Dynamic Neural Networks with Semi Empirical Model for Mobile Radio Path Loss Estimation

2019 ◽  
pp. 119-129
Author(s):  
Bhuvaneshwari Achayalingam ◽  
Hemalatha Rallapalli ◽  
Satya Savithri Tirumala
Keyword(s):  
Neural Networks ◽  
Empirical Model ◽  
Path Loss ◽  
Mobile Radio ◽  
Loss Estimation ◽  
Dynamic Neural Networks ◽  
Radio Path ◽  
Semi Empirical

scholarly journals Performance Assessment of an NH3/LiNO3 Bubble Plate Absorber Applying a Semi-Empirical Model and Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4313
Author(s):  
Carlos Amaris ◽  
Maria E. Alvarez ◽  
Manel Vallès ◽  
Mahmoud Bourouis
Keyword(s):  
Neural Networks ◽  
Mass Transfer ◽  
Artificial Neural Networks ◽  
Heat And Mass Transfer ◽  
Empirical Model ◽  
Mass Flux ◽  
Ann Model ◽  
Artificial Neural ◽  
Semi Empirical ◽  
Plate Absorber

In this study, ammonia vapor absorption with NH3/LiNO3 was assessed using correlations derived from a semi-empirical model, and artificial neural networks (ANNs). The absorption process was studied in an H-type corrugated plate absorber working in bubble mode under the conditions of an absorption chiller machine driven by low-temperature heat sources. The semi-empirical model is based on discretized heat and mass balances, and heat and mass transfer correlations, proposed and developed from experimental data. The ANN model consists of five trained artificial neurons, six inputs (inlet flows and temperatures, solution pressure, and concentration), and three outputs (absorption mass flux, and solution heat and mass transfer coefficients). The semi-empirical model allows estimation of temperatures and concentration along the absorber, in addition to overall heat and mass transfer. Furthermore, the ANN design estimates overall heat and mass transfer without the need for internal details of the absorption phenomenon and thermophysical properties. Results show that the semi-empirical model predicts the absorption mass flux and heat flow with maximum errors of 15.8% and 12.5%, respectively. Maximum errors of the ANN model are 10.8% and 11.3% for the mass flux and thermal load, respectively.

On the Prediction of Nitrogen Oxides From Gas Turbine Combustion Chambers Using Neural Networks

2008 ◽  
Author(s):  
Marc LaViolette ◽  
Michael Strawson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Gas Turbine ◽  
Empirical Model ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Combustion Chambers ◽  
Nitrogen Emissions ◽  
Gas Turbine Combustion ◽  
Semi Empirical

This paper describes a method of predicting the oxides of nitrogen emissions from gas turbine combustion chambers using neural networks. A short review of existing empirical models is undertaken and the reasoning behind the choice of correlation variables and mathematical formulations is presented. This review showed that the mathematical functions obtained from the underlying theory used to develop the semi-empirical model ultimately limit their general applicability. Under these conditions, obtaining a semi-empirical model with a large domain and good accuracy is difficult. An overview of the use of neural networks as a modelling tool is given. Using over 2000 data points, a neural network that can predict NOx emissions with greater accuracy than published correlations was developed. The coefficients of determination of the prediction for the previous published semi-empirical models are 0.8048 and 0.7885. However one tends to grossly overpredict and the other underpredict. The coefficient of determination is 0.8697 for the model using a neural network. Because of the nature of neural networks, this more accurate model does not allow better insight into the physical and chemical phenomena. It is however, a useful tool for the initial design of combustion chambers.

Variability of mobile radio path loss in Auckland at 465 MHz

1983 ◽  
Vol 19 (15) ◽  
pp. 588 ◽  
Author(s):  
G.B. Rowe ◽  
A.G. Williamson ◽  
B. Egan
Keyword(s):  
Path Loss ◽  
Mobile Radio ◽  
Radio Path

scholarly journals A Semi-Empirical Model for Predicting Frost Properties

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 412
Author(s):  
Shao-Ming Li ◽  
Kai-Shing Yang ◽  
Chi-Chuan Wang
Keyword(s):  
Thermal Conductivity ◽  
Linear Programming ◽  
Numerical Model ◽  
Empirical Model ◽  
Quantitative Method ◽  
Predictive Ability ◽  
Dimensionless Temperature ◽  
Crystal Shape ◽  
Proposed Model ◽  
Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

Wind-driven rain on buildings: Accuracy of the ISO semi-empirical model

2021 ◽  
Vol 212 ◽  
pp. 104606
Author(s):  
Firouzeh Souri ◽  
Hua Ge ◽  
Ted Stathopoulos
Keyword(s):  
Empirical Model ◽  
Semi Empirical
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