• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.535-546
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• 2010

Digital images have undesired color casts due to various illumination conditions and intrinsic characteristics of cameras. Since the color casts in the images deteriorate performance of color representations, color correction is required for further analysis of images. In this paper, an algorithm for detection and removal of color casts is presented. The proposed algorithm consists of four steps: retrieving similar image using color by correlation, extraction of near neutral color regions, kernel density estimation, and removal of color casts. Ambiguities in near neutral color regions are excluded based on kernel density estimation by the color by correlation algorithm. The method determines whether there are color casts by chromaticity distributions in near neutral color regions, and removes color casts for color constancy. Experimental results suggest that the proposed method outperforms the gray world algorithm and the color by correlation algorithm.

• Journal of IKEEE
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• v.28 no.1
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• pp.6-13
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• 2024

This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of Forest and Environmental Science
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• v.33 no.3
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• pp.239-242
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• 2017

Although camera traps have been widely used to understand the abundance of wildlife in recent decades, the effort has been restricted to small sub-set of wildlife which can mark-and-recapture. The Random Encounter Model shows an alternative approach to estimate the absolute abundance from camera trap detection rate for any animals without the need for individual recognition. Our study aims to examine the feasibility and validity of the Random Encounter Model for the density estimation of endangered leopard cats (Prionailurus bengalensis) in Maekdo riparian park, Busan, South Korea. According to the model, the estimated leopard cat density was $1.76km^{-2}$ (CI 95%, 0.74-3.49), which indicated 2.46 leopard cats in $1.4km^2$ of our study area. This estimate was not statistically different from the previous leopard cat population count ($2.33{\pm}0.58$) in the same area. As follows, our research demonstrated the application and usefulness of the Random Encounter Model in density estimation of unmarked wildlife which helps to manage and protect the target species with a better understanding of their status.

• Journal of Nutrition and Health
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• v.28 no.4
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• pp.282-290
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• 1995

Subcutaneous fat thickness of 74 young male was measured at six sites(biceps, triceps, subscapula, suprailiac, abdomen, thigh). The present study compared caliper with ultrasound measurements for the estimation of body fat. Caliper measurements subcutaneous at six sites had higher coefficient variation than did the ultrasound measures. Caliper measurements subcutaneous at six sites had higher ratio (caliper/ultrasound) than did the ultrasound measures. Compared to other body sites, the correlations between caliper and ultrasound measurements were high for the subscapula(r=0.7327), abdomen(r=0.7355) and thigh (r=0.7207) sites. the correlations between caliper and ultrasound measurements were high for the suprailiac(r=0.6616) site by lean group. the correlations between caliper and ultrasound measurements were high for the abdomen(r=0.7636) site by normal group. The correlations between caliper and ultrasound measurements were high for the subscapula (r=0.8959) and abdomen(r=0.8237) sites by obese group. Ultrasound measurement of biceps(r=-0.4459), abdomen9r=-0.4469), thigh(r=-0.4348) had the highest correlation with body density. Caliper measurement of triceps(r=-0.4017), subscapula(r=-0.4454), abdomen(r=-0.4293) had the highest correlation with body density. Ultasound measurements subcutaneous fat at lean group, obese group had higher coefficients of correlation with body density than did the caliper measurement. Caliper measurements subcutaneous fat at normal group had higher coefficients of correlation with body density than did the ultrasound measures. Ultrasound showed to be superior to the caliper technique in measuring subcutaneous fat of obese persons.

• Clean Technology
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• v.29 no.4
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• pp.249-254
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• 2023

In this study, porosity estimation was conducted by the physical properties of zeolite. Because of the difficulty of directly measuring the porosity of particulate matter, the porosity was calculated by applying the measured physical properties of zeolite to the calculation formula presented in various literature. For this purpose, the average particle size, particle size distribution, specific surface area, and pore characteristics of three types of zeolite - zeolite beta, zeolite Y, and ZSM-5 - were measured. In addition, the true density using gas and liquid phases, and two types apparent density (tap and untapped density) were measured. We calculated the porosity using these results, compare and analyzed the results, and evaluated main factors that determine the porosity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2439-2450
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• 1997

As recording densities increase in digital magnetic recording channels, the performances of digital detection techniques such as PRML and DFE degrade significantly due to nonlinear distortion in recording channels. The primary impediments for hgih-density recording are generally classified as nonlinear transition shift, which can be reduced substantially by the precompensation technique, and partial erasure which usually requires sophisticated nonlinear equalization techniques. In order to acheieve the highest density recording, accurate estimation of the parameters associated with these two noninear distortions is crucial. In this paper, a new estimation technique to distinguish these two different nonlinear effect using a proposed adaptive algorithm in time domain is presented. The effectiveness of the proposed adaptive approach to identify uniquely the nonlinear parameter with bias is demonstrated by computer simulation.

• ETRI Journal
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• v.38 no.3
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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.

• Journal of Korea Game Society
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• v.8 no.3
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• pp.23-30
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• 2008

To strengthen the competitiveness of the game industry, one needs to develope a tool for evaluation of the satisfaction level of the game users, which leads to the improvement of the quality of games and increment of the amount of game trade. In this study, we developed factors to measure the satisfaction level and estimate their distributional properties. For the purpose, using a survey data for RPG games, we discussed several aspects for normalization of score distribution for satisfaction factors and estimated their density function by use of parametric density estimation of SAS/INSIGHT. We believe that the proposed results help us to predict or estimate the satisfaction level of newly developed games as well as the current popular games.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.