• Journal of Power Electronics
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• v.19 no.4
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• pp.989-999
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• 2019

This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Fault-tolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.50.1-50
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• 2001

Dynamic optimization with partially structured model in a slurry-phase HDPE reactor is implemented by the modified hierarchical dynamic optimization. Optimal trajectories of MI and density of HDPE are calculated as controlled variables and optimal profiles of the concentrations of ethylene, hydrogen and comonomer are calculated as manipulated variables in dynamic optimization. MI, density, the concentrations of ethylene, hydrogen and comonomer are used as controlled variables and flow rates of ethylene, hydrogen and comonomer are sued as manipulated variables in control implementation. Two-level hierarchical method is applied in dynamic optimization to reduce computation time. In the upper level formulation ...

• Journal of Korean Society of Steel Construction
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• v.12 no.1 s.44
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• pp.93-102
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• 2000

Substructuring-based hierarchical approach for design analysis and optimization of structural frames is presented in this study. The conceptual framework of this method is in the hierarchical modeling for design processes as well as structural systems and the methodology combining substructuring analysis and multilevel optimization. Mathematical models for analysis and synthesis are established on the common basis of substructuring systems. Modularized behavioral analysis, design sensitivity analysis and optimization are linked and integrated on the mathematical and structural basis of substructuring. Substructures are coordinated with the active constraints for system level and the weight ratio criteria. Numerical examples for test frames show the validity and effectiveness of the present approach.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.204-206
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• 2007

In this study, we introduce the optimization method of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation, The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. Particularly, in parameter identification, we use the UNDX operator which uses multiple parents and generate offsprings around the geographic center off mass of these parents.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.6
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• pp.58-65
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• 1993

In this research, we developed a new algorithm for hierarchical placement of integrated circuits. For efficient placement of a large circuit, the given circuit is recursively partitioned to form a hierarchy tree and then simulated-annealing-based placement method is applied at each level of the hierarchy to find a near optimum solution. During the placemtnt, global optimization is performed at high levels of the hierarchy and local optimization is performed at low levels. When compared with conventional placement methods, the new hierarchical placement method produced favorable results.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Journal of Environmental Science International
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• v.4 no.1
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• pp.71-80
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• 1995

A discrete state space model for a multiple-reach river system is formulated using the dynamics of biochemical oxygen demand(BOD) and dissolved oxygen(DO). A hierarchical optimization technique, which is applicable to large-scale systems with time-delays in states, is also described to control stream quality in a river as an optimal manner based on the interaction prediction method. The steady state tracking error of the proposed method is determined analytically and a necessary and sufficient condition on which a constant target tracking problem has zero steady-state error is derived. Computer simulations for the river pollution model illustrate the algorithm.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1635-1645
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• 1999

In this paper, an intelligent resynthesis technique for timing optimization at the architecture-level has been studied. The proposed technique can remedy the problem which may occur in combinational timing optimization techniques applied to circuits which have the hierarchical subblock structure at the architectural-level. The approach first tries to maintain the original hierarchical subblock while minimizing the longest delay of whole circuit. This paper tries to find a new approach to timing optimization for circuits which have hierarchical structure at architectural-level, and has verified its effectiveness experimentally. We claim its usefulness from the fact that most designers design the circuits hierarchically due to the increase of design complexity.