Artificial Neural Network Drilling Parameter Optimization System Improves ROP by Predicting/Managing Bit Wear

2012 ◽  
Author(s):  
Yashodhan Keshav Gidh ◽  
Arifin Purwanto ◽  
Hani Ibrahim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parameter Optimization ◽  
Optimization System ◽  
Bit Wear ◽  
Drilling Parameter ◽  
Artificial Neural

scholarly journals Optimasi Waktu Akuisisi Data Satelit Noaa18 Menggunakan Jaringan Syaraf Tiruan Backpropagation

AVITEC ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Anis Maghfirotul Habibah ◽  
Ibrahim Nawawi ◽  
Ika Setyowati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Data ◽  
Elevation Angle ◽  
Earth Station ◽  
Optimization System ◽  
Time Optimization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Earth stations are built to monitor the presence of satellites starting from satellite data, monitoring satellites, and carry out orders and corrections if needed. On the earth station there is a satellite data receiving antenna, the more elevation angle of the current satellite data receiver antenna can affect the time duration of the satellite data. The purpose of this research is to apply the Artificial Neural Network (ANN) method to design a time optimization system for satellite data at the LAPAN Pekayon earth station, East Jakarta. The data used as input is the elevation angle. The benefit of this research is expected to make it easier for operators and technicians to measure the time optimization of satellite data at earth stations. The best training results with learning rate = 0.2, error = 0.0001, max. epoch = 100000, neuron hidden layer = 15. The MSE value obtained is 0.0001 reaching the goal at epoch 68810. Regret the training / training reverse sequence reaches 0.99878. The best test result is to use learning speed 0.2 hidden layer neurons 15 comparison of training data = 54 and test data = 18. The accurate result is exactly the same as the specified error, namely 0.0001. The difference in the average target duration is 3 seconds compared to the ANN target. Artificial Neural Network (ANN) with the back propagation method of training function gradient descent (traingd), was successfully used to an optimization system for satellite data acquisition time at earth stations.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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