• Title/Summary/Keyword: Gaussian Distribution

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A Fault Detection and Exclusion Algorithm using Particle Filters for non-Gaussian GNSS Measurement Noise

  • Yun, Young-Sun;Kim, Do-Yoon;Kee, Chang-Don
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.2
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    • pp.255-260
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    • 2006
  • Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

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A Copula method for modeling the intensity characteristic of geotechnical strata of roof based on small sample test data

  • Jiazeng Cao;Tao Wang;Mao Sheng;Yingying Huang;Guoqing Zhou
    • Geomechanics and Engineering
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    • v.36 no.6
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    • pp.601-618
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    • 2024
  • The joint probability distribution of uncertain geomechanical parameters of geotechnical strata is a crucial aspect in constructing the reliability functional function for roof structures. However, due to the limited number of on-site exploration and test data samples, it is challenging to conduct a scientifically reliable analysis of roof geotechnical strata. This study proposes a Copula method based on small sample exploration and test data to construct the intensity characteristics of roof geotechnical strata. Firstly, the theory of multidimensional copula is systematically introduced, especially the construction of four-dimensional Gaussian copula. Secondly, data from measurements of 176 groups of geomechanical parameters of roof geotechnical strata in 31 coal mines in China are collected. The goodness of fit and simulation error of the four-dimensional Gaussian Copula constructed using the Pearson method, Kendall method, and Spearman methods are analyzed. Finally, the fitting effects of positive and negative correlation coefficients under different copula functions are discussed respectively. The results demonstrate that the established multidimensional Gaussian Copula joint distribution model can scientifically represent the uncertainty of geomechanical parameters in roof geotechnical strata. It provides an important theoretical basis for the study of reliability functional functions for roof structures. Different construction methods for multidimensional Gaussian Copula yield varying simulation effects. The Kendall method exhibits the best fit in constructing correlations of geotechnical parameters. For the bivariate Copula fitting ability of uncertain parameters in roof geotechnical strata, when the correlation is strong, Gaussian Copula demonstrates the best fit, and other Copula functions also show remarkable fitting ability in the region of fixed correlation parameters. The research results can offer valuable reference for the stability analysis of roof geotechnical engineering.

Analysis of Drain Induced Barrier Lowering for Double Gate MOSFET Using Gaussian Distribution (가우스분포를 이용한 이중게이트 MOSFET의 드레인유기장벽감소 분석)

  • Jung, Hak-Kee
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.2
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    • pp.325-330
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    • 2012
  • In this paper, drain induced barrier lowering(DIBL) has been analyzed as one of short channel effects occurred in double gate(DG) MOSFET to be next-generation devices. Since Gaussian function been used as carrier distribution for solving Poisson's equation to obtain analytical solution of potential distribution, we expect our results using this model agree with experimental results. DIBL has been investigated according to projected range and standard projected deviation as variables of Gaussian function, and channel structure and channel doping intensity as device parameter. Since the validity of this analytical potential distribution model derived from Poisson's equation has already been proved in previous papers, DIBL has been analyzed using this model. Resultly, DIBL has been greatly changed for channel structure and doping concentration.

Lip Shape Representation and Lip Boundary Detection Using Mixture Model of Shape (형태계수의 Mixture Model을 이용한 입술 형태 표현과 입술 경계선 추출)

  • Jang Kyung Shik;Lee Imgeun
    • Journal of Korea Multimedia Society
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    • v.7 no.11
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    • pp.1531-1539
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    • 2004
  • In this paper, we propose an efficient method for locating human lips. Based on Point Distribution Model and Principle Component Analysis, a lip shape model is built. Lip boundary model is represented based on the concatenated gray level distribution model. We calculate the distribution of shape parameters using Gaussian mixture. The problem to locate lip is simplified as the minimization problem of matching object function. The Down Hill Simplex Algorithm is used for the minimization with Gaussian Mixture for setting initial condition and refining estimate of lip shape parameter, which can refrain iteration from converging to local minima. The experiments have been performed for many images, and show very encouraging result.

