• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.111-117
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• 2017

The arrival time of rescue ships is very important in the event of distress. This paper presents the development of experimental data to calculate the arrival time of rescue ships. The ship's traffic probability distribution was used. Mokpo Port was selected as the area of study, and AIS data for a 1 year period were used. For the ship's traffic probability distribution, a gateline was established. The lateral range distribution was calculated and fitted to the normal distribution and two Gaussian mixture distributions (GMD2), and each parameter was extracted. After the locations of ${\mu}$, ${\mu}{\pm}1{\sigma}$ of the normal distribution and ${\mu}_1$ of the two Gaussian mixture distribution(GMD2) were set as waypoints, the location and probability were determined. A scenario was established in relation to each type of parameter. Thus, the arrival time can be calculated.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.260-267
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• 2011

This paper proposes a method for segmenting objects from the background in IR(Infrared) images based on GrabCut algorithm. The GrabCut algorithm needs the window encompassing the interesting known object. This procedure is processed by user. However, to apply it for object recognition problems in image sequences. the location of window should be determined automatically. For this, we adopted the Otsu' algorithm for segmenting the interesting but unknown objects in an image coarsely. After applying the Otsu' algorithm, the window is located automatically by blob analysis. The GrabCut algorithm needs the probability distributions of both the candidate object region and the background region surrounding closely the object for estimating the Gaussian mixture models(GMMs) of the object and the background. The probability distribution of the background is computed from the background window, which has the same number of pixels within the candidate object region. Experiments for various IR images show that the proposed method is proper to segment out the interesting object in IR image sequences. To evaluate performance of proposed segmentation method, we compare other segmentation methods.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.781-788
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• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• Journal of Photoscience
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• v.12 no.2
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• pp.87-93
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• 2005

A new assay technique to distinguish between pure compounds and the isomeric mixtures has been suggested using single molecule (SM) fluorescence detection technique. Since the number of emission spots in a fluorophorespread film prepared from a genuine dye solution was determined by experimental condition, the deviation of spot numbers from the expected values could be considered to be an indication of lower purity of the sample solution. The lower limit of sample concentration for this assay was determined to be $5{\times}10^{-10}$ M to show uniform number of expected spots within 10% uncertainties in our experimental condition. An individual fluorescence intensity distribution for a mixture of isomers having doubly different emissivities was simulated by adding distributions obtained from Cy3 and nile red (NR) independently. The result indicated that the mixture could be identified from the pure compounds through the difference in the number of Gaussian functions to fit the distribution. This new assay technique can be applied to the purity test for synthetic biofluorophores which are usually prepared in small quantities not enough for classical ensemble assays.

• MALSORI
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• no.60
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• pp.145-164
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• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.37.1-37.1
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• 2013

We present our study of analyzing the photometric properties of the globular cluster (GC) system which resides in the extended halo of the central bright Coma cluster galaxy NGC 4874. The core of the Coma cluster of galaxies (Abell 1656) was observed with both the HST Advanced Camera for Surveys (ACS) in the F475W (g475) and F814W (I814) and Wide Field Camera 3 IR Channel (WFC3/IR) in the F160W (H160) filters. The data analysis procedure and GC candidate selection criteria are briefly described. We investigate the interesting "tilt" features in color-magnitude diagrams for this GC system and their link to the nonlinear color-metallicity relation for GCs. The NGC 4874's GC system exhibits a bimodal distribution in the optical g475-I814 color and much more than half the GCs fall in the red side at g475-I814 ~ 1.1. This bimodality is weakened in the optical-IR I814-H160 color; the quantitative analysis on the features of both color distributions using the Gaussian Mixture Modeling code proves the bimodalities are different. Both colors, thus, cannot linearly reflect the bimodality of an underlying metallicity, supporting the suggestion that observed bimodalities in extragalactic GC colors are the metallicity-to-color projection effect.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.179-192
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• 2001

When a statistical model has a hierarchical structure such as multilayer perceptrons in neural networks or Gaussian mixture density representation, the model includes distribution with unidentifiable parameters when the structure becomes redundant. Since the exact structure is unknown, we need to carry out statistical estimation or learning of parameters in such a model. From the geometrical point of view, distributions specified by unidentifiable parameters become a singular point in the parameter space. The problem has been remarked in many statistical models, and strange behaviors of the likelihood ratio statistics, when the null hypothesis is at a singular point, have been analyzed so far. The present paper studies asymptotic behaviors of the maximum likelihood estimator and the Bayesian predictive estimator, by using a simple cone model, and show that they are completely different from regular statistical models where the Cramer-Rao paradigm holds. At singularities, the Fisher information metric degenerates, implying that the cramer-Rao paradigm does no more hold, and that he classical model selection theory such as AIC and MDL cannot be applied. This paper is a first step to establish a new theory for analyzing the accuracy of estimation or learning at around singularities.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.147-154
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• 2002

In this paper, we propose an error estimation method based on the Bhattacharyya distance for multimodal data. First, we try to find the empirical relationship between the classification error and the Bhattacharyya distance. Then, we investigate the possibility to derive the error estimation equation based on the Bhattacharyya distance for multimodal data. We assume that the distribution of multimodal data can be approximated as a mixture of several Gaussian distributions. Experimental results with remotely sensed data showed that there exist strong relationships between the Bhattacharyya distance and the classification error and that it is possible to predict the classification error using the Bhattacharyya distance for multimodal data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.365-373
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• 2013

Representative tidal gauging (TG) stations are selected to cover the tidal characteristics of the Korean peninsula coastal seas, and the statistical parameters of the data are analysed from the perspective of the probability distribution at that TG station. The shape of the distribution in the Incheon and Gunsan TG stations, which are tide-dominated areas, shows two clear modes at HWONT and LWONT in the distributions, and in the Mokpo station, shows an asymmetric double peak distribution. In contrast, the frequency distribution shape shows a smoothed flat peak in the Jeju, Yeosu and Busan TG stations, and a single peak in the Pohang and Sokcho TG stations. The emersion and submersion equations suggested as the 6-parameter Gaussian mixture models in this study are accurate, and well fitted to the observed tidal elevation data. The ${\mu}_1$, ${\mu}_2$ parameters are highly correlated to the LWONT and HWONT, and the ${\sigma}_1$ and ${\sigma}_2$ parameters are also closely correlated to the mean tidal range. The ${\mu}_1$ and ${\mu}_2$ parameters coincide with the modes of the suggested probability distribution of the hourly tidal level data.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.43-57
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• 2022

Advances in measurement techniques have reduced measurement costs and enhanced safety resulting in less uncertainty. For example, an acoustic doppler current profiler (ADCP) based suspended sediment concentration (SSC) measurement technique is being accepted as an alternative to the conventional data collection method. In Korean rivers, horizontal ADCPs (H-ADCPs) are mounted on the automatic discharge monitoring stations, where SSC can be measured using the backscatter of ADCPs. However, automatic discharge monitoring stations and sediment monitoring stations do not always coincide which hinders the application of the new techniques that are not feasible to some stations. This work presents and analyzes H-ADCP-SSC models for 9 discharge monitoring stations in Korean rivers. In application of the Gaussian mixture model (GMM) to sediment-related variables (catchment area, particle size distributions of suspended sediment and bed material, water discharge-sediment discharge curves) from 44 sediment monitoring stations, it is revealed that those characteristics can distinguish sediment monitoring stations regionally. Linking the two results, we propose a protocol determining the H-ADCP-SSC model where no H-ADCP-SSC model is available.