• 한국농공학회지
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• 제45권1호
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• pp.102-114
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• 2003

The objectives of the paper are to evaluate cell based pollutant loadings for different storm events, to monitor the hydrology and water quality of the Baran HP#6 watershed, and to validate AGNPS with the field data. Simplification was made to AGNPS in estimating storm erosivity factors from a triangular rainfall distribution. GIS-AGNPS interface model consists of three subsystems; the input data processor based on a geographic information system. the models. and the post processor Land use patten at the tested watershed was classified from the Landsat TM data using the artificial neural network model that adopts an error back propagation algorithm. AGNPS model parameters were obtained from the GIS databases, and additional parameters calibrated with field data. It was then tested with ungauged conditions. The simulated runoff was reasonably in good agreement as compared with the observed data. And simulated water quality parameters appear to be reasonably comparable to the field data.

• Smart Structures and Systems
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• 제3권2호
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• pp.201-217
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• 2007

An active damage detection technique is introduced to locate damage in an isotropic plate using Lamb waves. This technique uses a time-domain energy model of Lamb waves in plates that the wave amplitude inversely decays with the propagation distance along a ray direction. Accordingly the damage localization is formulated as a least-squares problem to minimize an error function between the model and the measured data. An active sensing system with integrated actuators/sensors is controlled to excite/receive $A_0$ mode of Lamb waves in the plate. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the undamaged plate from the recorded signal of the damaged plate. In the experimental study, after collecting the scattered wave signals, a discrete wavelet transform (DWT) is employed to extract the first scattered wave pack from the damage, then an iterative method is derived to solve the least-squares problem for locating the damage. Since this method does not rely on time-of-flight but wave energy measurement, it is more robust, reliable, and noise-tolerant. Both numerical and experimental examples are performed to verify the efficiency and accuracy of the method, and the results demonstrate that the estimated damage position stably converges to the targeted damage.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 한국통신학회논문지
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• 제29권12C호
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• pp.1677-1685
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• 2004

최근에 FD(frame Dropping)-CD(Coefficient Dropping) 트랜스코딩기법은 계산량을 줄이고, 손쉽게 구현할 수 있다는 측면에서 주목을 받고 있다. 그러나, 기존의 기법에서는 CD로 인하여 발생되는 왜곡 특성이 GOP내의 부호화 의존성을 갖는 프레임의 복호화 영상 품질에 전파 및 누적되는 성질을 고려하고 있지 않다. 본 논문에서는 CD로 인하여 발생되는 왜곡을 수식적으로 분석하고, CD왜곡이 전파/누적됨을 모의실험을 통하여 분석한다. 또한, 실험직인 결과에 기초하여, CD에 의한 전파/누적 왜곡 특성을 지수 감소함수 형태로 모델링한다. 모의실험을 통하여, 제안된 모델은 CD로 인해 초래되는 전체 누적왜곡 크기를 잘 예측할 수 있음을 보인다.

• Advances in nano research
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• 제7권6호
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• pp.431-442
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• 2019

Vibration analysis of carbon nanotubes (CNTs) is very essential field owing to their many promising applications in tiny instruments. In current study, the Eringen's nonlocal elasticity theory with clamped-clamped and clamped-free end conditions is utilized for the vibration analysis of armchair and zigzag SWCNTs. The Fourier method is utilized to solve the ordinary differential equation. The motion equation for this system is developed using a novel wave propagation method. Complex exponential functions have been used and the axial model depends on BCs that has been described at the edges of CNTs. The behavior of different nonlocal parameters is considered to find the vibrational frequency of SWCNTs. It is exhibited that the effect of frequencies against aspect ratio by varying the bending rigidity. It has been investigated that by increasing the nonlocal parameter decreases the frequencies and on increasing the aspect ratio increases the frequencies for both the tubes. To generate the fundamental natural frequencies of SWCNTs, computer software MATLAB engaged. The numerical results are validated with existing open text. Since the percentage of error is negligible, the model has been concluded as valid.

• 조명전기설비학회논문지
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• 제25권5호
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• 한국전산구조공학회논문집
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• 제17권1호
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• pp.75-82
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• 2004

본 논문에서는 응력파 전파를 수치모의할 때 발생하는 수치적인 분산효과를 제거하기 위해 파동방정식에 기초한 일차원 유한요소모형을 이용하여 수치분산오차의 특성을 분석하고 분산오차를 제어할 수 있는 방법을 제안하였다. 질량행렬을 그대로 사용하는 경우와 집중질량행렬을 사용하는 경우에 대한 수치분산오차를 분석하였다. 개발된 분산제어기법은 공간미분항의 시간단계 가중치 및 질량집중도를 조정하는 음해법과 인위적인 분산항을 추가하는 양해법의 두가지 방법이다. 제안된 분산보정기법을 이용하여 계산한 수치해와 파동방정식의 해석해를 비교한 결과 본 연구에서 제안한 분산보정기법의 타당성을 확인하였다.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 한국정밀공학회지
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• 제14권4호
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• pp.110-118
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• 1997

Multi-layer neural networks with error back-propagation algorithm have a great potential for identifying nonlinear systems with unknown characteristics. However, because they have a demerit that the speed of convergence is too slow, various methods for improving the training characteristics of backpropagition networks have been proposed. In this paper, a fuzzy division method is proposed to improve the convergence speed, which can find out an effective fuzzy division by the tuning of membership function and independently train each neural network after dividing the network model into several parts. In the simulations, the proposed method showed that the optimal fuzzy partitions could be found from the arbitray initial ones and that the convergence speed was faster than the traditional method without the fuzzy division.

• 한국수자원학회논문집
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• 제50권11호
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• pp.769-779
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• 2017

최근 우리나라에서 빈번하게 발생되는 가뭄으로 인하여 많은 피해가 발생하고 있으며, 이에 대한 사전대응의 필요성이 커지고 있다. 가뭄에 대한 효과적인 사전대응을 위해서는 신뢰성 있는 가뭄 예측 정보가 필수적이다. 본 연구에서는 수문학적 가뭄에 대한 확률론적 예측을 수행하기 위하여 가뭄의 전이현상을 베이지안 네트워크 모형에 반영하였다. 가뭄의 전이현상을 고려한 베이지안 네트워크 기반의 가뭄 예측 모형(PBNDF)은 과거, 현재, 미래에 대한 다중 모형 앙상블 예측결과와 가뭄전이 관계를 결합하여 새로운 수문학적 가뭄 예측 결과를 생산하도록 구축되었다. 본 연구에서 PBNDF 모형은 파머수문학적 가뭄지수를 활용하여 낙동강 유역의 10개 지점을 대상으로 가뭄을 확률적으로 예측하는데 적용되었다. PBNDF 모형의 ROC 분석 결과 ROC 점수가 0.5 이상의 유의한 결과를 나타내 실제 예측 모형으로 활용가능하다는 것을 확인할 수 있었다. 또한, 기존에 개발된 모형(지속성 예측, 베이지안 네트워크 예측 모형)과 평균제곱오차의 제곱근(RMSE), 기술 점수(SS)를 활용하여 비교를 수행하였으며, 그 결과 PBNDF 모형의 RMSE는 상대적으로 낮은 값을 가지며, SS는 약 0.1~0.15 정도 높은 것으로 나타나 예측성능이 향상되었다는 것을 확인할 수 있었다.