• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.31-37
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• 1995

We consider density estimates of the usual type generated by a kernel function. By using the limit theorems for the maximum of normalized deviation of the estimate from its expected value, we propose to use data dependent bandwidth in the tests of goodness of fit based on these statistics. Also a small sample Monte Carlo simulation is conducted and proposed method is compared with Kolmogorov-Smirnov test.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.140-141
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• 2006

Most PM components are exposed to cyclic loading over long periods of time, yet, the fatigue performance is often at best characterized by a fully reversed bending strength. The effects of density, deviating loading modes, external notches or mean stresses must usually be estimated. The amount of available data is nowadays sufficient to come to fact-based estimates.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.408-413
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• 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).

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.231-242
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• 1995

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.325-328
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• 1996

In this paper, we proposed an estimation method of a posterior probability and PDF(Probability density function) using a feed forward neural network and code books of VQ(vector quantization). In this study, We estimates a posterior probability and probability density function, which compose a new parameter with well-known Mel cepstrum and verificate the performance for the five vowels taking from syllables by NN(neural network) and PNN(probabilistic neural network). In case of new parameter, showed the best result by probabilistic neural network and recognition rates are average 83.02%.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.1
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• pp.100-106
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• 2009

This paper deals with an estimation problem of the gain and phase imbalances between the in-phase and quadrature components in the quadrature receivers which are widely used in wireless communications. It is shown that the estimates derived from the suggested auto-calibration algorithm is asymptotically minimum-variance unbiased as a function of the sampling time. In order to show this characteristic, the probability density functions of the estimates for the gain and phase imbalances are derived first. Then the mean and variance functions are investigated analytically or numerically based on the density functions.

• Journal of The Korean Astronomical Society
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• v.22 no.1
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• pp.63-79
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• 1989

Current estimates, based on the same star-count analysis, of the distance to the globule Bamard 361 range from 300 pc to 650 pc. All the problems associated with the estimates have been fully rectified in this study, and a modification has been made to the classical Wolf diagram to improve the accuracy in the distance determination. A reference field was carefully selected close to the globule but well outside the globule boundary, and star counts for this field were performed on the blue POSS plate in order to set up the reference magnitude sequence appropriate to the general area of B 361. From the reference sequence, the stellar density function has been derived specifically for the direction toward the globule. Correction was made for the general interstellar extinction, and the luminosity function with the Wielen's dip was adopted. The resulting density function clearly reveals the existence of the local Cygnus-Orion arm in the direction of B 361 at about 700 pc away from the Sun. Analysis of the star-count data for the program field locates the globule at distance $600{\pm}50$ pc ; thus, the globule is an object located in the Cygnus-Orion arm, residing somewhat toward its leading edge.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5229-5232
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• 2014

Background: While many studies have concluded that local traffic density is positively associated with childhood leukemia, the results are inconsistent. We therefore performed a meta-analysis to assess the relationship between traffic density and the risk of childhood leukemia. Methods: A systematic literature review was carried out using PubMed, EMBASE, and the Cochrane Library from January 1979 to December 2013. We selected and assessed journal articles evaluating the relationship between local traffic density and the risk of leukemia in children. The analysis was carried out using STATA version 12.0. Results: A total of 11 articles, including 12 estimates of effect, were included in our meta-analysis. The summary effect size from the random-effects model, expressed as an odds ratio, was 1.03 (95% CI: 0.98-1.09, p=0.002). No significant association between traffic density and the risk of childhood leukemia was found. Similar conclusions were found on subgroup analysis. Conclusions: The results of our meta-analysis suggested no association between traffic density and the risk of childhood leukemia. This implies that living in close proximity to roads with heavy traffic may not increase the risk of childhood leukemia. However, further high-quality prospective trials are needed to support these results.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.6
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• pp.619-625
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• 2009

FastSLAM which uses the Rao-Blackwellized particle filter is one of the famous solutions to SLAM (Simultaneous Localization and Mapping) problem that estimates concurrently a robot's pose and surrounding environment. However, the particle depletion problem arises from the loss of the particle diversity in the resampling process of FastSLAM. Then, the performance of FastSLAM degenerates over the time. In this work, DIR (Density Information-based Resampling) technique is proposed to solve the particle depletion problem. First, the cluster is constructed based on the density of each particle, and the density of each cluster is computed. After that, the number of particles to be reserved in each cluster is determined using a linear method based on the distance between the highest density cluster and each cluster. Finally, the resampling process is performed by rejecting the particles which are not selected to be reserved in each cluster. The performance of the DIR proposed to solve the particle depletion problem in FastSLAM was verified in computer simulations, which significantly reduced both the RMS position error and the feature error.