• Computational Structural Engineering
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• v.10 no.3
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• pp.125-131
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• 1997

Most dynamic systems have are known to various random properties in excitation and system parameters. In this paper, a procedure for response analysis is proposed for the linear dynamic system with random properties in both excitation and system parameters. The system parameters and responses with random properties are modeled by perturbation technique, and then response analysis is formulated by probabilistic and vibration theories. And probabilistic FEM is also used for the calculation of mean response which is difficult by the proposed response model. As an applicative example, the transient response is considered for systems of single degree of freedom with random mass and spring constant subjected to stationary white-noise excitation and the results are compared to those of numerical simulation.

• Structural Engineering and Mechanics
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• v.18 no.5
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• pp.591-607
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• 2004

The substance of the use of the derived non-linear creep constitutive equations under variable stress levels (see first part of the paper, Kmet 2004) is explained and the strategy of their application is outlined using the results of one-step creep tests of the steel spiral strand rope as an example. In order to investigate the creep strain increments of cables an experimental set-up was originally designed and a series of tests were carried out. Attention is turned to the individual main steps in the production and application procedure, i.e., to the one-step creep tests, definition of loading history, determination of the kernel functions, selection and definition of constitutive equation and to the comparison of the resulting values considering the product and the additive forms of the approximation of the kernel functions. To this purpose, the parametrical study is performed and the results are presented. The constitutive equations of non-linear creep of cable under variable stress history offer a strong tool for the real simulation of stochastic variable load history and prediction of realistic time-dependent response (current deflection and stress configuration) of structures with cable elements. By means of suitable stress combination and its gradual repeating various loads and times effects can be modelled.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.71.2-71.2
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• 2014

processes at the origin of the star formation in the galaxies over the last 10 billions years. While it was proposed in the past that merging of galaxies has a dominant role to explain the triggering of the star formation in the distant galaxies having high star formation rates. In the opposite, more recent studies revealed scaling laws linking the star formation rate in the galaxies to their stellar mass or their gas mass. The small dispersion of these laws seems to be in contradiction with the idea of powerful stochastic events due to interactions, but rather in agreement with the new vision of galaxy history where the latter are continuously fed by intergalactic gas. I was especially interested in one of this scaling law, the relation between the star formation (SFR) and the stellar mass (M*) of galaxies, commonly called the main sequence of star forming galaxies. I have studied this main sequence, SFR-M*, in function of the morphology and other physical parameters as the radius, the colour, the clumpiness. The goal was to understand the origin of the sequence's dispersion related to the physical processes underlying this sequence in order to identify the main mode of star formation controlling this sequence. This work needed a multi-wavelength approach as well as the use of galaxies profile simulation to distinguish between the different galaxy morphological types implied in the main sequence.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.13-20
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• 2005

This paper addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple recordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, withe fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1301-1312
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• 2020

Using decode-and-forward relaying in the cognitive radio networks, the spectrum efficiency can improve furthermore. The optimization algorithm of the spectrum sensing estimation time is presented for the cognitive relay networks in this paper. The longer sensing time will bring two aspects of the consequences. On the one hand, the channel parameters are estimated more accurate so as to reduce the interferences to the authorized users and to improve the throughput of the cognitive users. On the other hand, it shortens the transmission time so as to decease the system throughput. In this time, it exists an optimal sensing time to maximize the throughput. The channel state information of the sub-bands is considered as the exponentially distributed, so a stochastic programming method is proposed to optimize the sensing time for the cognitive relay networks. The computer simulation results using the Matlab software show that the algorithm is effective, which has a certain engineering application value.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.246-250
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• 2000

A distinctive feature in vibration control of a large civil infrastructure is the existence of large disturbances, such as wind, earthquake, and sea wave forces. Those disturbances govern the behavior of the structure, however, they cannot be precisely measured, especially for the case of wind-induced vibration control. The sliding mode fuzzy control (SMFC), which is of interest in this study, may use not only the structural response measurement but also the wind force measurement. Hence, an adaptive disturbance estimation filter is introduced to generate a wind force vector at each time instance based on the measured structural response and the stochastic information of the wind force. The structure of the filter is constructed based on an auto-regressive with auxiliary input model. A numerical simulation is carried out on a benchmark problem of a wind-excited building. The results indicate that the overall performance of the proposed SMFC is as good as the other methods and that most of the performance indices improve as the adaptive disturbance estimation filter is introduced.

• Advances in concrete construction
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• v.9 no.5
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• pp.469-478
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• 2020

This study aims to establish a new methodological framework for the evaluation of the evolution of the reliability of plain concrete for pavement vs number of cycles under flexural fatigue loading. According to the framework, a new method calculating the reliability was proposed through probability simulation in order to describe a random accumulation of fatigue damage, which combines reliability theory, one-to-one probability density functions transformation technique, cumulative fatigue damage theory and Weibull distribution theory. Then the statistical analysis of flexural fatigue performance of cement concrete tested was carried out utilizing Weibull distribution. Ultimately, the reliability for the tested cement concrete was obtained by the proposed method. Results indicate that the stochastic evolution behavior of concrete materials under fatigue loading can be captured by the established framework. The flexural fatigue life data of concrete at different stress levels is well described utilizing the two-parameter Weibull distribution. The evolution of reliability for concrete materials tested in this study develops by three stages and may corresponds to develop stages of cracking. The proposed method may also be available for the analysis of degradation behaviors under non-fatigue conditions.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.625-630
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• 2000

Numerical and experimental investigations are peformed for the rarefied gas flows in pumping channels of a helical-type drag pump. Modern turbomolecular pumps include a drag stage in the discharge side, operating roughly in $10^{-2}{\sim}10Torr$. The flow occurring in the pumping channel develops from the molecular transition to slip flow traveling downstream. Two different numerical methods are used in this analysis: the first one is a continuum approach in solving the Navier-Stokes equations with slip boundary conditions, and the second one is a stochastic particle approach through the use of the direct simulation Monte Carlo(DSMC) method. The flow in a pumping channel is three-dimensional(3D), and the main difficulty in modeling a 3D case comes from the rotating frame of reference. Thus, trajectories of particles are no longer straight lines. In the Present DSMC method, trajectories of particles are calculated by integrating a system of differential equations including the Coriolis and centrifugal forces. Our study is the first instance to analyze the rarefied gas flows in rotating frame in the presence of noninertial effects.