• Journal of Magnetics
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• v.22 no.2
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• pp.291-298
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• 2017

In this paper, the distribution of internal magnetic induction intensity of oil-film bearing twined solenoids was proposed. The magnetic field was generated by solenoids and magnetized bearing. The magnetized bearing was simplified as solenoid model. The mathematical model of magnetic induction intensity at any point of finite solenoid was deduced. Through experiment method, the distribution of the internal magnetic induction intensity of oil-film bearing and the magnetizing current formula of bearing was obtained. Further, the magnetic induction intensity distribution of magnetization bearing was solved successfully. The results showed that the magnetic induction was a second-degree parabola with open upwards along the axial plane and the distribution of magnetic induction intensity was opposite to the rule of magnetic induction intensity generated by solenoids. In addition, the magnetic flux density increased linearly with the increase of current.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.458-460
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• 1996

In this study, the full-state flux observer is designed in the synchronous DQ-frame and the speed adaptation rule is derived by using the MRAS(Model Reference Adaptive System) theory. In this rule, the induction motor becomes a reference model and the flux observer is taken as a adjustable model. A guideline of the adaptation gain is investigated for the precise and stable speed adaptation and the proposed scheme is compared with the conventional one designed in the stationary DQ-frame.

• The Pure and Applied Mathematics
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• v.11 no.3
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• pp.189-196
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• 2004

In this article, using the L'Hospital rule, mathematical induction, the trigonometric power formulae and integration by parts, some integral formulae for the improper integrals ${\int}_0^{\infty}$[sin$^{2m}({\alpha}x)]/(x^{2n})dx$ AND ${\int}_0^{\infty}$[sin$^{2m+1}({\alpha}x)]/(x^{2n+1})dx$ are established, where m $\geq$ n are all positive integers and $\alpha$$\neq$ 0.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.173-183
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• 1998

In this paper, an auto-tuning method for fuzzy controller's membership functions based on the neural network is presented. The neural network emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and the reformed fuzzy controller uses for speed control of induction motor. Thus, in the case of motor parameter variation, the proposed method is superior to a conventional method in the respect of operation time and system performance. 32bit micro-processor DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzzy control algorithm. Through computer simulation and experimental results, it is confirmed that the proposed method can provide more improved control performance than that PI controller and conventional fuzzy controller.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.74-76
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• 2009

In this paper, search method and sliding mode observer are developed for efficiency optimization of induction motor. The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. The search controller is based on the "Rosenbrock" method and finds the flux level at the minimum input power of induction motor. Once this optimal flux level has been determined, this information is utilized to update the rule base of a fuzzy controller A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is also used to compensate for mechanical uncertainties in the speed control of induction motor. Simulation results are presented to validate the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.10
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• pp.443-445
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Fuzzy-neural controller to make up these controllers in parallel is proposed and the speed control of an induction motor is performed using the proposed controller Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.47-52
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• 2014

Due to of high intensity, lower noise and easy controllability of the heat, induction heating system became well known. Induction heating method has been suggested as substitute for the gas heat source and adopted in the automation of curved hull forming system. In this study, an investigation was accomplished to find the effects on the change of material properties when the induction heating was applied on the mild steel plate. Plates were heated using weaving method to get sufficiently heat affected zone and then cooled with water or in the air. The mechanical properties of the heated plate were evaluated. As results, the tensile test, impact test and microstructures satisfied the class rule.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.128-132
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• 2001

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.389-394
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• 1997

Consider a tetrhedron is composed of six dihedral angles .phi.(i=1,2..., 6), and a vertex of a tetrahedron is also three dihedral angles. It will assume that a vertex A, for an example, is composed of there angles definded such as .alpha..betha. and .gamma. !. then there is a corresponding angle can be given as .phi1.,.phi2.,.phi3.. Here, in order to differentiate between a conventional triangle and dihedral angle, if a dihedral angle degined in this paper is symbolized as .phi..LAMBDA.,the value of cos.theta.of .phi./sab a/, in a trigonometric function rule,can be defined to tecos.phi..LAMBD/sab A/., and it is defined as a tetradedral cosine .phi. or simply called a tecos.phi.. Moreover, in a simillar method, the dihedral angle of tetrahedron .phi..LAMBDA. is given as : value of sin .theta. can defind a tetra-sin.phi..LAMBDA., and value of tan .theta. of .phi..LAMBDA. is a tetra-tan .phi..LAMBDA. By induction it can derive that a tetrahedral geometry on the basis of suggesting a geometric tetrahedron

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1474-1477
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• 2005

This paper is proposed maximum torque per ampere of induction motor using fuzzy-neural networks controller. Operation of maximum torque per ampere is achieved when, at a given torque and speed, the slip frequency is adjusted to that so that the stator current amplitude is minimized. This paper introduces a induction motor drive system with fuzzy-neural networks controller. A neural network-based architecture is described for fuzzy logic control. The characteristic rule and their membership function of fuzzy system are represented as the processing nodes in the neural network structure. This paper is proposed the analysis as well as the simulation results to verify the effectiveness of the new method.