• Journal of the Korean Operations Research and Management Science Society
• /
• v.22 no.4
• /
• pp.1-16
• /
• 1997

The cell loss probability recommended in the B-ISDN is in the range $10^{-8}~10^{-12}$. When a simulation technique is used to analyze the performance of the ATM node, an enormous amount of computer processing time is required. In this study, we derive an importance sampling simulation technique that can be used to evaluate the performance of the ATM node very quickly, that is, the probability that the queue size at the ATM node reaches some large value N. The simulation results show that the backlog probability obtianed by the importance saimpling simulation is very close to that obtained by the ordinary simulation and the computer time can be reduced drastically by the importance sampling simulation.

• Journal of the Korea Society for Simulation
• /
• v.13 no.3
• /
• pp.1-10
• /
• 2004

The cell loss probability required in the ATM network is in the range of 10$^{-9}$ ∼10$^{-12}$ . If Monte Carlo simulation is used to analyze the performance of the ATM node, an enormous amount of computer time is required. To obtain large speed-up factors, importance sampling may be used. Since the Markov-modulated processes have been used to model various high-speed network traffic sources, we consider discrete time single server queueing systems with Markov-modulated arrival processes which can be used to model an ATM node. We apply importance sampling based on the Large Deviation Theory for the performance evaluation of, MMBP/D/1/K, ∑MMBP/D/1/K, and two stage tandem queueing networks with Markov-modulated arrival processes and deterministic service times. The simulation results show that the buffer overflow probabilities obtained by the importance sampling are very close to those obtained by the Monte Carlo simulation and the computer time can be reduced drastically.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.24 no.63
• /
• pp.33-43
• /
• 2001

To estimate the reliability of a large and complex network with a small variance, we propose two dynamic Monte Carlo sampling methods: the sequential minimal cut set (SMCS) and the sequential minimal path set (SMPS) methods. These methods do not require all minimal cut sets or path sets to be given in advance and do not simulate all arcs at each trial, which can decrease the valiance of network reliability. Based on the proposed methods, we develop the importance sampling estimators, the total hazard (or safety) estimator and the hazard (or safety) importance sampling estimator, and compare the performance of these simulation estimators. It is found that these estimators can significantly reduce the variance of the raw simulation estimator and the usual importance sampling estimator. Especially, the SMCS algorithm is very effective in case that the failure probabilities of arcs are low. On the contrary, the SMPS algorithm is effective in case that the success Probabilities of arcs are low.

• Communications for Statistical Applications and Methods
• /
• v.27 no.3
• /
• pp.327-347
• /
• 2020

We consider a credit portfolio with highly skewed exposures. In the portfolio, small number of obligors have very high exposures compared to the others. For the Bernoulli mixture model with highly skewed exposures, we propose a new importance sampling scheme to estimate the tail loss probability over a threshold and the corresponding expected shortfall. We stratify the sample space of the default events into two subsets. One consists of the events that the obligors with heavy exposures default simultaneously. We expect that typical tail loss events belong to the set. In our proposed scheme, the tail loss probability and the expected shortfall corresponding to this type of events are estimated by a conditional Monte Carlo, which results in variance reduction. We analyze the properties of the proposed scheme mathematically. In numerical study, the performance of the proposed scheme is compared with an existing importance sampling method.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.21 no.5
• /
• pp.405-409
• /
• 2009

Reliability analysis of coastal structure using importance sampling was shown. When Monte Carlo simulation is used to evaluate overturng failure probability of coastal structure, very low failure probability leads to drastic increase in simulation time. However, importance sampling which uses randomly chosen design candidates around the failure surface makes it possible to analyze very low failure probability efficiently. In the numerical example, failure probability of caisson type quay wall was analyzed by using importance sampling and performance according to the level of failure probability was shown.

• Structural Engineering and Mechanics
• /
• v.84 no.3
• /
• pp.323-335
• /
• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• Structural Engineering and Mechanics
• /
• v.5 no.2
• /
• pp.115-126
• /
• 1997

Importance sampling methods have been developed with the aim of reducing the computational costs inherent in Monte Carlo methods. This study proposes a new algorithm called the adaptive kernel method which combines and modifies some of the concepts from adaptive sampling and the simple kernel method to evaluate the structural reliability of time variant problems. The essence of the resulting algorithm is to select an appropriate starting point from which the importance sampling density can be generated efficiently. Numerical results show that the method is unbiased and substantially increases the efficiency over other methods.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1999.10a
• /
• pp.351-358
• /
• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• Structural Engineering and Mechanics
• /
• v.32 no.1
• /
• pp.21-36
• /
• 2009

An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.

• The Journal of the Korea Contents Association
• /
• v.13 no.4
• /
• pp.53-63
• /
• 2013

This paper presents an importance sampling(IS) embedded experimental frame(EF) design for efficient Monte Carlo (MC) simulation. To achieve IS principles, the proposed EF contains two embedded sub-models, which are classified into Importance Sampler(IS) and Bias Compensator(BC) models. The IS and BC models stand between the existing system model and EF, which leads to enhancement of model reusability. Furthermore, the proposed EF enables to achieve fast stochastic simulation as compared with the crude MC technique. From the abstract two case studies with the utilization of the proposed EF, we can gain interesting experimental results regarding remarkable enhancement of simulation performance. Finally, we expect that this work will serve various content areas for enhancing simulation performance, and besides, it will be utilized as a tool to understand and analyze social phenomena.