• Title, Summary, Keyword: Gaussian

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Simulation of multivariate non-Gaussian wind pressure on spherical latticed structures

  • Aung, Nyi Nyi;Ye, Jihong;Masters, F.J.
    • Wind and Structures
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    • v.15 no.3
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    • pp.223-245
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    • 2012
  • Multivariate simulation is necessary for cases where non-Gaussian processes at spatially distributed locations are desired. A simulation algorithm to generate non-Gaussian wind pressure fields is proposed. Gaussian sample fields are generated based on the spectral representation method using wavelet transforms method and then mapped into non-Gaussian sample fields with the aid of a CDF mapping transformation technique. To illustrate the procedure, this approach is applied to experimental results obtained from wind tunnel tests on the domes. A multivariate Gaussian simulation technique is developed and then extended to multivariate non-Gaussian simulation using the CDF mapping technique. It is proposed to develop a new wavelet-based CDF mapping technique for simulation of multivariate non-Gaussian wind pressure process. The efficiency of the proposed methodology for the non-Gaussian nature of pressure fluctuations on separated flow regions of different rise-span ratios of domes is also discussed.

Simulation of non-Gaussian stochastic processes by amplitude modulation and phase reconstruction

  • Jiang, Yu;Tao, Junyong;Wang, Dezhi
    • Wind and Structures
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    • v.18 no.6
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    • pp.693-715
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    • 2014
  • Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

The Waveform Model of Laser Altimeter System with Flattened Gaussian Laser

  • Ma, Yue;Wang, Mingwei;Yang, Fanlin;Li, Song
    • Journal of the Optical Society of Korea
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    • v.19 no.4
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    • pp.363-370
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    • 2015
  • The current waveform model of a laser altimeter is based on a Gaussian laser beam of fundamental mode, while the flattened Gaussian beam has many advantages such as nearly constant energy distribution on the center of the cross-section. Following the theory of the flattened Gaussian beam and the waveform theory of the laser altimeter, some of the primary parameters of the received waveform were derived, and a laser altimetry waveform simulator and waveform processing software were programmed and improved under the circumstance of a flattened Gaussian beam. The result showed that the bias between theoretical and simulated waveforms was less than 3% for every order mode, the waveform width and range error would increase as target slope or order number rose. Under higher order mode, the shapes of the received waveforms were no longer Gaussian, and could be fitted more precisely as a generalized Gaussian function with power bigger than 2. The flattened beam got much better performance for a multi-surface target, especially when the small surface is far from the center of the laser footprint. This article provides the waveform theoretical basis for the use of a flattened Gaussian beam in a laser altimeter.

Gaussian Mixture Model Based Smoke Detection Algorithm Robust to Lights Variations (Gaussian 혼합모델 기반 조명 변화에 강건한 연기검출 알고리즘)

  • Park, Jang-Sik;Song, Jong-Kwan;Yoon, Byung-Woo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.7 no.4
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    • pp.733-739
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    • 2012
  • In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.

Approximation for the Two-Dimensional Gaussian Q-Function and Its Applications

  • Park, Jin-Ah;Park, Seung-Keun
    • ETRI Journal
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    • v.32 no.1
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    • pp.145-147
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    • 2010
  • In this letter, we present a new approximation for the twodimensional (2-D) Gaussian Q-function. The result is represented by only the one-dimensional (1-D) Gaussian Q-function. Unlike the previous 1-D Gaussian-type approximation, the presented approximation can be applied to compute the 2-D Gaussian Q-function with large correlations.

Skewness of Gaussian Mixture Absolute Value GARCH(1, 1) Model

  • Lee, Taewook
    • Communications for Statistical Applications and Methods
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    • v.20 no.5
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    • pp.395-404
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    • 2013
  • This paper studies the skewness of the absolute value GARCH(1, 1) models with Gaussian mixture innovations (Gaussian mixture AVGARCH(1, 1) models). The maximum estimated-likelihood estimator (MELE) employed (a two- step estimation method in order to estimate the skewness of Gaussian mixture AVGARCH(1, 1) models. Through the real data analysis, the adequacy of adopting Gaussian mixture innovations is exhibited in reflecting the skewness of two major Korean stock indices.

