Preferred Language
Articles
/
bBdFYJIBVTCNdQwC465R
Implementation of artificial neural network to achieve speed control and power saving of a belt conveyor system
...Show More Authors

According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through the conveyor belt motion. An optimal speed controlling mechanism of the conveyor belt is presented by detecting smartly the parts' number and weights using the vision sensor, where the latter will give sufficient visualization about the system. Then image processing will deliver the important data to ANN, which will optimally decide the best conveyor belt speed. This decided speed will achieve the aim of power saving in belt motion. The proposed controlling system will optimally switch the speed of the conveyor belt system to ON, OFF and idle status in order to minimize the consumption of energy in the conveyor belt. As the conveyor belt is fully loaded it moves at its maximum speed. But if the conveyor is partially loaded, the speed will be adjusted accordingly by the ANN. If no loading existed, the conveyor will be stopped. By this way, a very significant energy amount in addition to cost will be saved. The developed conveyor belt system will modernize industrial manufacturing lines, besides reducing energy consumption and cost and increasing the conveyor belts lifetime

Scopus Crossref
View Publication
Publication Date
Tue Jan 01 2019
Journal Name
Energy Procedia
The effect of the activation functions on the classification accuracy of satellite image by artificial neural network
...Show More Authors

View Publication
Scopus (19)
Crossref (12)
Scopus Clarivate Crossref
Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Artificial Neural Network (ANN) for Prediction of Viscosity Reduction of Heavy Crude Oil using Different Organic Solvents
...Show More Authors

The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

... Show More
View Publication Preview PDF
Crossref (3)
Crossref
Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Achieve the requirements of the Administrative Control according to human resource management practices
...Show More Authors

Human Resources Management Practices (HRMP) and managerial control represent two academic fields that have been and still are the focus of many studies. However, merging both fields and studying the relationship that connects them and also the role that HRMP play in achieving the requirements of managerial control represents a new and novel study according to the available literature in these fields.

To achieve these goals, this study has been conducted, using the surveying questionnaire method, on a sample of ten general inspector offices in Iraq that work in the field of control for ministries and independent committees. A questionnaire has been used to collect the data which was analyzed with several s

... Show More
View Publication Preview PDF
Crossref
Publication Date
Tue Oct 23 2018
Journal Name
Journal Of Economics And Administrative Sciences
Use projection pursuit regression and neural network to overcome curse of dimensionality
...Show More Authors

Abstract

This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Wed Jan 01 2020
Journal Name
Solid State Technology
Image Fusion Using A Convolutional Neural Network
...Show More Authors

Image Fusion Using A Convolutional Neural Network

Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Engineering
Water Quality Assessment and Total Dissolved Solids Prediction using Artificial Neural Network in Al-Hawizeh Marsh South of Iraq
...Show More Authors

The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The develope

... Show More
Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Engineering/
Water quality assessment and total dissolved solids prediction using artificial neural network in Al-Hawizeh marsh south of Iraq
...Show More Authors

The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The

... Show More
Publication Date
Sat Apr 01 2023
Journal Name
Heliyon
A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique
...Show More Authors

View Publication Preview PDF
Scopus (42)
Crossref (36)
Scopus Clarivate Crossref
Publication Date
Sat Dec 01 2018
Journal Name
Indian Journal Of Ecology
Classification of al-hammar marshes satellite images in Iraq using artificial neural network based on coding representation
...Show More Authors

Scopus (2)
Scopus
Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Design & Nature And Ecodynamics
Artificial Neural Network Assessment of Groundwater Quality for Agricultural Use in Babylon City: An Evaluation of Salinity and Ionic Composition
...Show More Authors

View Publication Preview PDF
Crossref (1)
Scopus Crossref