• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.692-697
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• 1994

In the actual sound environmental systems, it seems to be essentially difficult to exactly evaluate a whole probability distribution form of its response fluctuation, owing to various types of natural, social and human factors. Up to now, we very often reported two kinds of unified probability density expressions in the standard expansion from of Hermite and Laguerre type orthonormal series to generally evaluate non-Gaussian, non-linear correlation and/or non-stationary properties of the fluctuation phenomenon. However, in the real sound environment, there still remain many actual problems on the necessity of improving the above two standard type probability expressions for practical use. In this paper, first, a central point is focused on how to find a new probabilistic theory of practically evaluating the variety and complexity of the actual random fluctuations, especially through introducing some equivalence transformation toward two standard probability density expressions mentioned above in the expansion from of Hermite and Laguerre type orthonormal series. Then, the effectiveness of the proposed theory has been confirmed experimentally too by applying it to the actual problems on the response probability evaluation of various sound insulation systems in an acoustic room.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.2
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• pp.75-83
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• 2008

Joye et al. introduced a new prime generation algorithm (JPV algorithm hereafter), by removing the trial division from the previous combined prime generation algorithm (combined algorithm hereafter) and claimed that JPV algorithm is $30{\sim}40%$ faster than the combined algorithm. However, they only compared the number of Fermat-test calls, instead of comparing the total running times of two algorithms. The reason why the total running times could not be compared is that there was no probabilistic analysis on the running time of the JPV algorithm even though there was a probabilistic analysis for the combined algorithm. In this paper, we present a probabilistic analysis on the running time of the JPV algorithm. With this analytic model, we compare the running times of the JPV algorithm and the combined algorithm. Our model predicts that JPV algorithm is slower than the combined algorithm when a 512-bit prime is generated on a Pentium 4 system. Although our prediction is contrary to the previous prediction from comparing Fermat-test calls, our prediction corresponds to the experimental results more exactly. In addition, we propose a method to improve the JPV algorithm. With this method, the JPV algorithm can be comparable to the combined algorithm with the same space requirement.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.95-104
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• 2010

The tap water supply in Korea mainly depends on the surface water. However, the advanced water purification process becomes a necessity due to the deterioration of surface water quality and the risk of accidental spill. High cost of water treatment and public concerns make the decision makers turn to riverbank filtration as an alternative to the surface water. Riverbank filtration has been employed for water supply in many developed countries for more than 150 years. In Korea, riverbank filtration has drawn attention since 1990s as a supply source having potential to stably meet the ever-increasing water demand. Some cities located in the Nakdong River Basin are currently supplying water through riverbank filtration. This work studies the site suitability analysis for riverbank filtration using game theory. Theory of games, which is a branch of applied mathematics used in social sciences (most notably economics), biology, engineering and computer science, was applied to candidate locations for the selection of riverbank filtration site. We proposed a policy game model as a new method adopting a probabilistic approach. The model developed turned out to be an effective tool for site selection.

• Structural Engineering and Mechanics
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• v.46 no.1
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• pp.19-37
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• 2013

The optimum design of base isolation system considering model parameter uncertainty is usually performed by using the unconditional response of structure obtained by the total probability theory, as the performance index. Though, the probabilistic approach is powerful, it cannot be applied when the maximum possible ranges of variations are known and can be only modelled as uncertain but bounded type. In such cases, the interval analysis method is a viable alternative. The present study focuses on the bounded optimization of base isolation system to mitigate the seismic vibration effect of structures characterized by bounded type system parameters. With this intention in view, the conditional stochastic response quantities are obtained in random vibration framework using the state space formulation. Subsequently, with the aid of matrix perturbation theory using first order Taylor series expansion of dynamic response function and its interval extension, the vibration control problem is transformed to appropriate deterministic optimization problems correspond to a lower bound and upper bound optimum solutions. A lead rubber bearing isolating a multi-storeyed building frame is considered for numerical study to elucidate the proposed bounded optimization procedure and the optimum performance of the isolation system.

• Geophysics and Geophysical Exploration
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• v.2 no.2
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• pp.112-121
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• 1999

This article reviews the development of geophysical inverse theory. In a series of articles published in 1967, 1968, and 1979, G. Backus and F. Gilbert a trade-off between model resolution and estimation errors in geophysical inverse problems, and gave a criterion to compromise the reciprocal relation. Although the criterion was not clear in the physical point of view, it had been extensively used in the interpretation of geophysical date in the 1970s. This was the starting point of the fruitful development of inverse theory in geophysics. A reasonable criterion to compromise the reciprocal relation was derived to solve linear problems by D. D. jackson in 1979, introducing the concept of a priori information about unknown model parameters. This Jackson's approach was extended to solve nonlinear problems on the basis o probabilistic approach to the inverse problems formulated by A. Tarantola and B. Vallete in 1982. At the end of 1980s ABIC (Akaike Bayesian Information Criterion) was introduced for selecting a more reasonable model in geophysics. Now the date inversion is regarded as the process of extracting new information from observed data, combining in with a priori information about model parameters, and constructing a more clear image of model.

