Optimisation of the range of an optical fibre pH sensor using feed-forward artificial neural network

2003 ◽  
Vol 90 (1-3) ◽  
pp. 175-181 ◽  
Author(s):  
Faiz Bukhari Mohd Suah ◽  
Musa Ahmad ◽  
Mohd Nasir Taib
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Fibre ◽  
Ph Sensor ◽  
Feed Forward ◽  
Artificial Neural

scholarly journals An intelligent optical fibre pH sensor based on sol-gel advanced material and artificial neural network

2005 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Mohd Nasir Taib ◽  
Faiz Bukhari Mohd Suah ◽  
Musa Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Fibre ◽  
Dynamic Range ◽  
Sol Gel ◽  
Ph Sensor ◽  
Bromophenol Blue ◽  
Back Propagation Algorithm ◽  
Feed Forward Network ◽  
Artificial Neural

The application of artificial neural network (ANN) in signal processing of optical fibre pH sensor is presented. The pH sensor is developed based on the use of bromophenol blue indicator immobilized in a sol-gel thin film as a sensing material. A three layer feed-forward network was used and the network training was performed using the back-propagation algorithm. Spectra generated from the pH sensor at several selected wavelengths are used as the input for the ANN. The bromophenol blue indicator, which has a limited dynamic range of 3.00-5.50 pH units, was found to show higher pH dynamic range of 2.00-12.00 and low calibration error after training with ANN. The trained ANN was successfully employed to predict several spectra from unknown buffer solution with an average error of 0.06 pH units.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural

scholarly journals Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Film Thickness ◽  
Spur Gear ◽  
Back Propagation Algorithm ◽  
Elman Neural Network ◽  
Feed Forward ◽  
Oil Film ◽  
Feed Forward Network ◽  
Artificial Neural

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

Sign in / Sign up

Export Citation Format

Share Document