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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 Optimization of Vocabulary Recognition Clustering Model using Configuration Thread Control (형상 형성 제어를 이용한 어휘인식 공유 모델의 가우시안 최적화)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.2
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    • pp.127-134
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    • 2010
  • In continuous vocabulary recognition system by probability distribution of clustering method has used model parameters of an advance estimate to generated each contexts for phoneme data surely needed but it has it's bad points of gaussian model the accuracy unsecure of composed model for phoneme data. To improve suggested probability distribution mixed gaussian model to optimized that phoneme data search supported configuration thread system. This paper of configuration thread system has used extension facet classification user phoneme configuration thread information offered gaussian model the accuracy secure. System performance as a result of represent vocabulary dependence recognition rate of 98.31%, vocabulary independence recognition rate of 97.63%.

In Out-of Vocabulary Rejection Algorithm by Measure of Normalized improvement using Optimization of Gaussian Model Confidence (미등록어 거절 알고리즘에서 가우시안 모델 최적화를 이용한 신뢰도 정규화 향상)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.12
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    • pp.125-132
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    • 2010
  • In vocabulary recognition has unseen tri-phone appeared when recognition training. This system has not been created beginning estimation figure of model parameter. It's bad points could not be created that model for phoneme data. Therefore it's could not be secured accuracy of Gaussian model. To improve suggested Gaussian model to optimized method of model parameter using probability distribution. To improved of confidence that Gaussian model to optimized of probability distribution to offer by accuracy and to support searching of phoneme data. This paper suggested system performance comparison as a result of recognition improve represent 1.7% by out-of vocabulary rejection algorithm using normalization confidence.

Codebook design for subspace distribution clustering hidden Markov model (Subspace distribution clustering hidden Markov model을 위한 codebook design)

  • Cho, Young-Kyu;Yook, Dong-Suk
    • Proceedings of the KSPS conference
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    • 2005.04a
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    • pp.87-90
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    • 2005
  • Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

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Initial Size Distribution of the Milky Way Globular Clusters

  • Shin, Ji-Hye;Kim, Sung-Soo S.
    • The Bulletin of The Korean Astronomical Society
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    • v.35 no.2
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    • pp.35.1-35.1
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    • 2010
  • Unlike the initial mass function, the initial size distribution of globular cluster (GC) systems is not well known. We calculate the evolution of the mass function (MF), radial distribution (RD), and size distribution (SD) of the Galactic GC system. By comparing the results from this calculation and the present-day MF, RD, and SD of the Galactic GC system, we infer the initial SD of the GC system. We find that a Gaussian distribution of the half-mass radius and a Gaussian distribution of the half-mass to Jacobi radius ratio are the best-fit initial SDs of the Galactic GC system.

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Influence of non-Gaussian characteristics of wind load on fatigue damage of wind turbine

  • Zhu, Ying;Shuang, Miao
    • Wind and Structures
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    • v.31 no.3
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    • pp.217-227
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    • 2020
  • Based on translation models, both Gaussian and non-Gaussian wind fields are generated using spectral representation method for investigating the influence of non-Gaussian characteristics and directivity effect of wind load on fatigue damage of wind turbine. Using the blade aerodynamic model and multi-body dynamics, dynamic responses are calculated. Using linear damage accumulation theory and linear crack propagation theory, crack initiation life and crack propagation life are discussed with consideration of the joint probability density distribution of the wind direction and mean wind speed in detail. The result shows that non-Gaussian characteristics of wind load have less influence on fatigue life of wind turbine in the area with smaller annual mean wind speeds. Whereas, the influence becomes significant with the increase of the annual mean wind speed. When the annual mean wind speeds are 7 m/s and 9 m/s at hub height of 90 m, the crack initiation lives under softening non-Gaussian wind decrease by 10% compared with Gaussian wind fields or at higher hub height. The study indicates that the consideration of the influence of softening non-Gaussian characteristics of wind inflows can significantly decrease the fatigue life, and, if neglected, it can result in non-conservative fatigue life estimates for the areas with higher annual mean wind speeds.