This paper discusses using H2 and H∞ robust control approaches for designing control systems. These approaches are applied to elementary control system designs, and their respective implementation and pros and cons are introduced. The H∞ control synthesis mainly enforces closed-loop stability, covering some physical constraints and limitations. While noise rejection and disturbance attenuation are more naturally expressed in performance optimization, which can represent the H2 control synthesis problem. The paper also applies these two methodologies to multi-plant systems to study the stability and performance of the designed controllers. Simulation results show that the H2 controller tracks a desirable closed-loop performance, while the H∞ controller guarantees robust stability for the closed-loop system. The validation of the techniques is demonstrated through the robust and performance gamma index, where the H∞ controller achieved a robust gamma index of 0.8591, indicating good robustness and the H2 controller achieved a performance gamma index of 2.1972, indicating a desirable performance. The robust control toolbox of MATLAB is used for simulation purposes. Overall, the paper shows that selecting a suitable, robust control strategy is crucial for designing effective control systems, and the H2 and H∞ robust control approaches are viable options for achieving this goal.
Tungsten inert gas arc welding–based shaped metal deposition is a novel additive manufacturing technology which can be used for fabricating solid dense parts by melting a cold wire on a substrate in a layer-by-layer manner via continuous DC arc heat. The shaped metal deposition method would be an alternative way to traditional manufacturing methods, especially for complex featured and large-scale solid parts manufacturing, and it is particularly used for aerospace structural components, manufacturing, and repairing of die/molds and middle-sized dense parts. This article presents the designing, constructing, and controlling of an additive manufacturing system using tungsten inert gas plus wire–based shaped metal deposition metho
... Show MoreThe use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow
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Abstract :
Robust statistics Known as, Resistance to mistakes resulting of the deviation of Check hypotheses of statistical properties ( Adjacent Unbiased , The Efficiency of data taken from a wide range of probability distributions follow a normal distribution or a mixture of other distributions with different standard deviations.
power spectrum function lead to, President role in the analysis of Stationary random processes, organized according to time, may be discrete random variables or continuous. Measuring its total capacity as frequency function.
Estimation methods Share with
... Show MoreObjective: The study aimed to identify the adolescents' family meal eating patterns, and find out the relationship between adolescents' family meal eating patterns and their weight control behaviors. Methodology: A descriptive study was conducted on impact of adolescents' family meal eating patterns upon their weight control behaviors in secondary schools at Baghdad city, starting from 20th of April 2013 to the end of October 2014. Non- probability (purposive) sample of 1254 adolescents were chosen from secondary schools of both sides of Al-Karkh and Al-Russafa sectors. Data was collected through a specially
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
... Show MoreBackground: Patients with type 2 diabetes have an increased prevalence of lipid abnormalities, contributing to their high risk of cardiovascular diseases (CVD).Glycated hemoglobin (HbA1c) is a routinely used marker for long-term glycemic control. In accordance with its function as an indicator for the mean blood glucose level, HbA1c predicts the risk for the development of diabetic complications in diabetic patients[2].Apart from classical risk factors like dyslipidemia, HbA1c has now been regarded as an independent risk factor for (CVD) in subjects with or without diabetes.Objective The aim of this study was to find out association between glycaemic control (HbA1c as a marker) and serum lipid profile in type 2 diabetic patients.Methods
... Show MoreAmputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte
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