• 한국분말재료학회지
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• 제16권4호
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• pp.254-261
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• 2009

In this study, we investigated the unit process parameters in spherical $UO_2$ kernel preparation. Nearly perfect spherical $UO_3$ microspheres were obtained from the 0.6M of U-concentration in the broth solution, and the microstructure of the $UO_2$ kernel appeared the good results in the calcining, reducing, and sintering processes. For good sphericity, high density, suitable microstructure, and no-crack final $UO_2$ microspheres, the temperature control range in calcination process was $300{\sim}450^{\circ}C$, and the microstructure, the pore structure, and the density of $UO_2$ kernel could be controlled in this temperature range. Also, the concentration changes of the ageing solution in aging step were not effective factor in the gelation of the liquid droplets, but the temperature change of the ageing solution was very sensitive for the final ADU gel particles.

• 한국수학사학회지
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• 제19권3호
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• pp.113-122
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• 2006

본 논문에서는 오늘날 관심의 대상이 되고 있는 평활에 관한 수학적 배경, 통계적 방법론, 그리고 평활방법론이 적용될 수 있는 컴퓨터-소프트웨어 교육분야에 관하여 조사하고 논하고자 한다. 뿐만 아니라 수학과 통계를 기반으로 히스토그램, 커널밀도추정량, 적응커널추정량, 띠너비선택방법에 관한 개념과 방법론을 소개하고자한다.

• 대한수학회보
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• 제54권1호
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• pp.343-358
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• 2017

Let {$X_i$} be a sequence of stationary ${\alpha}-mixing$ random variables with probability density function f(x). The recursive kernel estimators of f(x) are defined by $$\hat{f}_n(x)={\frac{1}{n\sqrt{b_n}}{\sum_{j=1}^{n}}b_j{^{-\frac{1}{2}}K(\frac{x-X_j}{b_j})\;and\;{\tilde{f}}_n(x)={\frac{1}{n}}{\sum_{j=1}^{n}}{\frac{1}{b_j}}K(\frac{x-X_j}{b_j})$$, where 0 < $b_n{\rightarrow}0$ is bandwith and K is some kernel function. Under appropriate conditions, we establish the Berry-Esseen bounds for these estimators of f(x), which show the convergence rates of asymptotic normality of the estimators.

• 한국수자원학회논문집
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• 제30권6호
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• pp.641-648
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• 1997

상관계수가 변수간의 선형 상관관계를 나타내듯이 mutual information은 변수간의비선형 상관관계를 나타내준다. 본 논문에서는 mutual information 추정법으로 다변수 핵 미도함수(multivariate kernel density estimator)를 이용한 방법이 여러 time lags값에 대하여 산정 되었다. 많은 수문자료에서 보여지는 비선형 관계를 Mutual Information으로 확인하여 보았고, 또한 Mutual Information값이 거의 0인 점에서 optimal delay time을 구하여, 하나의 자료로부터 다변수 회귀분석 모델을 만들 때 이용할 수 있다.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2011년도 제43차 동계학술발표논문집 19권1호
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• pp.85-88
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• 2011

Fuzzy C-Means (FCM) 알고리듬은 가장 널리 사용되는 군집화 알고리듬 중 하나로 다양한 응용 분야에서 사용되고 있다. 하지만 FCM은 여러 가지 문제점을 가지고 있으며 초기 원형 설정이 그 중 하나이다. FCM은 국부 최적해에 수렴하므로 초기 원형 설정에 따라 클러스터링 결과가 달라진다. 이 논문에서는 이러한 FCM의 초기 원형 설정 문제를 개선하기 위하여 커널밀도 추정 (kernel density estimation) 기법을 활용하는 방법을 제안한다. 제안한 방법에서는 먼저 커널 밀도 추정을 수행한 후 밀도가 높은 지역에 클러스터의 초기 원형을 설정하고 원형이 설정된 영역의 밀도를 감소시키는 과정을 반복함으로써 효율적으로 초기 원형을 설정할 수 있다. 제안된 방법이 일반적으로 사용되는 무작위 초기화 방법에 비해 효율적이라는 사실은 실험결과를 통해 확인할 수 있다.

• ETRI Journal
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• 제34권5호
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• pp.771-774
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• 2012

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.

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

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.254-268
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• 2013

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

• ETRI Journal
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• 제38권3호
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• pp.579-588
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• 2016

To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.