• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권3호
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• 한국생산제조학회지
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• 제6권1호
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• 한국산업융합학회 논문집
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• 제5권2호
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• pp.103-110
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• 2002

An artificial neural network is applied to the prediction of compressive strength, slump value of concrete. Standard mixed tables arc trained and estimated, and the results are compared with those of experiments. To consider the varieties of material properties, the standard mixed tables of two companies of Ready Mixed Concrete are used. And they are trained with the neural network. In this paper, standard back propagation network is used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of 210, $240kgf/cm^2$ and target slump value of 12, 15cm are used because the amount of production of that range arc the most at ordinary companies. In results, in the prediction of compressive strength and slump value, the predicted values are converged well to those of standard mixed tables at the target error of 0.10, 0.05, 0.001 regardless of two companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5394-5409
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• 2019

The paper investigates a novel detector receiver with Kalman channel information estimator and iterative channel response equalization for MIMO (multi-input multi-output) OFDM (orthogonal frequency division multiplexing) communication systems in fast multipath fading environments. The performances of the existing linear equalizers (LE) are not good enough over most fast fading multipath channels. The existing adaptive equalizer with decision feedback structure (ADFE) can improve the performance of LE. But error-propagation effect seriously degrades the system performance of the ADFE, especially when operated in fast multipath fading environments. By considering the Kalman channel impulse response estimation for the fast fading multipath channels based on CE-BEM (complex exponential basis expansion) model, the paper proposes the iterative receiver with soft decision feedback equalization (SDFE) structure in the fast multipath fading environments. The proposed SDFE detector receiver combats the error-propagation effect for fast multipath fading channels and outperform the existing LE and ADFE. We demonstrate several simulations to confirm the ability of the proposed iterative receiver over the existing receivers.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

• 한국지반공학회지:지반
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• 제13권2호
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• pp.77-90
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• 1997

지하수 거동에 대한 불확실성을 극복하기 위해서 암반 지반의 투수계수를 예측할 수 있는 신뢰적이고 경제적인 방법이 필요하다. 이러한 목적을 위하여 암반의 투수계수 예측 방법에 대한 연구가 수행되어졌다. 인공 신경망 이론을 적용한 투수계수 예측 방법에 대한 일환으로 오차역 전파 학습알고리즘을 이용한 투수계수 예측 방법에 대하여 연구를 수행하였으며, 이 방법의 타당성 검토를 위하여 현장투수시험 결과와 지반물성치들에 적용하여 검증을 실시하였다. 검증결과 평균오차 범위가 작아 비교적 정착한 투수계수 예측방법임을 보여주었다.

• 한국통신학회논문지
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• 제13권2호
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• pp.120-126
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• 1988

본 논문은 부호분할 다중통신을 위한 비동기 FH/MFSK(Frequency Hopping-Multilevel Frequency Shift Keying) 반복전송 시스템의 성능에 관한 연구이다. 잡음(Noise)과 페이딩(Multipath propagation)이 없고 사용자 상호간의 간섭(Interference)만 존재한다고 가정하고 사용자수 M이 주어졌을 때 실제상황을 모델로 하여 시뮬레이션(Simulation)하여 구한 간섭량과 Random Coding때의 간섭량과 비교하여 거의 차이가 없음을 보였다. 또한 비동기 FH/MFSK 반복전송 시스템의 Bound 형태로 표현된 워드 에러(Word error)확률의 식으로 계산한 값과 실제상황을 모델로 해서 시뮬레이션하여 나온 결과가 잘 일치하고 있음을 보였다.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• 한국해양공학회지
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• 제32권3호
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• pp.166-176
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• 2018

This paper presents a numerical sensitivity analysis for the simulation of the motion performance of an offshore structure in waves using computational fluid dynamics (CFD). Starting with 2D wave simulations with varying numerical parameters such as grid spacing and CFL value, proper numerical conditions were found for accurate wave propagation that avoids numerical diffusion problems. These results were mapped on 2D error distributions of wave amplitude and wave length against the numbers of grids per wave length and per wave height under a given CFL condition. Finally, the 2D numerical sensitivity result was validated through CFD simulation of the motion of a FPSO in waves showing good accuracy in motion RAOs compared with existing potential flow solutions.

• 대한토목학회논문집
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• 제12권3호
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• pp.231-238
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• 1992

계산시간(計算時間)의 단축(短縮)을 위하여 EVP(Error Vector Propagation) 방법(方法)을 사용하여 타원형(楕圓形) 완경사방정식(緩傾斜方程式)을 해석(解析)하였다. 수치실험(數値實驗)은 수중(水中)에 타원형(楕圓形) 여울이 존재하는 완경사(緩傾斜) 해역(海域)에서 수행하였으며, 포물선형(抛物線形) 모형(模型) 및 쌍곡선형(雙曲線形) 모형(模型)을 같이 계산하여 각각의 결과(結果)를 수리실험(水理實驗) 결과(結果)와 비교(比較)하였다. 또한 이안제(離岸堤)가 설치된 파랑장(波浪場)의 경우에도 쌍곡선형(雙曲線形) 모형(模型)의 결과(結果) 및 수리실험(水理實驗) 결과(結果)와 비교(比較)하였다. 적용결과(適用結果) 계산시간(計算時間) 면에서는 다른 모형(模型)에 비하여 만족스럽게 단축(短縮)할 수 있었으며, 해(解)의 정확성(正確性)에서는 약간의 진동현상(振動現象)이 나타나지만 그 경향(傾向)은 잘 일치하였다.