• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.451-456
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• 2018

The use of single component distribution to describe the irregular stand structure of degraded forest often lead to bias. Such biasness can be overcome by the application of finite mixture distribution. Therefore, in this study, finite mixture distribution was used to characterise the irregular stand structure of the Gmelina arborea plantation in Omo forest reserve. Thirty plots, ten each from the three stands established in 1984, 1990 and 2005 were used. The data were pooled per stand and fitted. Four finite mixture distributions including normal mixture, lognormal mixture, gamma mixture and Weibull mixture were considered. The method of maximum likelihood was used to fit the finite mixture distributions to the data. Model assessment was based on negative loglikelihood value ($-{\Lambda}{\Lambda}$), Akaike information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The results showed that the mixture distributions provide accurate and precise characterisation of the irregular diameter distribution of the degraded Gmelina arborea stands. The $-{\Lambda}{\Lambda}$, AIC, BIC and RMSE values ranged from -715.233 to -348.375, 703.926 to 1433.588, 718.598 to 1451.334 and 3.003 to 7.492, respectively. Their performances were relatively the same. This approach can be used to describe other irregular forest stand structures, especially the multi-species forest.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.711-716
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• 2003

This paper proposes the method to select the number of optimal mixtures in each state in Continuous Density HMM (Hidden Markov Models), Previously, researchers used the same number of mixture components in each state of HMM regardless spectral characteristic of speaker, To model each speaker as accurately as possible, we propose to use a different number of mixture components for each state, Selection of mixture components considered the probability value of mixture by each state that affects much parameter estimation of continuous density HMM, Also, we use PCA (principal component analysis) to reduce the correlation and obtain the system' stability when it is reduced the number of mixture components, We experiment it when the proposed method used average 10% small mixture components than the conventional HMM, When experiment result is only applied selection of mixture components, the proposed method could get the similar performance, When we used principal component analysis, the feature vector of the 16 order could get the performance decrease of average 0,35% and the 25 order performance improvement of average 0.65%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6534-6541
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• 2013

The aim of this study was to develop a fiber-reinforced asphalt mixture that was designed to do the following: 1) address fatigue cracks, which is a major source of damage; and 2) increase the rutting resistance. This study reports the effects of the aggregate mixture design that incorporates a dumbbell-shaped fiber. An experiment was carried out to measure the unit weights and unit weight ratios between the mixture that was compacted and the one that was not. A method to substitute a specific aggregate mixture with the dumbbell-shaped fiber was confirmed using the volume concept according to the Bailey method. The results showed that the weight of the PCS aggregate mixture that need to be replaced was 11.88g when a 0.3% reinforcing fiber was added to the 1950g mixture.

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.325-336
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• 1999

Collinearity among independent variables can have severe effects on the precision of response estimation for some region of interest in the experiments with mixture. A method of finding optimal linear restriction on regression parameter in linear model for mixture experiments in the sense of minimizing integrated mean squared error is studied. We use the formulation of optimal restrictions on regression parameters for estimating responses proposed by Park(1981) by transforming mixture components to mathematically independent variables.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.2
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• pp.197-201
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• 2010

$SF_6$ gas would be used in power transformer, GIS (Gas insulated switchgear) and so on because of its electrically superior insulation and chemically stable structure. Recently, the reduction of $SF_6$ is required to avoid global warming and the researches on the dilution of $SF_6$ with other gases have been carried out. $SF_6$ mixture gases with $N_2$ and $C_xF_y$ have drawn attention to the synergy effect. However, in order to understand the mechanism of the synergy effect, it is important to analyze and evaluate properties of mixture gases quantitatively. In this paper, we investigated the mechanism of synergy effect from electron collision processes and electron energy distribution by solving Boltzmann equation with propagator method. Three kinds of gases for dilution of $SF_6$ ($SF_6/N_2$, $SF_6/CF$4 and $SF_6/C_4F_8$) are considered in this simulation. On the properties of $SF_6/N_2$ mixture gas, the variation of reduced electric field was shown highly within 0%~40% mixtures of $SF_6$. And the more low-level electron energy has been distributed, the higher insulation capability has appeared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Korean Chemical Engineering Research
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• v.51 no.6
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• pp.677-684
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• 2013

A mixed refrigerant cycle (MRC) has been widely used in liquefaction of natural gas because it is simple and easily operable with reasonable equipment costs. One of the important techniques in MRC is selection of a refrigerant mixture and decision of its optimum mixing ratio. In this work, it is examined whether mixture components (refrigerants) and their mixing ratio influence performance of general MRC processes. In doing this, mixture design and response surface method, which are well-known statistical techniques, are used to find optimal mixture refrigerants and their optimal mixing ratio that minimize total energy consumption of the entire liquefaction process. A MRC process using several refrigerants and various mixing ratios is simulated by Aspen HYSYS and mixture design and response surface method are implemented using Minitab. According to the results, methane ($C_1$), ethane ($C_2$), propane ($C_3$) and nitrogen ($N_2$) are selected as best mixture refrigerants and the determined mixture ratio (mole ration) can reduce total energy consumption by up to 50%.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.332-338
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• 2013

We propose a method for target detection in Infrared images. In order to effectively detect a target region from an image with noises and clutters, spatial information of the target is first considered by analyzing pixel distributions of projections in horizontal and vertical directions. These distributions are represented as Gaussian distributions, and Gaussian Mixture Model is created from these distributions in order to find thresholding points of the target region. Through analyzing the calculated Gaussian Mixture Model, the target region is detected by eliminating various backgrounds such as noises and clutters. This is performed by using a novel thresholding method which can effectively detect the target region. As experimental results, the proposed method has achieved better performance than existing methods.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.156-167
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• 1996

D-optimality is used often in design augmentation of mixture experiments. Although such alphabetic criteria provide a valuable foundation for generating designs, they often fail to convey the true nature of the design's support of the fitted model in terms of prediction variance over a region of interest. Thus, a graphical method is proposed to evaluate augmented designs in mixture experiments. This method can be used to evaluate the effect of missing observation and outlier in mixture experiments.