• Structural Engineering and Mechanics
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• 제25권3호
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• pp.347-363
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• 2007

A novel methodology, referred to as Auxiliary Domain Method (ADM), allowing for a very efficient solution of nonlinear reliability problems is presented. The target nonlinear failure domain is first populated by samples generated with the help of a Markov Chain. Based on these samples an auxiliary failure domain (AFD), corresponding to an auxiliary reliability problem, is introduced. The criteria for selecting the AFD are discussed. The emphasis in this paper is on the selection of the auxiliary linear failure domain in the case where the original nonlinear reliability problem involves multiple objectives rather than a single objective. Each reliability objective is assumed to correspond to a particular response quantity not exceeding a corresponding threshold. Once the AFD has been specified the method proceeds with a modified subset simulation procedure where the first step involves the direct simulation of samples in the AFD, rather than standard Monte Carlo simulation as required in standard subset simulation. While the method is applicable to general nonlinear reliability problems herein the focus is on the calculation of the probability of failure of nonlinear dynamical systems subjected to Gaussian random excitations. The method is demonstrated through such a numerical example involving two reliability objectives and a very large number of random variables. It is found that ADM is very efficient and offers drastic improvements over standard subset simulation, especially when one deals with low probability failure events.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.79-89
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• 2016

In this paper, we proposed the object classification method using genetic and dynamic random forest consisting of optimal combination of unit tree. The random forest can ensure good generalization performance in combination of large amount of trees by assigning the randomization to the training samples and feature selection, etc. allocated to the decision tree as an ensemble classification model which combines with the unit decision tree based on the bagging. However, the random forest is composed of unit trees randomly, so it can show the excellent classification performance only when the sufficient amounts of trees are combined. There is no quantitative measurement method for the number of trees, and there is no choice but to repeat random tree structure continuously. The proposed algorithm is composed of random forest with a combination of optimal tree while maintaining the generalization performance of random forest. To achieve this, the problem of improving the classification performance was assigned to the optimization problem which found the optimal tree combination. For this end, the genetic algorithm methodology was applied. As a result of experiment, we had found out that the proposed algorithm could improve about 3~5% of classification performance in specific cases like common database and self infrared database compare with the existing random forest. In addition, we had shown that the optimal tree combination was decided at 55~60% level from the maximum trees.

• Structural Engineering and Mechanics
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• 제55권3호
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• pp.461-472
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• 2015

The existence of initial imperfections in manufacturing or assembly of double-layer space structures having hundreds or thousands of members is inevitable. Many of the imperfections, such as the initial curvature of the members and residual stresses in members, are all random in nature. In this paper, the probabilistic effect of initial curvature imperfections in the load bearing capacity of double-layer grid space structures with different types of supports have been investigated. First, for the initial curvature imperfection of each member, a random number is generated from a gamma distribution. Then, by employing the same probabilistic model, the imperfections are randomly distributed amongst the members of the structure. Afterwards, the collapse behavior and the ultimate bearing capacity of the structure are determined by using nonlinear push down analysis and this procedure is frequently repeated. Ultimately, based on the maximum values of bearing capacity acquired from the analysis of different samples, structure's reliability is obtained by using Monte Carlo simulation method. The results show the sensitivity of the collapse behavior of double-layer grid space structures to the random distribution of initial imperfections and supports type.

• 한국한의학연구원논문집
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• 제13권1호통권19호
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• pp.147-152
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• 2007

As a result to amplifying 12 samples of 'Sok-dan' through an random amplified polymorphic DNA (RAPD) method using eighteen DEC and URP primers, distinct band forms enabling discrimination of Phlomus umbrosa and Dipsacus asperoides were observable in the UBC 320 primer, UBC 367 primer, UBC 385 primer, UBC 414 primer, UBC 423 primer, URP 3 primer, URP 5 primer and URP 9 primer. The polymorph result amplified with a random primer was evaluated through Gelcompar II, showing a result dividable into two groups. The divided groups were the dried sample group of Dipsacus asperoides and the group of Phlomis umbrosa. In order to recognize the distinction between Dipsaci Radix types, the genetic variation of 'Sok-dan' produced domestically and imported was evaluated through RAPD, and the potential to distinguish these in forms of dried medicine was identified, presenting a method to authentification of Phlomis umbrosa and Dispacus asperoides.

