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Comparison of Robust Circular S and Circular Least Squares Estimators for Circular Regression Model using Simulation
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In this paper, the Monte-Carlo simulation method was used to compare the robust circular S estimator with the circular Least squares method in the case of no outlier data and in the case of the presence of an outlier in the data through two trends, the first is contaminant with high inflection points that represents contaminant in the circular independent variable, and the second the contaminant in the vertical variable that represents the circular dependent variable using three comparison criteria, the median standard error (Median SE), the median of the mean squares of error (Median MSE), and the median of the mean cosines of the circular residuals (Median A(k)). It was concluded that the method of least squares is better than the methods of the robust circular S method in the case that the data does not contain outlier values because it was recorded the lowest mean criterion, mean squares error (Median MSE), the least median standard error (Median SE) and the largest value of the criterion of the mean cosines of the circular residuals A(K) for all proposed sample sizes (n=20, 50, 100). In the case of the contaminant in the vertical data, it was found that the circular least squares method is not preferred at all contaminant rates and for all sample sizes, and the higher the percentage of contamination in the vertical data, the greater the preference of the validity of estimation methods, where the mean criterion of median squares of error (Median MSE) and criterion of median standard error (Median SE) decrease and the value of the mean criterion of the mean cosines of the circular residuals A(K) increases for all proposed sample sizes. In the case of the contaminant at high lifting points, the circular least squares method is not preferred by a large percentage at all levels of contaminant and for all sample sizes, and the higher the percentage of the contaminant at the lifting points, the greater the preference of the validity estimation methods, so that the mean criterion of mean squares of error (Median MSE) and criterion of median standard error (Median SE) decrease, and the value of the mean criterion increases for the mean cosines of the circular residuals A(K) and for all sample sizes.

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
USE OF MODIFIED MAXIMUM LIKELIHOOD METHOD TO ESTIMATE PARAMETERS OF THE MULTIPLE LINEAR REGRESSION MODEL
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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Comparison Study of Electromyography Using Wavelet and Neural Network
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In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

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Publication Date
Fri Apr 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Bayes Estimators With others , for scale parameter and Reliability function of two parameters Frechet distribution
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Publication Date
Tue Dec 07 2021
Journal Name
Tencon 2021 - 2021 Ieee Region 10 Conference (tencon)
Robust State Feedback Control of Electric Heating Furnace Using a New Disturbance Observer
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As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Geological Journal
Estimation of Initial Oil in Place for Buzurgan Oil Field by Using Volumetric Method and Reservoir Simulation Method
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The estimation of the initial oil in place is a crucial topic in the period of exploration, appraisal, and development of the reservoir. In the current work, two conventional methods were used to determine the Initial Oil in Place. These two methods are a volumetric method and a reservoir simulation method. Moreover, each method requires a type of data whereet al the volumetric method depends on geological, core, well log and petrophysical properties data while the reservoir simulation method also needs capillary pressure versus water saturation, fluid production and static pressure data for all active wells at the Mishrif reservoir. The petrophysical properties for the studied reservoir is calculated using neural network technique

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Publication Date
Sun Jan 01 2023
Journal Name
Aip Conference Proceedings
Mining categorical Covid-19 data using chi-square and logistic regression algorithms
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Publication Date
Mon Dec 11 2017
Journal Name
Al-khwarizmi Engineering Journal
Simulation Model of Wind Turbine Power Control System with Fuzzy Regulation by Mamdani and Larsen Algorithms
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Abstract 

     The aim of this work is to create a power control system for wind turbines based on fuzzy logic. Three power control loop was considered including: changing the pitch angle of  the blade, changing the length of the blade and turning the nacelle. The stochastic law was given for changes and instant inaccurate assessment of wind conditions changes. Two different algorithms were used for fuzzy inference in the control loop, the Mamdani and Larsen algorithms. These two different algorithms are materialized and developed in this study in Matlab-Fuzzy logic toolbox which has been practically implemented using necessary intelligent control system in electrical engineerin

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Publication Date
Sat Sep 23 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
An Efficient S.brunken Estimators For The Mean Of Normal Population With Kuown Variance
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This  article  co;nsiders a shrunken  estimator  Â·Of  Al-Hermyari·   and

AI Gobuii (.1) to estimate  the mean (8) of a normal clistributicm N (8 cr4)  with  known variance  (cr+),  when  <:I    guess value (So) av11il ble about the mean (B) as· an initial estrmate. This estimator is shown to be

more efficient tl1an the class-ical estimators  especially when 8 is close to 8•. General expressions .for bias and MSE -of considered  estitnator are gi 'en, witeh  some examples.  Nut.nerical cresdlts, comparisons  and

conclusions ate reported.

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Publication Date
Wed Aug 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Some Estimation Methods Of GM(1,1) Model With Missing Data and Practical Application
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This paper presents a grey model GM(1,1) of the first rank and a variable one and is the basis of the grey system theory , This research dealt  properties of grey model and a set of methods to estimate parameters of the grey model GM(1,1)  is the least square Method (LS) , weighted least square method (WLS), total least square method (TLS) and gradient descent method  (DS). These methods were compared based on two types of standards: Mean square error (MSE), mean absolute percentage error (MAPE), and after comparison using simulation the best method was applied to real data represented by the rate of consumption of the two types of oils a Heavy fuel (HFO) and diesel fuel (D.O) and has been applied several tests to

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Publication Date
Tue Nov 09 2021
Journal Name
Abu Dhabi International Petroleum Exhibition & Conference
Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field
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Abstract<p>Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorit</p> ... Show More
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