• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.95-126
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• 2009

The present contribution addresses the parallelization of advanced simulation methods for structural reliability analysis, which have recently been developed for large-scale structures with a high number of uncertain parameters. In particular, the Line Sampling method and the Subset Simulation method are considered. The proposed parallel algorithms exploit the parallelism associated with the possibility to simultaneously perform independent FE analyses. For the Line Sampling method a parallelization scheme is proposed both for the actual sampling process, and for the statistical gradient estimation method used to identify the so-called important direction of the Line Sampling scheme. Two parallelization strategies are investigated for the Subset Simulation method: the first one consists in the embarrassingly parallel advancement of distinct Markov chains; in this case the speedup is bounded by the number of chains advanced simultaneously. The second parallel Subset Simulation algorithm utilizes the concept of speculative computing. Speedup measurements in context with the FE model of a multistory building (24,000 DOFs) show the reduction of the wall-clock time to a very viable amount (<10 minutes for Line Sampling and ${\approx}$ 1 hour for Subset Simulation). The measurements, conducted on clusters of multi-core nodes, also indicate a strong sensitivity of the parallel performance to the load level of the nodes, in terms of the number of simultaneously used cores. This performance degradation is related to memory bottlenecks during the modal analysis required during each FE analysis.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.54-59
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• 2017

This paper presents an efficient finite analysis model and a simulation-based reliability analysis method for stowage device system failure of a container crane with respect to lateral load. A quasi-static analysis model is introduced to simulate the nonlinear resistance characteristics and failure of tie-down and stowage pin, which are the main structural stowage devices of a crane. As a reliability analysis method, a subset simulation method is applied considering the uncertainties of later load and mechanical characteristic parameters of stowage devices. An efficient Markov chain Monte Carlo (MCMC) method is applied to sample random variables. Analysis result shows that the proposed model is able to estimate the probability of failure of crane system effectively which cannot be calculated practically by crude Monte Carlo simulation method.

• Structural Engineering and Mechanics
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• v.25 no.3
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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.

• IE interfaces
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• v.23 no.1
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• pp.1-11
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• 2010

There are various ways to manage the patient flow of the hospital outpatient system. However, it is difficult to apply many implementation solutions to the real outpatient system at once. Because first, the expected effects of each different solution are very much depend on the real situation of the system and applied other solutions, and second, owing to the limited resources, each solution should be implemented according to the priority. In order to overcome these difficulties, this paper focuses on proposing the comprehensive subset of implementation solutions, which is one of the most effective among various kinds of subsets, and verifying the effects of it. The comprehensive subset of solutions is derived from conducting design of experiments and simulation which determine the optimum set of different solutions and analyze the particular interactions and priority order among them. This implementation strategy can solve the difficulties of applying different kinds of various solutions to the hospital outpatient system.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.72-79
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• 2010

Device-to-device(D2D) links which share resources in a cellular network present a challenge in radio resource management due to the potentially severe interference they may cause to the cellular network. In this paper, a resource allocation scheme based on subset reuse methods is proposed to minimize the interference from the D2D links. We consider an adaptive group-wise subset reuse method to enhance the efficiency of frequency resource allocation for cellular and D2D links. A power optimization scheme is also proposed for D2D links if cellular links are interfered by adjacent D2D transmissions. The computer simulation results show that performance gain is obtained in link SINR, and total cell throughput increases as nearby traffic becomes more dominant.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.3
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• pp.31-38
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• 2010

In this paper, we have analyzed and compared the BER and the throughput performance through the computer simulation, after applying several MIMO schemes on the MIMO-OFDM system. Then, the throughput performance of the proposed system, Adaptive-MCM, is analyzed. As a result, the MIMO-OFDM Adaptive-MCM system proposed has a higher average data rate than Non Adaptive-MCM system through the improvement of Trade-off problem between throughput and SNR.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.12
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• pp.1195-1202
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• 1991

This paper describes a newly developed method of production simulation by using the Mixture of Cumulant Approximation (MOCA). In this method, the load is modelled as random variable (r.v.) which can be interpreted in terms of partitioning the load into various categories. We can consider the load shape of multi-modal characteristics. The number of load category and demarcation points of each load category are calculated automatically by using interpolation and least square method. Each generating unit of a supply system is modelled as r.v. of unit outage capacity according to the number of unit outage subset. Since the computation burden of each subset's moments increases exponentially as units are convolved to the system, we further derive the specific recursive formulae. In simulating the energy limited units, hydro unit simulation is performed using Energy Invariance Property and the simulation of pumped storage unit is modelled as compulsory and economic operations. The proposed MOCA method is applide to the test systems and the results are compared with those of cumulant and Booth Baleriaux method. It is verified that the MOCA method is considerably reliable and stable both pathological and well behaved system.