scholarly journals Prediction of longitudinal and transverse profiles of pressure flushing cones using artificial intelligence and data pre-processing

2021 ◽  
Author(s):  
Mehdi Daryaee ◽  
Farshad Ahmadi ◽  
Peyman Peykani ◽  
Mohammadreza Zayeri
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Critical Issues ◽  
Higher Weights ◽  
Important Input ◽  
Input Variables

Abstract One of the most critical issues in dam reservoir management is the determination of sediment level after flushing operation. Artificial intelligence (AI) methods have recently been considered in this context. The present study adopts four AI approaches, including the Feed-Forward Neural Network (FFNN), Cascade Feed-Forward Neural Network (CFFNN), Gene Expression Programming (GEP), and Bayesian Networks (BNs). Experimental data were exploited to train and test the models. The results revealed that the models were able to estimate the post-flushing sediment level accurately. FFNN outperformed the other models. Furthermore, the importance of model inputs was determined using the τ-Kendall (τ–k), Random Forest (RF), and Shannon Entropy (SE) pre-processing methods. The initial level of sediment was found to be the most important input, while the orifice output flow rate was observed to have the lowest importance in modeling. Finally, inputs of higher weights were introduced to the FFNN model (as the best predictive model), and the analysis of the results indicated that the exclusion of less important input variables would have no significant impact on model performance.

scholarly journals Analisis Pengenalan Bendera Negara Rumpun Melayu Menggunakan Metode Feed Forward Neural Network

2020 ◽  
Vol 10 (2) ◽  
pp. 144-152
Author(s):  
H Santoso ◽  
D Murdianto
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Learning Rate ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Brunei Darussalam ◽  
Hidden Layer

Telah dilakukan analisis pada sistem pengenalan gambar empat buah bendera negara rumpun melayu secara digital. Negara tersebut adalah Indonesia, Malaysia, Singapura, dan Brunei Darussalam. Tujuan dari penelitian ini adalah sebagai bentuk langkah awal dalam melatih sistem Artificial Intelligence (Kecerdasan Buatan) dalam membedakan empat buah negara rumpun melayu berdasarkan warna dan motif bendera pada sebuah peta digital. Proses analisis dan pelatihan pengenalan bendera tersebut menggunakan metode Feed Forward Neural Network (FFNN). Hasilnya menunjukkan bahwa penggunaan 4 buah Hidden Layer, serta penggunaan Learning Rate 0,5 memberikan kemampuan pengenalan citra bendera secara tepat dengan persentase akurasi rata-rata mencapai 74,15%.

A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network

2014 ◽  
Vol 31 (8) ◽  
pp. 1838-1849 ◽  
Author(s):  
J. Zeng ◽  
Y. Nojiri ◽  
P. Landschützer ◽  
M. Telszewski ◽  
S. Nakaoka
Keyword(s):  
Neural Network ◽  
Spatial Resolution ◽  
Nonlinear Function ◽  
Global Ocean ◽  
Co2 Uptake ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Surface Ocean ◽  
Data Points ◽  
Input Variables

Abstract A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO2 (fCO2) on a 1° × 1° spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO2 Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO2 increase rate of 1.50 μatm yr−1. The rate was used to normalize multiple years’ fCO2 observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO2 climatology. The model simulates monthly fCO2 distributions that agree well with observations and yields an anthropogenic CO2 update of −1.9 to −2.3 PgC yr−1. The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont–Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO2 uptake. The method’s benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO2 as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO2 in all seasons, and (iii) the model can be extended to produce global fCO2 maps at a higher resolution in time and space as long as the required data for input variables are available.

CLASSIFYING JET CASCADES WITH A MUON USING A NEURAL NETWORK

1992 ◽  
Vol 03 (supp01) ◽  
pp. 321-330
Author(s):  
Martin Los
Keyword(s):  
Neural Network ◽  
Bottom Quark ◽  
Feed Forward Neural Network ◽  
Final State ◽  
Feed Forward ◽  
Quark Decay ◽  
Input Variables

We constructed a feed-forward neural network to tag jets with a prompt muon from bottom quark decay. Using kinematic variables and the reconstructed jet charge as input variables, we studied the response of the network to backgrounds of other cascades having a muon in the final state. Finally, we analysed the behaviour of this classifier on a di-muon sample as part of a [Formula: see text] mixing analysis.

scholarly journals A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

2021 ◽  
Vol 118 ◽  
pp. 103766
Author(s):  
Ahmed J. Aljaaf ◽  
Thakir M. Mohsin ◽  
Dhiya Al-Jumeily ◽  
Mohamed Alloghani
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Feed Forward Neural Network ◽  
Feed Forward
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