• Steel and Composite Structures
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• v.28 no.2
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• pp.195-207
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• 2018

First-order reliability method (FORM) is enhanced based on the search direction using relaxed conjugate reliability (RCR) approach for the embedded nanocomposite beam under buckling failure mode. The RCR method is formulated using discrete conjugate map with a limited scalar factor. A dynamical relaxed factor is proposed to control instability of proposed RCR, which is adjusted using sufficient descent condition. The characteristic of equivalent materials for nanocomposite beam are obtained by micro-electro-mechanical model. The probabilistic model of nanocomposite beam is simulated using the sinusoidal shear deformation theory (SSDT). The beam is subjected to external applied voltage in thickness direction and the surrounding elastic medium is modeled by Pasternak foundation. The governing equations are derived in terms of energy method and Hamilton's principal. Using exact solution, the implicit buckling limit state function of nanocomposite beam is proposed, which is involved various random variables including thickness of beam, length of beam, spring constant of foundation, shear constant of foundation, applied voltage, and volume fraction of ZnO nanoparticles in polymer. The robustness, accuracy and efficiency of proposed RCR method are evaluated for this engineering structural reliability problem. The results demonstrate that proposed RCR method is more accurate and robust than the excising reliability methods-based FORM. The volume fraction of ZnO nanoparticles and the applied voltage are the sensitive variables on the reliable levels of the nanocomposite beams.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.142-154
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• 1994

In this paper, stochastic imperfection sensitivity analyses of stiffened cylindrical shells under static load are presented. Multimode formulation is performed for the buckling load calculation based on the Donnell's theory and Galerkin approximation. Random imperfection field theory and response surface method are combined with deterministic bucking analysis scheme to perform stochastic imperfection sensitivity analyses of stiffened cylindrical shells considering random geometric imperfection. From the characteristics of probabilistic bucking load, the relation between reliability index and safety parameter can be obtained in addition to the relation between load and reliability index. Those results can be used to determine the range of required safety parameter and acceptable imperfection.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.5
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• pp.73-80
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• 2018

At the early stages of development of high-speed propulsion systems, associated uncertainties cannot be easily modeled into probabilistic distributions, owing to the lack of test data, cost, and difficulty of simulating real-flight environments on the ground. To tackle this issue, in this research, the combustion efficiencies of dual-combustion ramjet engines are assumed to have been provided by experts and quantified by evidence theory. Using quantified uncertainty, the inlet area and combustor exit are optimized while satisfying reliability margins of thrust and thermal choking. The result shows a reasonable design of the engine under uncertain circumstances.

• Journal of the Korean Geotechnical Society
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• v.23 no.11
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• pp.109-117
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• 2007

Uncertainty is pervasive in rock slope stability analysis due to various reasons and subsequently it may cause serious rock slope failures. Therefore, the importance of uncertainty has been recognized and subsequently the probability theory has been used to quantify the uncertainty since 1980's. However, some uncertainties, due to incomplete information, cannot be handled satisfactorily in the probability theory and the fuzzy set theory is more appropriate for those uncertainties. In this study the random variable is considered as fuzzy number and the fuzzy set theory is employed in rock slope stability analysis. However, the previous fuzzy analysis employed the approximate method, which is first order second moment method and point estimate method. Since previous studies used only the representative values from membership function to evaluate the stability of rock slope, the approximated analysis results have been obtained in previous studies. Therefore, the Monte Carlo simulation technique is utilized to evaluate the probability of failure for rock slope in the current study. This overcomes the shortcomings of previous studies, which are employed vertex method. With Monte Carlo simulation technique, more complete analysis results can be secured in the proposed method. The proposed method has been applied to the practical example. According to the analysis results, the probabilities of failure obtained from the fuzzy Monte Carlo simulation coincide with the probabilities of failure from the probabilistic analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.60-64
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• 1997

Designing fuzzy controller, there are difficulties that we have to determine fuzzy rules and shapes of membership functions which are usually obtained by the amount of trial-and-error or experiences from the experts. In this paper, to overcome these defects, genetic algorithms which is probabilistic search method based on genetics and evolution theory are used to determine fuzzy rules and fuzzy membership functions. We design a series compensation fuzzy controller, then determine basic structures, input-output variables, fuzzy inference methods and defuzzification methods for fuzzy controllers. We develop genetic algorithms which may search more accurate optimal solutions. For evaluating the fuzzy controller performances through experiments upon an actual system, we design the fuzzy controllers for the speed control of a DC series motor with nonlinear characteristics and show good output responses to reference inputs.