Multi‐band MR fingerprinting (MRF) ASL imaging using artificial‐neural‐network trained with high‐fidelity experimental data

2020 ◽  
Vol 85 (4) ◽  
pp. 1974-1985
Author(s):  
Hongli Fan ◽  
Pan Su ◽  
Judy Huang ◽  
Peiying Liu ◽  
Hanzhang Lu
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
High Fidelity ◽  
Artificial Neural ◽  
Multi Band

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

scholarly journals Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel

2020 ◽  
pp. 238-238
Author(s):  
Adel Bouali ◽  
Salah Hanini ◽  
Brahim Mohammedi ◽  
Mouloud Boumahdi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Flow Boiling ◽  
Low Flow ◽  
Inclined Channel ◽  
Artificial Neural

The flow and heat transfer characteristics in a nuclear power plant in the event of a serious accident are simulated by boiling water in an inclined rectangular channel. In this study an artificial neural network model was developed with the aim of predicting heat transfer coefficient (HTC) for flow boiling of water in inclined channel, the network was designed and trained by means of 520 experimental data points that were selected from within the literature. orientation ,mass flux, quality and heat flow which were employed to serve as variables of input of multiple layer perceptron (MLP) neural network, whereas the analogous HTC was selected to be its output. Via the method of trial-and-error, MLP network with 30 neurons in the hidden layer was attained as optimal ANN structure. The fact that is was enabled to predict accurately the HTC. For the training set, the mean relative absolute error (MRAE) is about 0.68 % and the correlation coefficient (R) is about 0.9997. As for the testing and validation set they are respectively about 0.60 % and 0.9998 and about 0.79 % and 0.9996. The comparison of the developed ANN model with experimental data and empirical correlations in vertical channel under the low flow rate and low quality shows a good agreement.

Wind turbine power modelling and optimization using artificial neural network with wind field experimental data

2020 ◽  
Vol 280 ◽  
pp. 115880 ◽  
Author(s):  
Haiying Sun ◽  
Changyu Qiu ◽  
Lin Lu ◽  
Xiaoxia Gao ◽  
Jian Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Wind Turbine ◽  
Wind Field ◽  
Artificial Neural ◽  
Modelling And Optimization

A Prediction Model of Deflection of Fixing Thin Parts

2012 ◽  
Vol 217-219 ◽  
pp. 1526-1529
Author(s):  
Yu Mei Liu ◽  
Wen Ping Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Simulated Data ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

Modelling the Natural Evaporation of the Concentrated Seawater after Desalinized

2015 ◽  
Vol 713-715 ◽  
pp. 2989-2992
Author(s):  
Xue Kui Wang ◽  
Ying Zhou ◽  
Ling Li ◽  
Tian Cheng Gao ◽  
Na Tang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Relative Humidity ◽  
Evaporation Rate ◽  
Evaporation Model ◽  
Irradiation Intensity ◽  
Artificial Neural ◽  
Good Agreement

The influence of natural evaporation factors (the irradiation intensity, speed of the wind, temperature of the brine, temperature and relative humidity of the air) on the desalinated seawater evaporation rate was measured experimentally. A natural evaporation model was built by correlating the experimental data using the artificial neural network. This model was well correlated with the influence of natural evaporation factors, and it showed a good agreement of the results and evaporation theory.

Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data

2018 ◽  
Vol 233 (7) ◽  
pp. 2549-2569
Author(s):  
Farrukh Mazhar ◽  
Mohammad A Choudhry ◽  
Muhammad Shehryar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flight Test ◽  
Aerial Vehicle ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Trained Neural Network

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40 %) nanofluid using experimental data

2016 ◽  
Vol 126 (2) ◽  
pp. 837-843 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Mohammad Reza Hassani Ahangar ◽  
Davood Toghraie ◽  
Mohammad Hadi Hajmohammad ◽  
Hadi Rostamian ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Artificial Neural
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