scholarly journals Selection of the Best Optimal Operational Parameters to Reduce the Fuel Consumption Based on the Clustering Method of Artificial Neural Networks

2019 ◽  
Author(s):  
Tien Anh Tran
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Operational Parameters ◽  
Clustering Method ◽  
Artificial Neural ◽  
Selection Of

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

Artificial neural networks in the selection of shoe lasts for people with mild diabetes

2019 ◽  
Vol 64 ◽  
pp. 37-45 ◽  
Author(s):  
Chung-Chuan Wang ◽  
Ching-Hu Yang ◽  
Chung-Shing Wang ◽  
Dandan Xu ◽  
Bo-Shin Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mild Diabetes ◽  
Artificial Neural ◽  
Selection Of

scholarly journals ESTIMATION OF FUEL CONSUMPTION IN AGRICULTURAL MECHANIZED OPERATIONS USING ARTIFICIAL NEURAL NETWORKS

2017 ◽  
Vol 37 (1) ◽  
pp. 136-147 ◽  
Author(s):  
Pedro H. M. Borges ◽  
Zaíra M. S. H. Mendoza ◽  
João C. S. Maia ◽  
Aloísio Bianchini ◽  
Haroldo C. Fernándes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Artificial Neural

scholarly journals Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 1721-1743 ◽  
Author(s):  
Birsel Ayrulu-Erdem ◽  
Billur Barshan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Motion Classification ◽  
Artificial Neural ◽  
Leg Motion ◽  
Function Selection ◽  
Selection Of

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.

Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Ahmed K. Abbas ◽  
Salih Rushdi ◽  
Mortadha Alsaba ◽  
Mohammed F. Al Dushaishi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Complex Problem ◽  
Real Field ◽  
Operational Parameters ◽  
Rate Of Penetration ◽  
Weight On Bit ◽  
Artificial Neural ◽  
Deviated Wells

Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and minimizing expensive drilling costs. However, due to the geological uncertainty and many uncontrolled operational parameters influencing the ROP, its prediction is still a complex problem for the oil and gas industries. In the present study, a reliable computational approach for the prediction of ROP is proposed. First, fscaret package in a R environment was implemented to find out the importance and ranking of the inputs’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs), was developed. In order to gain a deeper understanding of the relationships between input parameters and ROP, this model was used to check the effect of the weight on bit (WOB), rotation per minute (rpm), and flow rate (FR). Finally, the simulation results of three deviated wells showed an acceptable representation of the physical process, with reasonable predicted ROP values. The main contribution of this research as compared to previous studies is that it investigates the influence of well trajectory (azimuth and inclination) and mechanical earth modeling parameters on the ROP for high-angled wells. The major advantage of the present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the deviated wells, and eventually reducing the drilling cost for future wells.

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