CONDITIONAL TRANSFORM WITH RESPECT TO THE GAUSSIAN PROCESS INVOLVING THE CONDITIONAL CONVOLUTION PRODUCT AND THE FIRST VARIATION

  • Chung, Hyun Soo;Lee, Il Yong;Chang, Seung Jun
    • Bulletin of the Korean Mathematical Society
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    • v.51 no.6
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    • pp.1561-1577
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    • 2014
  • In this paper, we define a conditional transform with respect to the Gaussian process, the conditional convolution product and the first variation of functionals via the Gaussian process. We then examine various relationships of the conditional transform with respect to the Gaussian process, the conditional convolution product and the first variation for functionals F in $S_{\alpha}$ [5, 8].

Gaussian Response Curves Fitting for Ecology Data (생태학 자료에 대한 가우시안 반응곡선 적합)

  • Jeong, Hyeong Chul
    • Journal of the Korean Data Analysis Society
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    • v.20 no.5
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    • pp.2307-2318
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    • 2018
  • In this study, the Gaussian response regression and Gaussian logistics regression to describe the species occurrence along the environmental gradients is considered. In ecology, the occurrence of species is generally influenced by environmental variables and follows the Gaussian curve form. Using Gaussian curves, optimal environmental conditions, tolerance of environmental factors, peak abundance or peak abundance probability can be estimated for the expression of the species. In this study, Poisson regression or logistic regression analysis, which is a generalized linear model, is used to estimate the Gaussian curve. These models are applied to the spider data of Aart, Smeenk (1975) and the occurrence of the spider, the probability of occurrence of the spider are estimated on the environmental gradients. However, it is not possible to estimate the Gaussian curve unless the species abundance is followed by a uni-modal distribution. In this study, the univariate Gaussian response curve estimation is limited by the analysis with one environmental variable.

Gaussian Curvature Error Estimation for Mesh Simplification (Gaussian 곡률 오차 추정을 이용한 Mesh 간략화)

  • 임수일;임수일;김창헌
    • Proceedings of the Korean Information Science Society Conference
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    • pp.650-652
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    • 1998
  • 본 논문은 mesh 간략화를 위한 새로운 Gaussian 곡률 오차 추정 방법을 제안한다. Gaussian 곡률은 임의의 형상을 갖는 삼각화 된 단면체 표면에 대하여 위상과 기하학적 정보를 angle 과 face 의 관계로 정형화하여, vertex에 관한 곡률로 근사하여 표현한다. 간략화 방법은 지역적 형상으로부터 전체적인 형상을 추정한 후, 적절한 curvature criteria 로 간략화가 될 vertex를 선택하고 제거한다. 제거된 vertex에 의해 생성된 hole은 곡률에 기반하여 삼각화하고 곡률이 변화되는 vertex들의 Gaussian 곡률 오차를 계산한다. 각 간략화 level마다 최대 Gaussian 곡률 오차를 계산하므로, 사용자는 Gaussian 곡률 오차 추정으로 원하는 간략화 level을 지정할 수 있다. 또한 주어진 오차 안에서 vertex뿐만 아니라 edge나 face의 제거로, 간략화 되는 영역을 확산시켜 필요한 위상과 기하학적 정보를 유지하는 간략화를 할 수 있다.

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An Adaptive Noise Removal Method Using Local Statistics and Generalized Gaussian Filter (국부 통계 특성 및 일반화된 Gaussian 필터를 이용한 적응 노이즈 제거 방식)

  • Song, Won-Seon;Nguyen, Tuan-Anh;Hong, Min-Cheol
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.1C
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    • pp.17-23
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    • 2010
  • In this paper, we present an adaptive noise removal method using local statistics and generalized Gaussian filter. we propose a generalized Gaussian filter for removing noise effectively and detecting noise adaptively using local statistics based human visual system. The simulation results show the objective and subjective capabilities of the proposed algorithm.