• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.471-475
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• 1997

PWM-VSI based ac-drives have high nonlinearity due to dead-time in the inverter and the voltage drop across the switching devices. In this paper, we introduced a new nonlinear model of PWM-VSl including parastic capacitor and also showed validity of the model by circuit simulations and experiments. Furthermore, we proposed an on-line identification algorithm for the uncertain model parameters.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.66-68
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• 1997

In this paper, our interest is the identification and control of nonlinear dynamic plant, induction motor, by using neural networks. We usually use vector control in the induction motor such as in the DC motor. When we go over the inputs of voltage source invertor, we can find that torque current and flux current couple each other in the induction motor. Before putting control inputs in the system, we should remove the coupling terms which we already know from them. But we should consider that cross coupling terms have time-varying variables. In this paper, we identified the parameter of induction motor by using neural networks and designed the controller with identified parameters. Through this procedure we obtained compensated inputs which are decoupled each other. Using induction motor currents control, we can make the d axis current hold constant value and control the q axis current at the same time.

• 전기학회논문지
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• 제64권7호
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• pp.1040-1046
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• 2015

This paper introduces GP(Genetic Programming) based color detection model for an object detection of humanoid robot vision. Existing color detection methods have used linear/nonlinear transformation of RGB color-model. However, most of cases have difficulties to classify colors satisfactory because of interference of among color channels and susceptibility for illumination variation. Especially, they are outstanding in degraded images from robot vision. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various environments in robot vision for real humanoid Nao.

• Structural Engineering and Mechanics
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• 제64권3호
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• pp.353-360
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• 2017

This study presents the Big Bang and Big Crunch (BB-BC) optimization algorithm for detection of structure damage in large severity. Local damage is represented by a perturbation in the elemental stiffness parameter of the structural finite element model. A nonlinear objective function is established by minimizing the discrepancies between the measured and calculated acceleration responses (AR) of the structure. The BB-BC algorithm is utilized to solve the objective function, which can localize the damage position and obtain the severity of the damage efficiently. Numerical simulations have been conducted to identify both single and multiple structural damages for beam, plate and European Space Agency Structures. The present approach gives accurate identification results with artificial measurement noise.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.371-376
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• 1999

Stick-slip friction is present to some degree in almost all actuators and mechanisms and is often responsible for performance limitations. Simulation of stick-slip friction is difficult because of strongly nonlinear behavior in the vicinity of zero velocity. A straightforward method for representing and simulating friction effects is presented. True zero velocity sticking is represented without equation reformulation or the introduction of numerical stiffness problems. Stick-slip motion is investigated experimentally, and the fundamental characteristics of the stick-slip motion are clarified. Based on these experimental results, the characteristics of static in the period of stick and kinetic friction in the period of slip are studied concretely so as to clarify the stick-slip process.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.695-700
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• 2008

Artificial neural network (ANN) has been extensively used in areas of nonlinear system modeling, analysis and design applications. Basically, ANN has its distinct capabilities of implementing system identification and/or function approximation using a number of input/output patterns that can be obtained via numerical and/or experimental manners. The paper describes a role of ANN, especially a back-propagation neural network (BPN) in the context of engineering analysis, design and optimization. Fundamental mechanism of BPN is briefly summarized in terms of training procedure and function approximation. The BPN based causality analysis (CA) is further discussed to realize the problem decomposition in the context of multidisciplinary design optimization. Such CA is also applied to quantitatively evaluate the uncoupled or decoupled design matrix in the context of axiomatic design with the independence axiom.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.926-931
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• 2008

In order to design a high-performance controller with excellent positioning and tracking performance, an optimal tuning method based on the integrated design concept is studied. DOBs, feedforward controllers and CCC are applied to control the bi-axial linear servomechanism. To derive accurate dynamic models of mechanical subsystems equipped with linear servos for the integrated tuning, system identification processes are conducted through the sine sweeping. An optimal tuning problem with stability, robustness and overshoot constraints is formulated as a nonlinear constrained optimization problem. Optimal gains are obtained through the SQP method. Experimental results confirm that both tracking and contouring errors are significantly reduced by applying the proposed controller and integrated tuning method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.329-332
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• 2003

본 논문에서는 새로이 제안된 자기 구조화하는(Self-structuring) 새로운 Radial-Basis Function Network(RBFN)에 대해서 실험적인 검증을 했다. 이 자기 구조화하는 새로운 RBFN은 기존의 RBFN과 비교해서 여러 장점이 있다. Lyapunov 이론에 기초해서 새로운 학습 규칙을 선정하였기 때문에 시스템의 안정도를 보장할 수 있다. 그리고, 자기 구조화의 과정 즉, 생성과 병합을 통해 은닉층에서 적정수의 뉴런을 결정할 수 있다. 기존의 RBFN과 성능을 비교하기 위하여, 실제 비선형 시스템인 2축 암로봇에 대해 실험한 결과를 보였다. 결과적으로, 우리는 실험결과를 통해 자기 구조화하는 RBFN의 효율적인 구조와 시스템에 대한 안정도를 보장함을 볼 수 있다.

• 한국소음진동공학회논문집
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• 제24권12호
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• pp.943-947
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• 2014

Recently, a new method for reconstructing a forced nonlinear dynamic system has been proposed; specifically, the simultaneous reconstruction of its excitation as well as restoring characteristics of the system. The reconstruction was just theoretically shown to be possible by measuring the system's responses, based on newly introduced notions, a J-function and a zero-crossing time. However, numerically in the current paper, we are to reconstruct a linear system, i.e., we focus on numerical experiments to reconstruct both the excitation and the linear restoring characteristic of a linear forced oscillating system by using response data, based on the J-function and the zero-crossing time.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.463-466
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• 2002

The Field Robot means the machinery applied for outdoor tasks in construction, agriculture and undersea etc. In this paper, to field-robotize a hydraulic excavator, we have proposed a robust and systematic controller design method. Disturbance observer is used as inner controller to reshape the excavating system into the linear dynamics of nominal model by compensating coupled nonlinear terms, model uncertainties and external load variations. Using the linear model that is obtained through off-line system identification, a H control scheme is applied to construct a disturbance observer and a servo-controller systematically.