• 제어로봇시스템학회논문지
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• 제9권1호
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• pp.22-29
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• 2003

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is an inverse hysteresis model, base on neural network and the feedback control is implemented with PID control. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance.

• 한국통신학회논문지
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• 제15권4호
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• pp.295-300
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• 1990

역 운동학 문제는 로보트 매니퓰레이터 제어에서 중요한 관점이 되어 왔다. 본 논문에서는 Jacobi 제어 기법을 실현하기 위하여 Hopfield, Tank의 신경회로망 모델을 사용하였다. 뉴런의 상태는 매니퓰레이터의 관절 속도를 나타내고, 연결강도는 Jacobi 행렬의 값으로 결정되어 진다. 회로망의 에너지 함수는 실제 관절 속도와 원하는 관절 속도간의 최소 자승 오차와 대응하도록 구성한다. 매 샘플링에서 연결 강도와 뉴런의 상태는 현재의 관절위치값에 따라서 변한다. 여유 자유도를 가지는 평면 매니퓰레이터에 대한 역 운동학 해를 컴퓨터 시뮬레이션을 통하여 구하였다.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.862-868
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• 2007

일반적으로 인간손에 있는 검지 손가락의 평면운동은 3개의 관절운동에 의해 이루어진다. 이러한 운동을 위해서는 기본적으로 역기구학 문제를 풀어야 하는데, 이것은 로봇 손을 이용한 파지나 조작행위에 있어서 필수적이다. 따라서 본 논문에서는 이러한 로봇 손가락의 역기구학 문제를 지능적으로 해결할 수 있는 뉴럴 러닝에 기반한 방법을 제안하고자 한다. 제안된 방법은 뉴럴 러닝에 있어서 동적인 학습율을 적용함으로써 보다 빠른 학습이 가능하고, 생체모방에 근거한 인간 손가락의 운동특성을 고려하는 것이 특징이다. 제안된 방법의 유용성을 입증하기 위하여 시뮬레이션을 수행한다.

• 대한토목학회논문집
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• 제39권1호
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• pp.147-154
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• 2019

유한요소모델 업데이팅은 계측에 의한 구조물의 실제 응답과 가장 가까운 응답을 내는 유한요소모델의 매개변수를 찾는 문제로 정의할 수 있다. 기존 연구에서는 실 구조물과 해석 모델의 응답의 오차를 최소화하는 최적화에 기반 한 방법이 개발되었다. 이 연구에서는 목표 모드 정보로부터 유한요소 모델의 매개변수를 직접 얻을 수 있는 역 고유치 문제를 구성하고 역 고유치 문제를 빠르고 정확하게 풀기 위한 깊은 신경망(Deep Neural Network)을 구성하는 방법을 제안한다. 개발한 방법의 적용 예로서 현수교의 역 고유치 함수를 모사하는 신경망을 이용한 동적 유한요소모델 업데이트를 보인다. 해석 결과 제시한 방법은 매우 높은 정확도로 목표 모드에 대응하는 매개변수를 찾아낼 수 있음을 보였다.

• Journal of Welding and Joining
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• 제21권4호
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• pp.31-38
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• 2003

Line heating is an effective and economical process for forming flat metal plates into three-dimensional shapes for plating of ships. Because the nature of the line heating process is a transient thermal process, followed by a thermo elastic plastic stress field, predicting deformed shapes of plate is very difficult and complex problem. In this paper, neural network model o3r solving the inverse problem of metal forming is proposed. The backpropagation neural network systems for determining line-heating positions from object shape of plate are reported in this paper. Two cases of the network are constructed-the first case has 18 lines which have different positions and directions and the second case has 10 parallel heating lines. The input data are vertical displacements of plate and the output data are selected heating lines. The train sets of neural network are obtained by using an analytical solution that predicts plate deformations in line heating process. This method shows the feasibility that the neural network can be used to determine the heating-line positions in line heating process.

• 전기전자학회논문지
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• 제24권4호
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• pp.982-990
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• 2020

Groundwater is essential source of the freshwater. Groundwater is stored in the body of the rocks or sediments, called aquifer. Finding an aquifer is a very important part of the geophysical survey. The best method to find the aquifer is to make a borehole. Single borehole is not a suitable method if the aquifer is not located in the borehole drilled area. To overcome this problem, a cross borehole method is used. Using a cross borehole method, we can estimate aquifer location more precisely. Electrical impedance tomography is use to estimate the aquifer location inside the subsurface using the cross borehole method. Electrodes are placed inside each boreholes and area between these boreholes are analysed. An aquifer is a non-uniform structure with complex shape which can represented by the truncated Fourier series. Deep neural network is evaluated as an inverse problem solver for estimating the aquifer boundary coefficients.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2007년도 춘계학술발표논문집
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.147-150
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• 1996

This paper describes controller design and implementation method for controlling the temperature distribution in a thermal stratified wind tunnel(TSWT) by using a neural network algorithm. It is impossible to derive a mathematical model of the relation between heat inputs and temperature outputs in the test section of the TSWT governed by a nonlinear turbulent flow. Thus inverse neural network models with a multi layer perceptron structure are used in a feedforward control loop and feedback control loop to generate an arbitrary temperature distribution in the test section of the TSWT.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2008년도 추계학술발표논문집
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.211-218
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• 1999

The traditional robot manipulators are actuated by continuous range of motion actuators such as motors or hydraulic cylinders. However, there are many applications of mechanisms and robotic manipulators where only a finite number of locations need to be reached, and the robot’s trajectory is not important as long as it is bounded. Binary manipulator uses actuators which have only two stable states. As a result, binary manipulators have a finite number of states. The number of states of a binary manipulator grows exponentially with the number of actuators. This kind of robot manipulator has some advantage compared to a traditional one. Feedback control is not required, task repeatability can be very high, and finite state actuators are generally inexpensive. And this kind of robot manipulator has a fault tolerant mechanism because of kinematic redundancy. In this paper, we solve the inverse kinematic problem of a binary parallel robot manipulator using neural network and test the validity of this structure using some arbitrary points m the workspace of the robot manipulator. As a result, we can show that the neural network can find the nearest feasible points and corresponding binary states of the joints of the robot manipulator