• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.1-9
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• 2007

Decision fusion in wireless sensor networks under non-Gaussian noise channels is studied. To consider the tail behavior noise distributions, we use a exponentially-tailed distribution as a wide class of noise distributions. Based on a canonical parallel fusion model with fading and noise channels, the likelihood ratio(LR) based fusion rule is considered as an optimal fusion rule under Neyman-Pearson criterion. With both high and low signal-to-noise ratio (SNR) approximation to the optimal rule, we obtain several suboptimal fusion rules. and we propose a simple fusion rule that provides robust detection performance with a minimum prior information, Performance evaluation for several fusion rules is peformed through simulation. Simulation results show the robustness of the Proposed simple fusion rule.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.4
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• pp.499-506
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• 2012

In this paper, a dynamic modeling for the velocity and position information of a single frequency stand-alone GPS(Global Positioning System) receiver is described. In static condition, the position error dynamic model is identified as a first/second order transfer function, and the velocity error model is identified as a band-limited Gaussian white noise via non-parametric method of a PSD(Power Spectrum Density) estimation in continuous time domain. A Kalman filter is proposed considering both correlated/white measurements noise based on identified GPS error model. The performance of the proposed Kalman filtering method is verified via numerical simulation.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.151-153
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• 2006

we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by a sequential Newton-Raphson expectation maximization (SNEM), incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.

• Structural Engineering and Mechanics
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• v.33 no.1
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• pp.31-46
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• 2009

An anisotropic damage mechanics approach is introduced which models the static and dynamic behavior of mass concrete in 3D space. The introduced numerical approach is able to model non-uniform cracking within the cracked element due to cracking in Gaussian points of elements. The validity of the proposed model is considered using available experimental and theoretical results under the static and dynamic loads. No instability and stress locking is observed in the conducted analyses. The Morrow Point dam is analyzed including dam-reservoir interaction effects to consider the nonlinear seismic behavior of the dam. It is found that the resulting crack profiles are in good agreement with those obtained from the smeared crack approach. It is concluded that the proposed model can be used in nonlinear static and dynamic analysis of concrete dams in 3D space and enables engineers to define the damage level of these infrastructures. The performance level of the considered system is used to assess the static and seismic safety using the defined performance based criteria.

• Structural Engineering and Mechanics
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• v.74 no.4
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• pp.471-479
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• 2020

This paper aims to study the effect of the elastic nonlocality on the transient waves in a two-dimensional thermoelastic medium influenced by thermal loading due to the laser pulse. The bounding plane surface is heated by a non-Gaussian laser beam. The problem is discussed under the Eringen's nonlocal elasticity model and the Green-Naghdi (G-N) theory with and without energy dissipation. The normal mode analysis method is used to get the exact expressions for the physical quantities which illustrated graphically by comparison and discussion. The effects of nonlocality and different values of time on the displacement, the stresses, and the temperature were made numerically. All the computed results obtained have been depicted graphically and explained.

• Wind and Structures
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• v.21 no.1
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• pp.1-23
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• 2015

Wind tunnel testing of a low-rise building with openings (holes) of different sizes and shapes on a roof corner is conducted to measure the internal and external pressures from the building model. Detailed analysis of the testing data is carried out to investigate the characteristics of the internal and external pressures of the building with different openings' configurations. Superimposition of the internal and external pressures makes the emergence of positive net pressures on the roof. The internal pressures demonstrate an overall uniform distribution. The probability density function (PDF) of the internal pressures is close to the Gaussian distribution. Compared with the PDF of the external pressures, the non-Gaussian characteristics of the net pressures weakened. The internal pressures exhibit strong correlation in frequency domain. There appear two humps in the spectra of the internal pressures, which correspond to the Helmholtz frequency and vortex shedding frequency, respectively. But, the peak for the vortex shedding frequency is offset for the net pressures. Furthermore, the internal pressure characteristics indirectly reflect that the length of the front edge enhances the development of the conical vortices.The objective of this study aims to further understanding of the characteristics of internal, external and net pressures for low-rise buildings in an effort to reduce wind damages to residential buildings.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2299-2318
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• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.349-359
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• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.