• Title/Summary/Keyword: Neural Network Modeling

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I-V Modeling Based on Artificial Neural Network in Anti-Reflective Coated Solar Cells (반사방지막 태양전지의 I-V특성에 대한 인공신경망 모델링)

  • Hong, DaIn;Lee, Jonghwan
    • Journal of the Semiconductor & Display Technology
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    • v.21 no.3
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    • pp.130-134
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    • 2022
  • An anti-reflective coating is used to improve the performance of the solar cell. The anti-reflective coating changes the value of the short-circuit current about the thickness. However, the current-voltage characteristics about the anti-reflective coating are difficult to calculate without simulation tool. In this paper, a modeling technique to determine the short-circuit current value and the current-voltage characteristics in accordance with the thickness is proposed. In addition, artificial neural network is used to predict the short-circuit current with the dependence of temperature and thickness. Simulation results incorporating the artificial neural network model are obtained using MATLAB/Simulink and show the current-voltage characteristic according to the thickness of the anti-reflective coating.

Fuzzy Division Method to Minimize the Modeling Error in Neural Network (뉴럴 네트웍 모델링에서 에러를 최소화하기 위한 퍼지분할법)

  • Chung, Byeong-Mook
    • Journal of the Korean Society for Precision Engineering
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    • v.14 no.4
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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.

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Modeling Differential Global Positioning System Pseudorange Correction

  • Mohasseb, M.;El-Rabbany, A.;El-Alim, O. Abd;Rashad, R.
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.1
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    • pp.21-26
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    • 2006
  • This paper focuses on modeling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

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Camera Calibration Using Neural Network with a Small Amount of Data (소수 데이터의 신경망 학습에 의한 카메라 보정)

  • Do, Yongtae
    • Journal of Sensor Science and Technology
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    • v.28 no.3
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    • pp.182-186
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    • 2019
  • When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

A Study on the ORP Modeling in SBR Process for Nitrogen Removal: Polynomial Neural Network Is Employed (질소제거를 위한 SBR 공정운전에서 ORP 모델링에 관한 연구: 다항식 뉴럴네트워크 기법 중심)

  • 김동원;박영환;박귀태
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.4
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    • pp.221-225
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    • 2004
  • This paper shows the application of artificial intelligence technique such as polynomial neural network in modeling and identification of sequencing batch reactor (SBR). A wastewater treatment process for nitrogen removal in the SBR is presented. Simulation results have shown that the nonlinear process can be modeled reasonably well by the Present scheme which is simple but efficient.

동적 비선형 신호의 온라인 모델링

  • 한정희;왕지남
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1994.10a
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    • pp.371-376
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    • 1994
  • This paper presents an on-line modeling method approach for the machine condition. the machine condition is continuously monitored with a sensor such as, a vibration, a current, an acoustic emission (AE) sensor. In this study, neural network modeling by radial basis function is designed for analysis a prediction error. An on-line learning algorithm is designed using the RLS(recursive least square) estimation and the existing clustering method of Kohonen neural network. Experimental results show that the proposed RBNN modeling is suitable for predicting simulated data.

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Predictive Modeling of River Water Quality Factors Using Artificial Neural Network Technique - Focusing on BOD and DO- (인공신경망기법을 이용한 하천수질인자의 예측모델링 - BOD와 DO를 중심으로-)

  • 조현경
    • Journal of Environmental Science International
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    • v.9 no.6
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    • pp.455-462
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    • 2000
  • This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

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Optimal Learning of Neo-Fuzzy Structure Using Bacteria Foraging Optimization

  • Kim, Dong-Hwa
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1716-1722
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    • 2005
  • Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

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Neural Network-Based Modeling for Fuel Consumption Prediction of Vehicle (차량 연료 소모량 예측을 위한 신경회로망 기반 모델링)

  • Lee, Min-Goo;Jung, Kyung-Kwon;Yi, Sang-Hoi
    • 전자공학회논문지 IE
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    • v.48 no.2
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    • pp.19-25
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    • 2011
  • This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.

Soft computing with neural networks for engineering applications: Fundamental issues and adaptive approaches

  • Ghaboussi, Jamshid;Wu, Xiping
    • Structural Engineering and Mechanics
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    • v.6 no.8
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    • pp.955-969
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    • 1998
  • Engineering problems are inherently imprecision tolerant. Biologically inspired soft computing methods are emerging as ideal tools for constructing intelligent engineering systems which employ approximate reasoning and exhibit imprecision tolerance. They also offer built-in mechanisms for dealing with uncertainty. The fundamental issues associated with engineering applications of the emerging soft computing methods are discussed, with emphasis on neural networks. A formalism for neural network representation is presented and recent developments on adaptive modeling of neural networks, specifically nested adaptive neural networks for constitutive modeling are discussed.