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In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements cons...
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In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements constituting the alloy. The typical approach to Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis uses calibration curves, suitably built based on appropriate reference standards. More recently, statistical methods relying on the principles of ANNs are increasingly used. In particular, an ANN can be used for a preliminary exploration of the LIBS spectra in order to find out the most significant areas of the spectrum, which will be used by another ANN dedicated to the calibration. In this paper we will show that the use of ANNs to deal with LIBS spectra provides a viable, fast and robust method for LIBS quantitative analysis. Actually, this approach requires a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and can automatically analyze a large number of samples.
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摘要 :
In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements cons...
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In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements constituting the alloy. The typical approach to Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis uses calibration curves, suitably built based on appropriate reference standards. More recently, statistical methods relying on the principles of ANNs are increasingly used. In particular, an ANN can be used for a preliminary exploration of the LIBS spectra in order to find out the most significant areas of the spectrum, which will be used by another ANN dedicated to the calibration. In this paper we will show that the use of ANNs to deal with LIBS spectra provides a viable, fast and robust method for LIBS quantitative analysis. Actually, this approach requires a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and can automatically analyze a large number of samples.
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Different types of warranty policy are produced in order to fulfill the demand of manufacturers and also the requirement of buyers. Recent developments in warranty cost model with integrated expert system have improved the flexibi...
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Different types of warranty policy are produced in order to fulfill the demand of manufacturers and also the requirement of buyers. Recent developments in warranty cost model with integrated expert system have improved the flexibility and effectiveness of conventional system while heightens the need for marketing. In this paper, feed forward backpropagation neural network with multi layer perceptron is used as an expert system tool to model the warranty cost and inspection interval during the warranty period. In the implementation stage, the values of Mean Square Error has achieved to the goal and from the accuracy analysis of experimental result, the proposed model was found to achieve considerable good performance.
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摘要 :
Different types of warranty policy are produced in order to fulfill the demand of manufacturers and also the requirement of buyers. Recent developments in warranty cost model with integrated expert system have improved the flexibi...
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Different types of warranty policy are produced in order to fulfill the demand of manufacturers and also the requirement of buyers. Recent developments in warranty cost model with integrated expert system have improved the flexibility and effectiveness of conventional system while heightens the need for marketing. In this paper, feed forward backpropagation neural network with multi layer perceptron is used as an expert system tool to model the warranty cost and inspection interval during the warranty period. In the implementation stage, the values of Mean Square Error has achieved to the goal and from the accuracy analysis of experimental result, the proposed model was found to achieve considerable good performance.
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The gas deviation factor (Z-factor) is an effective thermodynamic property required to address the deviation of the real gas behavior from that of an ideal gas. Empirical models and correlations to compute Z-factor based on the eq...
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The gas deviation factor (Z-factor) is an effective thermodynamic property required to address the deviation of the real gas behavior from that of an ideal gas. Empirical models and correlations to compute Z-factor based on the equation of states (EOS) are often implicit, because they needed huge number of iterations and thus computationally very expensive. Many explicit empirical correlations are also reported in the literature to improve the simplicity; yet, no individual explicit correlation has been formulated for the complete full range of pseudoreduced temperatures and pseudo-reduced pressures, which demonstrates a significant research gap. The inaccuracy in determining gas deviation factor will lead to huge error in computing subsequent natural gas properties such as gas formation volume factor (Bg), gas compressibility (cg), and original gas in place (OGIP). Previously reported empirical correlations provide better estimation of gas deviation factor at lower pressures but at higher reservoir pressures their accuracies becomes questionable. In this study, a simple and improved Z-factor empirical model is presented in a linear fashion using a robust artificial intelligence (AI) tool, the Artificial Neural Network (ANN). The new model is trained on more than 3000 data points from laboratory experiments obtained from several published sources. The proposed model is only a function of pseudo reduced temperature and pseudo reduced pressure of the gases which makes it simpler than the existing implicit and complicated correlations. The accuracy and generalization capabilities of the proposed ANN based model is also tested against previously published correlations at low and high gas reservoir pressures on an unseen published dataset. The comparative results on a published dataset show that the new model outperformed other methods of predicting Z-factor by giving less average absolute percentage error (AAPE), less root mean square error (RMSE) and high coefficient of determination (R2). The error obtained was less than 3% compared to the measured data.
