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Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
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The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinematic equation. To feed the neural network, experimental data were taken from an elastic robot arm for training the network, these data presented by joint angles, deformation variables and end-effector positions. The results of network training showed a good fit between the output results of the neural network and the targets data. In addition, this method for finding the inverse of kinematic equation proved its effectiveness and validation when applying the results of neural network practically in the robot’s operating software for controlling the real light robot’s position.

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Publication Date
Wed Feb 01 2023
Journal Name
Journal Of Engineering
Applying the WaterGEMS Software to Conduct a Comparison of the Darcy-Weisbach and Hazen-Williams Equations for Calculating the Frictional Head Loss in a Selected Pipe Network
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Darcy-Weisbach (D-W) is a typical resistance equation in pressured flow; however, some academics and engineers prefer Hazen-Williams (H-W) for assessing water distribution networks. The main difference is that the (D-W) friction factor changes with the Reynolds number, while the (H-W) coefficient is a constant value for a certain material. This study uses WaterGEMS CONNECT Edition update 1 to find an empirical relation between the (H-W) and (H-W) equations for two 400 mm and 500 mm pipe systems. The hydraulic model was done, and two scenarios were applied by changing the (H-W) coefficient to show the difference in results of head loss. The results showed a strong relationship between both equations with correlation coefficients of 0.999,

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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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Publication Date
Sun Mar 01 2020
Journal Name
Sustainable Chemistry And Pharmacy
A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network
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Publication Date
Tue Aug 01 2017
Journal Name
Journal Of Engineering
Rigid trunk sewer deterioration prediction models using multiple discriminant and neural network models in Baghdad city, Iraq
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The deterioration of buried sewers during their lifetime can be affected by several factors leading to bad performance and can damage the infrastructure similar to other engineering structures. The Hydraulic deterioration of the buried sewers caused by sewer blockages while the structural deterioration caused by sewer collapses due to sewer specifications and the surrounding soil characteristics and the groundwater level. The main objective of this research is to develop deterioration models, which are used to predict changes in sewer condition that can provide assessment tools for determining the serviceability of sewer networks in Baghdad city. Two deterioration models were developed and tested using statistical software SPSS, the

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Publication Date
Mon Jul 31 2017
Journal Name
Journal Of Engineering
Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq
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Publication Date
Sun May 01 2016
Journal Name
Journal Of Engineering
Prediction of Ryznar Index for the treated water from WTPs on Al-Karakh side of Baghdad City using Artificial Neural Network (ANN) technique
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In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For

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Publication Date
Wed Mar 01 2023
Journal Name
Baghdad Science Journal
Fractional Hartley Transform and its Inverse
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The Hartley transform generalizes to the fractional Hartley transform (FRHT) which gives various uses in different fields of image encryption. Unfortunately, the available literature of fractional Hartley transform is unable to provide its inversion theorem. So accordingly original function cannot retrieve directly, which restrict its applications. The intension of this paper is to propose inversion theorem of fractional Hartley transform to overcome this drawback. Moreover, some properties of fractional Hartley transform are discussed in this paper.

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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
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Publication Date
Thu May 01 2025
Journal Name
2025 3rd International Conference On Business Analytics For Technology And Security (icbats)
Comparison of Deep Neural Network Models (LSTM, Bi-LSTM, GRU and Bi-GRU) for Gold Price Prediction
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This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

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Publication Date
Tue Oct 08 2002
Journal Name
Iraqi Journal Of Laser
Study of Laser Propagation Parameters in the Underdense Plasma Region Using a Two Dimensional Simulation Code
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The propagation of laser beam in the underdense deuterium plasma has been studied via computer simulation using the fluid model. An appropriate computer code “HEATER” has been modified and is used for this purpose. The propagation is taken to be in a cylindrical symmetric medium. Different laser wavelengths (1 = 10.6 m, 2 = 1.06 m, and 3 = 0.53 m) with a Gaussian pulse type and 15 ns pulse widths have been considered. Absorption energy and laser flux have been calculated for different plasma and laser parameters. The absorbed laser energy showed maximum for  = 0.53 m. This high absorbitivity was inferred to the effect of the pondermotive force.

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