• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.87-94
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• 1997

There are many kinds of complicated equipments in the chemical plants. So the chemical plants have high possibility of accidents. Hazard analysis is one of the basic tasks to ensure the safety of chemical plants. However, it has many shortcomings. To overcome the problems, there have been attempts to automate this work by utilizing computer technology, particularly knowledge-based technique. However, many of the past approaches are lacking in properties: safeguard consideration, accident diversity, cause and consequence diversity, pathway leading to accidents, and various hazard analysis reasoning. Therefore, in this study, three analysis algorithms were proposed using multimodel approach, and a hazard analysis system, AHA, was developed on G2. The case study was solved with AHA system successfully.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1443-1451
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• 2020

The objective of this study is to design a robust power control system for a small pressurized water reactor (PWR) to achieve stable power operations under conditions of external disturbances and internal model uncertainties. For this purpose, the multiple-input multiple-output transfer function models of the reactor core at five power levels are derived from point reactor kinetics equations and the Mann's thermodynamic model. Using the transfer function models, five local reactor power controllers are designed using an H infinity (H_∞) mixed sensitivity method to minimize the core power disturbance under various uncertainties at the five power levels, respectively. Then a multimodel approach with triangular membership functions is employed to integrate the five local controllers into a multimodel robust control system that is applicable for the entire power range. The performance of the robust power system is assessed against 10% of full power (FP) step load increase transients with coolant inlet temperature disturbances at different power levels and large-scope, rapid ramp load change transient. The simulation results show that the robust control system could maintain satisfactory control performance and good robustness of the reactor under external disturbances and internal model uncertainties, demonstrating the effective of the robust power control design.

• 한국가스학회:학술대회논문집
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• 1997.09a
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• pp.142-147
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• 1997

• 한국가스학회:학술대회논문집
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• 1998.09a
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• pp.41-46
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• 1998

• 한국가스학회:학술대회논문집
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• 2001.10a
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• pp.154-157
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• 2001

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.109-128
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• 2020

A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.