• 대한수학회보
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• 제59권2호
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• pp.285-301
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• 2022

In this paper, we mainly study the random sampling and reconstruction of signals living in the subspace V^p(𝚽, 𝚲) of L^p(ℝ^d), which is generated by a family of molecules 𝚽 located on a relatively separated subset 𝚲 ⊂ ℝ^d. The space V^p(𝚽, 𝚲) is used to model signals with finite rate of innovation, such as stream of pulses in GPS applications, cellular radio and ultra wide-band communication. The sampling set is independently and randomly drawn from a general probability distribution over ℝ^d. Under some proper conditions for the generators 𝚽 = {𝜙_λ : λ ∈ 𝚲} and the probability density function 𝜌, we first approximate V^p(𝚽, 𝚲) by a finite dimensional subspace V^p_N (𝚽, 𝚲) on any bounded domains. Then, we prove that the random sampling stability holds with high probability for all signals in V^p(𝚽, 𝚲) whose energy concentrate on a cube when the sampling size is large enough. Finally, a reconstruction algorithm based on random samples is given for signals in V^p_N (𝚽, 𝚲).

• Earthquakes and Structures
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• 제14권4호
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• pp.313-322
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• 2018

The present study is dedicated to investigate the SH body-as well as Love-waves propagation effects in porous media with uncertain porosity and permeability. A unified formulation of the governing equations for one-dimensional (1-D) wave propagation in anisotropic porous layered media is presented deterministically. The uncertainties around the above two cited parameters are taken into account by random fields with the help of Monte Carlo Simulations (MCS). Random samples of the porosity and the permeability are generated according to the normal and lognormal distribution functions, respectively, with a mean value and a coefficient of variation for each one of the two parameters. After performing several thousands of samples, the mathematical expectation (mean) of the solution of the wave propagation equations in terms of amplification functions for SH waves and in terms of dispersion equation for Love-waves are obtained. The limits of the Love wave velocity in a porous soil layer overlaying a homogeneous half-space are obtained where it is found that random variations of porosity change the zeros of the wave equation. Also, the increase of uncertainties in the porosity (high coefficient of variation) decreases the mean amplification function amplitudes and shifts the fundamental frequencies. However, no effects are observed on both Love wave dispersion and amplification function for random variations of permeability. Lastly, the present approach is applied to a case study in the Adapazari town basin so that to estimate ground motion accelerations lacked in the fast-growing during the main shock of the damaging 1999 Kocaeli earthquake.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.395-406
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• 2001

In this paper, we propose the test statistic for the umbrella alternatives on c-samples ranked set samples(RSS), where the peak of the umbrella is known. We obtain the asymptotic property of the proposed test statistic and the asymptotic relative efficiencies of the proposed test statistic with respect to U-statistic based on simple random samples(SRS). From the simulation work, we compare the empirical powers of the proposed test statistic with U-statistic based on SRS.

• Mycobiology
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• 제30권1호
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• pp.5-12
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• 2002

A total of 100 samples of chicken-viscera were collected from different poultry-slaughtering houses in Austria;(20 samples of each of gizzard, heart, intestine, liver and spleen). Intestine and gizzard were heavy contaminated with moulds than other examined visceral organs($4.4{\times}10^5$ and $2.6{\times}10^4$ colonies/1g of the samples, respectively). Fungal contamination was not detected in all samples of heart and spleen. Eighty-five mould isolates were collected from the examined samples, the majority of isolates belonging to Aspergillus glaucus group(20.0%) and Trichoderma(14.1%). These isolates comprised 15 species belonging to 9 genera. Members of Aspergillus glaucus(telomorph: Eurotium) group and Trichoderma were further confirmed their identification using random amplified polymorphic DNA-polymerase chain reaction(RAPD-PCR) technique.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.331-337
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• 2004

In the analysis of massive volume data, a neural network model is a useful tool. To implement the Neural network model, it is important to select initial value. Since the initial values are generally used as random value in the neural network, the convergent performance and the prediction rate of model are not stable. To overcome the drawback a possible method use samples randomly selected from the whole data set. That is, coefficients estimated by logistic regression based on the samples are the initial values.

• Journal of the Korean Data and Information Science Society
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• 제28권3호
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• pp.515-524
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• 2017

이 연구에서는 분류를 위한 RF (random forest)의 크기 결정에 유용한 승리표차 MV (margin of victory)에 기반한 불일치 측도를 제안하고자 한다. 여기서 MV는 현재의 RF에서 1등과 2등을 차지하는 집단이 무한 RF에서 차지하는 승리표차이다. 구체적으로 -MV가 양수이면 현재와 무한 RF 사이에 1등과 2등인 집단에서 불일치가 생긴다는 점에 착안하여, max(-MV, 0)을 하나의 불일치 측도로 제안한다. 이 불일치 측도에 근거하여 RF의 크기 결정에 적절한 진단통계량을 제안하며, 또한 이 통계량의 이론적인 점근분포를 유도한다. 마지막으로 이 통계량을 최근에 제안된 진단통계량들과 소표본 하에서 성능을 비교하는 모의실험을 실행한다.