• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.647-649
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• 2000

This paper presents a optimization procedure by using Response Surface Methodology(RSM) to determine design Parameters for reducing cogging torque in BLDC motor of Electric Power Steering (EPS). RSM is achieved through using the experiment design method in combination with Finite Element Method and well adapted to make analytical model for a complex problem considering a lot of interaction of these parameters. Moreover, Sequential Quadratic Problem (SQP) method is used to solve the resulting of constrained nonlinear optimization problem.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.87-93
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• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.1
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• pp.65-72
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• 2016

In this study, a fully active transfemoral prothesis with a knee joint is designed considering stair walking conditions. Since the torque at the knee joint required for stair walking condition is relative high compared with the one in normal walking condition, the proposed design has high torque generating mechanism. Moreover, the transfemoral prothesis is designed in compact size to reduce its weight, which is related to comfortable fit and fatigue of patients. Flat type BLDC motor is used for simple and compact structure and various components are used to generate required torque with target working angle and speed. The weight reduction of structure is carried out using optimization method after the initial design process is complete. The optimization is conducted under the load conditions of stair walking. The optimized design is validated via finite element analysis and experiments. As a result, the weight is reduced using topology and shape optimization but maintaining the safety of structure. Also the space efficiency is improved due to its compact size.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.158-160
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• 2006

The structure of interior permanent magnet type brushless DC motor (IPM type BLDC motor) has high saliency ratios that produce additional reluctance torque. But this structure has a significant cogging torque that may cause acoustic noise and vibration. In this paper describes an optimization of flux barrier in order to reduce cogging torque and torque ripple in IPM type BLDC motor using the Taguchi methods. The optimal design consider- ed noises such as manufacturing tolerances of permanent magnet size. So, optimal design ensures that torque performance is insensitive to noise.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1980-1990
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• 2017

A study on the multi-objective optimization of Interior Permanent-Magnet Synchronous Motors (IPMSMs) with 2, 3, 4 and 5 flux barriers per magnetic pole, based on Genetic Algorithm (GA) is presented by considering the aspect of irreversible demagnetization. Applying the 2004 Toyota Prius single-layer IPMSM as the reference machine, the asymmetrical two-, three-, four- and five-layer rotor models with the same amount of Permanent-Magnets (PMs) is presented to improve the torque characteristics, i.e., reducing the torque pulsation and increasing the average torque. A reduction of the torque pulsations is achieved by adopting different and asymmetrical flux barrier geometries in each magnetic pole of the rotor topology. The demagnetization performance in the PMs is considered as well as the motor performance; and analyzed by using finite element method (FEM) for verification of the optimal solutions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.20-29
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• 2005

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with a direct torque control (DTC). The equipment circuit considered with iron losses in RSM is analyzed theoretically, and the optimal current ratio between torque current and exiting current component are derived analytically. For the RSM driver, torque dynamic can be maintained with DTC using TMS320F2812 DSP Controller even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the Backpropagation Neural Network is adapted. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 inductance ratio of d/q.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.431-434
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• 2002

This paper presents an implementation of direct torque control(DTC) of Reluctance Synchronous Motor(RSM) with an efficiency optimization using the 32bit DSP TMS320C31. The influence of iron loss can not neglected as high speed and precision torque control of RSM, so the optimal current ration between torque current and exiting current analytically derived to drive RSM at maximum efficiency For RSM, torque dynamics can be maintained even with controlling the flux level because the generated torque is direct]y proportional to the stator current. The experimental results for an RSM are presented to validate the applicability of the proposed method. The developed control system is shown high efficiency features with 1.0Kw RSM having 2.57 ratio of d/q reluctance.

• Journal of Magnetics
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• v.21 no.1
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• pp.78-82
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• 2016

Cogging torque will affect the performance of a permanent magnet Brushless DC Motor (BLDCM), thus the reduction of cogging torque is key for BLDCM optimization. In this paper, the phase shifting of cogging torque for a fractional-slot concentrated winding BLDCM is analyzed using the Maxwell tensor method. Moreover, a 9-slot 10-pole concentrated winding BLDCM driven by ideal square waveform is studied with the finite element method (FEM). An effective method to reduce the cogging torque is obtained by adjusting the slot opening. In addition, the influences of different slot openings on back electromotive force (back-EMF), air gap flux density and flux linkage are investigated and experimentally validated using the prototype BLDCM.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2690-2695
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• 2007

This work presents numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis. Reynolds-averaged Navier-Stokes (RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

• International Journal of Air-Conditioning and Refrigeration
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• v.16 no.1
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• pp.1-8
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• 2008

Numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm(NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis is presented in this work. Reynolds-averaged Navier-Stokes(RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.