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This paper presents a novel intelligent data mining technique to estimate the optical properties of touch panel (TP) with different layers coating. The neural network (NN) model is developed to be the intelligent tool for the data analyzer. The new computational method based on well-trained NN's weights is used to analyze the influencing factors of TP film's chromatic aberration, i.e. L.a.b. values...
This paper presents the transmittance estimations for touch panel (TP) film with Cr and Cr2O3 coating by using neural network (NN) model. The NN model with quasi-Newton learning method was used to obtain the mapping between TP transmittance and its all possible influencing factors. This study tries to develop an artificial intelligent (AI) evaporation decision mechanism which can help the technician...
This paper presents the estimation of the interference of NH3 on the detection of NO by using neural network (NN) model. The NO Rayleigh surface acoustic wave (RSAW) sensor coated with polyaniline/WO3 (PANI/WO3) nanocomposite was employed as the detection sensor. The data sensed by RSAW sensor was collected and implemented and the detection property of the RSAW sensor was studied at room temperature...
In this paper, a transmittance estimator of touch panel decoration film by using neural network is presented. In the evaporation process, the coating material and the related control parameters are all important influencing factors in obtaining the desired transmittance. The relationship among the transmittance and these factors are very complex and nonlinear. It's very hard to use the certain mathematical...
In this paper, the predictions of optoelectronic attributes of Light-Emitting Diode (LED) chip, including luminous intensity, wavelength and forward voltage by using neural network were presented. The simulated data was measured by Electrical Luminescence (EL) technique. The well-trained neural models were used to predict the optoelectronic attributes of LED chip in its epitaxy growth stage in advance...
This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). In this data set, the load information is the sum of power load consumed by three areas, including Allentown, Baltimore and Philadelphia. The historical load and temperature information from year 2003 to year 2008...
AbstractBased on combining neural network (NN) with fuzzy logical system (FLS), a new family of three-layer feedforward networks, called soft-competition basis function neural networks (SCBFs), is proposed under the framework of the counter-propagation (CP) network. The hidden layer of SCBFs is designed as competitive layer with soft competitive strategy. The output function of their hidden neuron...
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