also investigated this normal approximation rate via a simulation study.

• Journal of the Korean Data and Information Science Society
• /
• 제14권2호
• /
• pp.247-255
• /
• 2003

Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제31권2호
• /
• pp.133-140
• /
• 1992

The problem of estimating a smooth distribution function F at a point $\tau$ based on randomly right censored data is treated under certain smoothness conditions on F . The asymptotic performance of a certain class of kernel estimators is compared to that of the Kap lan-Meier estimator of F($\tau$). It is shown that the .elative deficiency of the Kaplan-Meier estimate. of F($\tau$) with respect to the appropriately chosen kernel type estimate. tends to infinity as the sample size n increases to infinity. Strong uniform consistency and the weak convergence of the normalized process are also proved.

• Journal of the Korean Statistical Society
• /
• 제33권1호
• /
• pp.59-78
• /
• 2004

A regression function in generalized linear model may have a discontinuity/change point at unknown location. In order to estimate the location of the discontinuity point and its jump size, the strategy is to use a nonparametric approach based on one-sided kernel weighted local-likelihood functions. Weak convergences of the proposed estimators are established. The finite-sample performances of the proposed estimators with practical aspects are illustrated by simulated examples.