• Title/Summary/Keyword: gaussian model

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Pollutant Dispersion Analysis Using the Gaussian Puff Model with the Numerical Flowfield Information (유동장 수치해석이 포함된 퍼프모델을 이용한 오염물질의 확산 해석)

  • Jung Y. R.;Park W. G.;Park O. H.
    • Journal of computational fluids engineering
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    • v.4 no.3
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    • pp.12-20
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    • 1999
  • The computations of the flowfield and pollutant dispersion over a flat plate and the Russian hills of various slopes are described. The Gaussian plume and the puff model have been used to calculate concentration of pollutant. The Reynolds-averaged unsteady incompressible Navier-Stokes equation with low Reynolds κ-ε model has been used to calculate the flowfield. The flow data of a flat plate and the Russian hills from Navier-Stokes equation solutions has been used as the input data for the puff model. The computational results of flowfield agree well with experimental results of both a flat plate and Russian hills. The concentration prediction by the Gaussian plume model and the Gaussian puff model also agrees flirty well with experiments.

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Application of a Modular Multi-Gaussian Beam Model to Ultrasonic Wave Propagation with Multiple Interfaces

  • Jeong, Hyun-Jo;Park, Moon-Cheol;Schmerr Lester W.
    • Journal of the Korean Society for Nondestructive Testing
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    • v.25 no.3
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    • pp.163-170
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    • 2005
  • A modular Gaussian beam model is developed to simulate some ultrasonic testing configurations where multiple interfaces are involved. A general formulation is given in a modular matrix form to represent the Gaussian beam propagation with multiple interfaces. The ultrasonic transducer fields are modeled by a multi-Gaussian beam model which is formed by superposing 10 single Gaussian beams. The proposed model, referred to as "MMGB" (modular multi-Gaussian beam) model, is then applied to a typical contact and angle beam testing configuration to predict the output signal reflected from the corner of a vertical crack. The resulting expressions given in a modular matrix form are implemented in a personal computer using the MATLAB program. Simulation results are presented and compared with available experimental results.

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.

Analysis of Radiation Exposure from Nuclear Reactor Accident in Complex Terrain (산악지형에서의 원자력발전소 사고시의 피폭해석)

  • Moon Hee Han;Sung Ki Chae;Moon Hyun Chun
    • Nuclear Engineering and Technology
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    • v.17 no.3
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    • pp.216-223
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    • 1985
  • The Gaussian plume model is widely used to calculate the concentrations of gaseous radioactive effluents in the atmosphere. This model assumes that the terrain is flat, so that the dispersion coefficients which are the most important parameters in this model must be compensated in complex terrain such as in Korea. In this study the compensation of vertical dispersion coefficient in two dimensional x-z plane has been accomplished by comparing the Gaussian plume model with numerical model. The results show that the concentractions of radioactive effluents over complex terrain are more dilluted than those expected over flat terrain.

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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.

Asymptotic Gaussian Structures in a Critical Generalized Curie-Wiss Mean Field Model : Large Deviation Approach

  • Kim, Chi-Yong;Jeon, Jong-Woo
    • Journal of the Korean Statistical Society
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    • v.25 no.4
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    • pp.515-527
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    • 1996
  • It has been known for mean field models that the limiting distribution reflecting the asymptotic behavior of the system is non-Gaussian at the critical state. Recently, however, Papangelow showed for the critical Curie-Weiss mean field model that there exist Gaussian structures in the asymptotic behavior of the total magnetization. We construct Gaussian structures existing in the internal fluctuation of the system for the critical case of a generalized Curie-Weiss mean field model.

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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 Blending: Improved 3D Gaussian Splatting for Model Light-Weighting and Deep Learning-Based Performance Enhancement

  • Yeong-In Lee;Jin-Nyeong Heo;Ji-Hwan Moon;Ha-Young Kim
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.8
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    • pp.23-32
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    • 2024
  • NVS (Novel View Synthesis) is a field in computer vision that reconstructs new views of a scene from a set of input views. Real-time rendering and high performance are essential for NVS technology to be effectively utilized in various applications. Recently, 3D-GS (3D Gaussian Splatting) has gained popularity due to its faster training and inference times compared to those of NeRF (Neural Radiance Fields)-based methodologies. However, since 3D-GS reconstructs a 3D (Three-Dimensional) scene by splitting and cloning (Density Control) Gaussian points, the number of Gaussian points continuously increases, causing the model to become heavier as training progresses. To address this issue, we propose two methodologies: 1) Gaussian blending, an improved density control methodology that removes unnecessary Gaussian points, and 2) a performance enhancement methodology using a depth estimation model to minimize the loss in representation caused by the blending of Gaussian points. Experiments on the Tanks and Temples Dataset show that the proposed methodologies reduce the number of Gaussian points by up to 4% while maintaining performance.

Review of Spatial Linear Mixed Models for Non-Gaussian Outcomes (공간적 상관관계가 존재하는 이산형 자료를 위한 일반화된 공간선형 모형 개관)

  • Park, Jincheol
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.353-360
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    • 2015
  • Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.

A Gaussian Beam Light Distribution Model of the Biological Tissue (생체의 가우스빔 광분포모델)

  • 조진호;하영호;이건일
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.25 no.6
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    • pp.654-662
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    • 1988
  • A simple and useful model of light distribution for the biologhical tissue to the Gaussian beam is proposed. This model assumes that the incident Gaussian beam broadens into two Gaussian beams, travelling in the opposite directions as the result of both isotropic scattering and absorption in the tissue. With this assumption, two-dimensional light intensity of each flux as well as the equations of both absorption and scattering have been derived, and the validity of modeling has been confirmed experimentally. Consequently, the results paved a way for easy evaluation of the light distribution in the biological tissue.

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