Print ISSN: 1811-9212

Online ISSN: 2617-3352

Keywords : Neural Network


Thermocouples Data Linearization using Neural Network †

Karam M. Z. Othman

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2015, Volume 15, Issue 2, Pages 18-23

Abstract – Thermocouples are usually used for measuring temperatures in steel industry, gas turbine, diesel engine and many industrial processes. Thermocouple usually have nonlinear Temperature-Voltage relationship (mV=f(T˚)). However, on the monitoring side, it is required to have the inverse relationship [T˚=f-1(mV)] to determined the actual temperature sensed by the thermocouple. In this work the neural network is fully utilized to represent the required inverse nonlinear relationship of different and most popular thermocouples (K, J, B) Types. Levenberg Marquardt is used as learning process to find these neural networks. It is found that each type of thermocouples under test can be represented by a single neural network structure. Moreover, the obtained results show the power of neural network in representing the inverse static relationship of each thermocouple that gives less than 1% of the actual measured temperature in the whole temperature range in comparison to polynomial fitting method.

Intelligent Inertial Navigation System and Global Positioning System Navigator Based on Artificial Neural Network

Mr. AHMED MUDHER HASSAN

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2008, Volume 8, Issue 1, Pages 67-78

Abstract:
The integration of global positioning system (GPS) and Inertial Navigation System (INS) are continuously gaining interests in many positioning and navigation applications. Both systems have their unique features and shortcomings. Their integration offers systems that overcome each of their drawbacks and maximize each of their benefits. An INS/GPS integration method based on Artificial Neural Networks (ANNs) to fuse INS measurements and GPS measurements has been suggested. It is also provide high performance INS/GPS integration with accurate prediction for position
And velocity components during GPS signal absence. Thus the integration of the two systems presents a number of advantages and overcomes each systems inadequacy. An ANN was adopted in this paper using position and velocity update architectures and utilizing the window based weight updating strategy to updates the navigation knowledge in the strategy using two data test IMU systems.

INFECTED REGION RECOGNITION IN HUMAN BODY MEMBERS BASED ON WAVENET WITH MINIMUM DISTANCE

Hassan J. Hassan

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2007, Volume 7, Issue 2, Pages 95-107

Abstract:
Image identification plays a great role in industrial, remote sensing, medical and military applications. It is concerned with the generation of a signature to the image.
This work proposes a dynamic program (use Neural Network) to classify the texture of human member image then identify whether the member is infected or not. The program has the ability of determining which part of that member is infected depending on the comparison between the healthy member image stored in advance with a test image.
The first step is to make approximation to the image using wavelet network (Wavenet) technique. Through this technique we shall get an approximated image with reduced data. In addition, we shall get implicit information to that image. The second step is to subdivide the resultant image from the first step into 16 equally subparts then deal with each subpart as a unique image.
Finally, in the third step, the minimum distance (Mahalanobias Distance) approach is employed for subpart identification. All programs are written using MATLAB VER. 6.5 package.

COLOR IMAGE IDENTIFICATION BASED ON 2-D POWER SPECTRUM BASED ON NEURAL NETWORK

M.Sc. Hassan J. Hassan

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2007, Volume 7, Issue 1, Pages 74-86

Abstract:
Image identification plays a great role in industrial, remote sensing, and military
applications. It is concerned with the generation of a signature to the image.
This work proposes a dynamic program (use Neural Network) to identify the color image
depending on the distribution of the monochrome colors (red, green, and blue) in the same
image to make image signature accordingly, which is represented by a values named power
spectrum. The first step is to analyze the three-band monochrome image (color image) to
Red, Green and Blue image, then deal with each image as a grey scale one which is
represented as a 2-D matrix. The second step is to make Fourier Transform to each grey
scale image in order to extract the implicit information in that image. The calculations of 2-
D Power Spectrum for each image have been done to construct the final feature vector for
each one. Finally, in the third step, and in order to handle problems of large input
dimensions, a multilayer perceptron Neural Network has been used with two hidden layers.
The input of the Neural Network structure is the final feature vectors which are obtained
from the previous step. All programs are written using MATLAP VER. 6.5 programming
language.