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Second Order Sliding Mode Controller Design for Pneumatic Artificial Muscle
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In this paper, first and second order sliding mode controllers are designed for a single link robotic arm actuated by two Pneumatic Artificial Muscles (PAMs). A new mathematical model for the arm has been developed based on the model of large scale pneumatic muscle actuator model. Uncertainty in parameters has been presented and tested for the two controllers. The simulation results of the second-order sliding mode controller proves to have a low tracking error and chattering effect as compared to the first order one. The verification has been done by using MATLAB and Simulink software.

 

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Publication Date
Wed Oct 17 2018
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
ESTIMATION OF MUNICIPAL SOLID WASTE GENERATION AND LANDFILL VOLUME GENERATION AND LANDFILL VOLUME USING ARTIFICIAL NEURAL NETWORKS
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Publication Date
Wed Nov 22 2017
Journal Name
Farm Machinery And Processes Management In Sustainable Agriculture, Ix International Scientific Symposium
INFLUENCE OF PHYSICAL PROPERTIES OF WATER-ADJUVANT MIXTURE ON THE DROPLET STAINS DEPOSITING ON AN ARTIFICIAL TARGET
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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
Mon Apr 01 2019
Journal Name
Journal Of Engineering
Outdoor Testing of a Zig-Zag Solar Air heater with and without Artificial Roughness on Absorber Plate
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In this paper, thermal performance of a zig-zig solar air heater (ZZSAH) with and without using steel wire mesh on the absorber plate of the collector is experimentally investigated. The experimental work includes four inclination angles of the collector 20o, 30o, 45o, and 60o and four air mass flow rates of 0.03, 0.04, 0.06, and 0.08 kg/s under varieties of operating conditions of a geographic location of  Baghdad. New correlation equations of Nusselt number are obtained from experimental results for both types of collectors where the effect of varying of the inclination angle of collector taken into consideration in the experiment. The correlations show good agreement wi

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Publication Date
Tue Jun 30 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Artificial Neural Network to Predict Rate of Penetration from Dynamic Elastic Properties in Nasiriya Oil Field
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   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic propert

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Publication Date
Tue Dec 12 2017
Journal Name
Al-khwarizmi Engineering Journal
Model Reference Adaptive Control based on a Self-Recurrent Wavelet Neural Network Utilizing Micro Artificial Immune Systems
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Abstract 

This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per

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Publication Date
Mon Nov 11 2019
Journal Name
Day 3 Wed, November 13, 2019
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
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Abstract<p>The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the</p> ... Show More
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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
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Publication Date
Sat Jan 01 2022
Journal Name
Ssrn Electronic Journal
The Prospective of Artificial Neural Network (ANN’s) Model Application to Ameliorate Management of Post Disaster Engineering Projects
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Currently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica

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Publication Date
Fri Sep 15 2017
Journal Name
Journal Of Baghdad College Of Dentistry
The Effect of Artificial Saliva on The Surface Roughness of Different Esthetic Archwires (An in Vitro Study)
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Background:The demand for esthetic orthodontic appliances is increasing so that the esthetic orthodontic archwires were introduced. This in vitro study was designed to evaluate the surface roughness offiber-reinforced polymer composite (FRPC) archwires compared to coated nickel-titanium (NiTi) archwires immersed in artificial saliva. Materials and Methods:Three types of esthetic orthodontic archwires were used: FRPC (Dentaurum), Teflon coated NiTi (Dentaurum) and epoxy coated NiTi (Orthotechnology). They were round (0.018 inch) in cross section and cut into pieces of 15 mm in length.Forty pieces from each type were divided into four groups; one group was left at a dry condition and the other three groups were immersed in artificial saliva (

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