• Journal of The Korea Institute of Healthcare Architecture
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• v.26 no.2
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• pp.63-69
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• 2020

Purpose: The purpose of this study is to identify the neuro architecture items and detailed elements that can be considered for each detailed space in the future medical space design development through the development of a checklist of neuro architecture elements that can be utilized in medical space design. Methods:: This study first develops the neuro architecture element through theoretical research and prepares the basic plan for the checklist through consultation with the employees of the design company in which the researcher works. Finally, a checklist was developed through a survey of nine experts, including designers, hospital staff, and professors. Results: The result of this study 1) The neuro architecture component was developed in seven categories: light, color, sound, air, image, nature, ergonomic furniture and equipment. 2) Specifically, it consists of 49 elements including 7 light elements, 7 color elements, 5 sound elements, 4 air elements, 11 image elements, 6 elements in nature, 9 elements in ergonomic furniture and equipment. It was. 3) Although each of the detailed elements is more preferred according to the space, in general, all the elements should be considered in the context of the hospital space design. Implications: The checklist on the neuro architecture element will enable the development of the most faithful design as an efficient and useful tool for applying the neuro architecture philosophy that considers human beings in hospital design and pursues healing and happiness.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2283-2285
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• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.474-476
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• 1998

In this paper, a multi variable neuro-fuzzy controller for a boiler-turbine system is designed. Two architectures are used. The first consists of boiler-turbine system identification and the second is designing a controller. A generalized backpropagation algorithm is developed and used to train the neuro-fuzzy controller. Designed controller is good tracking property and rejects the input and output disturbances. The results of the proposed design method is verified through simulation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Knowledge Management Research
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• v.1 no.1
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• pp.65-80
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• 2000

The main purpose of this study is to answer a question that how a company can be a learning organization producing useful knowledge by applying neuro-cybernetics approach. This approach borrows its working principles from the human body systems. The current study urges that the principles can be applied to build a learning organization. System 1 to 5, the core parts of neuro-cybernetics, are explained. And it is explored that how these systems can be designed for a company to be a learning organization. Limitations of the current study are discussed at the end of the paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.454-457
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• 2004

In this paper, control method proposed for effective speed control of the induction motor indirect vector control. For the induction motor drive, indirect vector control scheme that controls torque current and flux current of the stator current independently so that it can have improved dynamics. Also, neuro-fuzzy algorithm employed for torque current control in order to optimal speed control The proposed neuro-fuzzy algorithm can be applied to the precise speed control of an induction motor drive system or the field of any other power systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.21-27
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• 1997

Neuro-fuzzy network that combined advantages of the neural network in learning and fuzzy system in inferencing can be used to establish a system model in the design of a controller. In this paper, we presented the neuro-fuzzy system that can be able to generated a linguistic fuzzy model which results in a similar input/output response to the original system. The network was used to model a system. We tested the performance ot the neuro-fuzzy network through computer simulations.