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It is well known that nonlinear dynamical systems with high dimensional are relatively complex to study. In this paper, the dynamics of an n-neuron annular neural network with time-delay are investigated. Through analyzing the ass...
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It is well known that nonlinear dynamical systems with high dimensional are relatively complex to study. In this paper, the dynamics of an n-neuron annular neural network with time-delay are investigated. Through analyzing the associated characteristic equation, we obtained the local stability at the equilibrium, and then show the conditions when the proposed system lose its stability so that the Hopf bifurcation occurs. Finally, we give some numerical simulations to justify the obtained results of the proposed scheme.
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In today's world on one hand where the computer has revolutionized the field of education and on other hand there are still some areas where there is a need of use of computers. Traditional ways of checking have various kinds of f...
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In today's world on one hand where the computer has revolutionized the field of education and on other hand there are still some areas where there is a need of use of computers. Traditional ways of checking have various kinds of faults. There are several mistakes that occur on the checking department's side like totaling error, marking mistake and sometimes there can be partiality in evaluation of marks. For any student the evaluation of the exam plays an important role. The students are generally classified on the basics of their performances in the examination. Therefore the assessment of examination should be carried out in a most efficient way. Current evaluation process leaves the future of the student at the mercy of the teachers. The understudies likewise don't motivate chances to express their insight and potential. Rather they are made to take in the stuff they had already learnt in their individual reading material. These variables upset the imagination of the understudies all things considered. Likewise a lot of cash and time is squandered. The practice of current evaluation system is used widely across the world and student s across all the areas have been facing the drawbacks of this current evaluation system. The advance of separation training has likewise been hampered by the non-accessibility of an automated assessment framework. This paper tends to how these striking insufficiencies in the instructive framework can be evacuated.
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摘要 :
In today's world on one hand where the computer has revolutionized the field of education and on other hand there are still some areas where there is a need of use of computers. Traditional ways of checking have various kinds of f...
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In today's world on one hand where the computer has revolutionized the field of education and on other hand there are still some areas where there is a need of use of computers. Traditional ways of checking have various kinds of faults. There are several mistakes that occur on the checking department's side like totaling error, marking mistake and sometimes there can be partiality in evaluation of marks. For any student the evaluation of the exam plays an important role. The students are generally classified on the basics of their performances in the examination. Therefore the assessment of examination should be carried out in a most efficient way. Current evaluation process leaves the future of the student at the mercy of the teachers. The understudies likewise don't motivate chances to express their insight and potential. Rather they are made to take in the stuff they had already learnt in their individual reading material. These variables upset the imagination of the understudies all things considered. Likewise a lot of cash and time is squandered. The practice of current evaluation system is used widely across the world and student s across all the areas have been facing the drawbacks of this current evaluation system. The advance of separation training has likewise been hampered by the non-accessibility of an automated assessment framework. This paper tends to how these striking insufficiencies in the instructive framework can be evacuated.
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Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to impr...
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Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to improve metrology at nanoscale characterization by hybridization of several techniques. Namely, optical critical dimension (OCD) or scatterometry, CD-Scanning electron microscopy (CDSEM), CD-Atomic force microscopy (CDAFM) and CD-Small angle x-rays scattering (CDSAXS). With virtual data of tabular-type generated by modelling, the ANN is able to predict the geometrical parameters compared to true measured values with high accuracies and detect irregularities in input data.
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摘要 :
Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to impr...
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Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to improve metrology at nanoscale characterization by hybridization of several techniques. Namely, optical critical dimension (OCD) or scatterometry, CD-Scanning electron microscopy (CDSEM), CD-Atomic force microscopy (CDAFM) and CD-Small angle x-rays scattering (CDSAXS). With virtual data of tabular-type generated by modelling, the ANN is able to predict the geometrical parameters compared to true measured values with high accuracies and detect irregularities in input data.
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