• Smart Structures and Systems
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• v.31 no.2
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• pp.113-130
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• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1426-1435
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• 1990

Dynamic behaviour of point tracking system mounted on flying vehicle shaking in a random manner is investigated and the system dynamic is represented by nonlinear stochastic equations. 2-D.O.F. nonlinear stochastic equations are successfully transformed to linear stochastic equations via equivalent linearization procedure in stochastic sense. Newly developed hybrid technique is used to obtain response statistics of the system under non-white random excitation, which is proved to be remarkably accurate one by performing stochastic simulation.

• Structural Engineering and Mechanics
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• v.49 no.1
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• pp.41-64
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• 2014

The main objective of this article is the exploitation of a stochastic hybrid mesh-free method based on stochastic generalized finite difference (SGFD), Newmark finite difference (NFD) methods and Monte Carlo simulation for thermoelastic wave propagation and coupled thermoelasticity analysis based on GN theory (without energy dissipation). A thick hollow cylinder with Gaussian uncertainty in mechanical properties is considered as an analyzed domain for the problem. The effects of uncertainty in mechanical properties with various coefficients of variations on thermo-elastic wave propagation are studied in details. Also, the time histories and distribution on thickness of cylinder of maximum, mean and variance values of temperature and radial displacement are studied for various coefficients of variations (COVs).

• Proceedings of the Korea Society for Simulation Conference
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• 2000.04a
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• pp.57-66
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• 2000

Production-distribution planning is the most important part in supply chain management (SCM). To solve this planning problem, either analytic or simulation approach has been developed. However these two approaches have their own demerits in problem solving. In this paper, we propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation method to solve production-distribution problems in supply chain. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and adjusted by the proposed specific process according to the results from independently developed simulation model which includes general production-distribution characteristics.

• ETRI Journal
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• v.33 no.5
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• pp.704-711
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• 2011

This paper proposes hybrid distributed stochastic addressing (HDSA), which combines the advantages of distributed addressing and stochastic addressing, to solve the problems encountered when constructing a network in a ZigBee-based wireless sensor network. HDSA can assign all the addresses for ZigBee beyond the limit of addresses assigned by the existing distributed address assignment mechanism. Thus, it can make the network scalable and can also utilize the advantages of tree routing. The simulation results reveal that HDSA has better addressing performance than distributed addressing and better routing performance than other on-demand routing methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.145-154
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• 1994

This paper proposes an efficient method for improving the training performance of the neural network by using a hybrid of a stochastic approximation and a backpropagation algorithm. The proposed method improves the performance of the training by appliying a global optimization method which is a hybrid of a stochastic approximation and a backpropagation algorithm. The approximate initial point for a stochastic approximation and a backpropagation algorihtm. The approximate initial point for fast global optimization is estimated first by applying the stochastic approximation, and then the backpropagation algorithm, which is the fast gradient descent method, is applied for a high speed global optimization. And further speed-up of training is made possible by adjusting the training parameters of each of the output and the hidden layer adaptively to the standard deviation of the neuron output of each layer. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to that of the backpropagation, the Baba's MROM, and the Sun's method with randomized initial point settings. The results of adaptive adjusting of the training parameters show that the proposed method further improves the convergence speed about 20% in training.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.784-786
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• 1996

This paper presents some stability simulations using EMTP to see the effect of EHV Lines with hybrid reclosing on power system stability. The Hybrid reclosing on EHV lines can improve the reliability of power systems. These studies is performed as a part of the research work which is aimed to find proper scheme of reclosing for EHV lines in Korea. Completing a number of simulation works with the diverse conditions such as ones reported in this paper, some decisions could be made through some stochastic approches for the simulation results for the proper scheme of reclosing for Korean systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.525-528
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• 2005

Much of the study has been dong on the design of dual controller that guarantee the stability and improvement of the system performance. A dual controller concept is proposed to consist of first controller estimates the control law and second controller suppresses the combined noises due to numerical error and internal noise as well. These irregular disturbances are not only increasing the fatigue but also destabilize the system because of unwanted output performance. The 'stochastic controller' is used to suppress the irregular random disturbance. Simulation is conducted to reveal that the proposed dual stochastic controller is highly efficient one to control a system hybrid noises.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.757-763
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• 2005

Much of the study has been done on the design of dual controller that guarantee the stability and improvement of the system performance. A dual controller concept is proposed to consist of first controller estimates the control law and second controller suppresses the combined noises due to numerical error and internal noise as well. These Irregular disturbances are not only increasing the fatigue but also destabilize the system because of unwanted output Performance. The 'stochastic controller' is used to suppress the irregular random disturbance. Simulation is conducted to reveal that the proposed dual stochastic controller is highly efficient one to control a system hybrid noises.