• Title/Summary/Keyword: Gaussian measure

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Shrinkage Estimator of Dispersion of an Inverse Gaussian Distribution

  • Lee, In-Suk;Park, Young-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.3
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    • pp.805-809
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    • 2006
  • In this paper a shrinkage estimator for the measure of dispersion of the inverse Gaussian distribution with known mean is proposed. Also we compare the relative bias and relative efficiency of the proposed estimator with respect to minimum variance unbiased estimator.

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A Density-based Clustering Method

  • Ahn, Sung Mahn;Baik, Sung Wook
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.715-723
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    • 2002
  • This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

A New Distance Measure for a Variable-Sized Acoustic Model Based on MDL Technique

  • Cho, Hoon-Young;Kim, Sang-Hun
    • ETRI Journal
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    • v.32 no.5
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    • pp.795-800
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    • 2010
  • Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.

3D Shape Recovery from Image Focus using Gaussian Process Regression (가우시안 프로세스 회귀분석을 이용한 영상초점으로부터의 3차원 형상 재구성)

  • Mahmood, Muhammad Tariq;Choi, Young Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.11 no.3
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    • pp.19-25
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    • 2012
  • The accuracy of Shape From Focus (SFF) technique depends on the quality of the focus measurements which are computed through a focus measure operator. In this paper, we introduce a new approach to estimate 3D shape of an object based on Gaussian process regression. First, initial depth is estimated by applying a conventional focus measure on image sequence and maximizing it in the optical direction. In second step, input feature vectors consisting of eginvalues are computed from 3D neighborhood around the initial depth. Finally, by utilizing these features, a latent function is developed through Gaussian process regression to estimate accurate depth. The proposed approach takes advantages of the multivariate statistical features and covariance function. The proposed method is tested by using image sequences of various objects. Experimental results demonstrate the efficacy of the proposed scheme.

ON THE CONTINUITY AND GAUSSIAN CHAOS OF SELF-SIMILAR PROCESSES

  • Kim, Joo-Mok
    • Journal of the Chungcheong Mathematical Society
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    • v.12 no.1
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    • pp.133-146
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    • 1999
  • Let {X(t), $t{\geq}0$} be a stochastic integral process represented by stable random measure or multiple Ito-Wiener integrals. Under some conditions, we prove the continuity and self-similarity of these stochastic integral processes. As an application, we get Gaussian chaos which has some shift continuous function.

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Segment-based Image Classification of Multisensor Images

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.28 no.6
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    • pp.611-622
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    • 2012
  • This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

Development of A FBG Sensor Interrogator for Detecting Strain and Performance Comparison of Peak Detection Algorithms (변형 검출을 위한 FBG 센서 인테로게이터 개발과 피크검출 알고리즘 성능 비교)

  • Park, Keun-Soo;Park, Jang-Sik
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.8
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    • pp.1137-1142
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    • 2013
  • FBG sensors are mainly used to measure strain and temperature of structures. In this paper, an interrogator of FBG sensors is developed and implemented to measure the crack of structures using FPGA and DSP. Developed interrogator consists of an optical source, an optical circulator, an optical grating and a CCD sensor and controller. The spectrum of the reflected light from the FBG sensor is analyzed and peak wavelength is detected. Next, strain of structure can be measured using shift of peak wavelength. Centroid algorithm and Gaussian fitting which are mainly applied to detect peak wavelength of the interrogator are compared in this paper. As a result of experiment, Gaussian fitting is suitable for a developed interrogator.

WICK DERIVATIONS ON WHITE NOISE FUNCTIONALS

  • Chung, Dong-Myung;Chung, Tae-Su
    • Journal of the Korean Mathematical Society
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    • v.33 no.4
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    • pp.993-1008
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    • 1996
  • The white noise analysis, initiated by Hida [3] in 1975, has been developed to an infinite dimensional distribution theory on Gaussian space $(E^*, \mu)$ as an infinite dimensional analogue of Schwartz distribution theory on Euclidean space with Legesgue measure. The mathematical framework of white noise analysis is the Gel'fand triple $(E) \subset (L^2) \subset (E)^*$ over $(E^*, \mu)$ where $\mu$ is the standard Gaussian measure associated with a Gel'fand triple $E \subset H \subset E^*$.

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BOUNDS OF ZERO MEAN GAUSSIAN WITH COVARIANCE FOR AVERAGE ERROR OF TRAPEZOIDAL RULE

  • Hong, Bum-Il;Choi, Sung-Hee;Hahm, Nahm-Woo
    • Journal of applied mathematics & informatics
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    • v.8 no.1
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    • pp.231-242
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    • 2001
  • We showed in [2] that if r≤2, zero mean Gaussian of average error of the Trapezoidal rule is proportional to h/sub i//sup 2r+3/ on the interval [0,1]. In this paper, if r≥3, we show that zero mean Gaussian of average error of the Trapezoidal rule is bounded by Ch⁴/sub i/h⁴/sub j/.