Design, Construction, and Controlling of A Shaped Metal Deposition Machine Using Arc Metal-Wire System
Publication Date
Wed Jun 01 2022
Journal Name
Applied Energy
(56)
(35)
Publication Date
Sat Feb 01 2025
Journal Name
Journal Of Structural Design And Construction Practice
Publication Date
Fri Jan 01 2021
Journal Name
Artificial Intelligence For Covid-19
(38)
(30)
Publication Date
Mon Nov 11 2019
Journal Name
Spe
Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis
Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame</p>
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neural network
strength
machine learning
ROP prediction
Upstream Oil & Gas
prediction
ANN model
input parameter
multiple regression analysis
artificial neural network
(7)
Publication Date
Tue Jun 18 2024
Journal Name
Heat Transfer Engineering
(7)
(8)
Publication Date
Wed Sep 22 2021
Journal Name
The Structural Design Of Tall And Special Buildings
(4)
(3)
Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
A Mathematical Model of a Thermally Activated Roof (TAR) Cooling System Using a Simplified RC-Thermal Model with Time Dependent Supply Water Temperature
concrete core conditioning
thermally activated roof
RC- thermal model
thermo-active building simulation.
This paper presents a computer simulation model of a thermally activated roof (TAR) to cool a room using cool water from a wet cooling tower. Modeling was achieved using a simplified 1-D resistance-capacitance thermal network (RC model) for an infinite slab. Heat transfer from the cooling pipe network was treated as 2-D heat flow. Only a limited number of nodes were required to obtain reliable results. The use of 6th order RC-thermal model produced a set of ordinary differential equations that were solved using MATLAB - R2012a. The computer program was written to cover all possible initial conditions, material properties, TAR system geometry and hourly solar radiation. The cool water supply was considered time
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Fri Dec 15 2023
Journal Name
Al-academy
The use of metal in ancient Syria in the manufacture of jewelry (ornamental tools in the third and second millennium BC and the molds used in their manufacture)
metal
ancient Syria
jewelry industry
ornamental tools
moulds
This research sheds light on the use of metal in the manufacture of jewelry, which is represented by ornamental tools in the period between the third and second millennium BC, in addition to the most important molds used in their manufacture. Man has been interested in metals since early ages, and was able to make tools that he uses in his daily life, especially jewelry. And the Syrian people got acquainted with the types of minerals, their characteristics, and how to deal with them. Minerals played an effective and prominent role in the economy of ancient Syria. Trade with those countries and secure their roads.
Publication Date
Sat Jun 27 2020
Journal Name
Asian Journal Of Civil Engineering
(5)
(5)
Publication Date
Mon Jan 20 2025
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Assessing Landsat Processing Levels and Support Vector Machine Classification
Support vector machine
remote sensing
atmospheric correction
Landsat 9
classification.
The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv
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