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Model based smooth super-twisting control of cancer chemotherapy treatment
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Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exogenous perturbations. In accordance with clinical recommendations, the tumor volume follows a predefined trajectory after chemotherapy. Secondly, the MBSSTC is applied for the three cell-kill models to attain accurate trajectory tracking even in the presence of uncertainties and disturbances. Compared to conventional super-twisting control (STC), the non-smooth term is introduced in the proposed control to enhance the anti-disturbance capability. Finally, simulation comparisons are performed across the proposed MBSSTC, conventional STC, and proportional–integral (PI) control methods to show the effectiveness and merits of our designed control method.

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Publication Date
Sun Aug 01 2021
Journal Name
International Journal Of Mechanical Engineering And Robotics Research
Adaptive Approximation-Based Feedback Linearization Control for a Nonlinear Smart Thin Plate
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This paper proposes feedback linearization control (FBLC) based on function approximation technique (FAT) to regulate the vibrational motion of a smart thin plate considering the effect of axial stretching. The FBLC includes designing a nonlinear control law for the stabilization of the target dynamic system while the closedloop dynamics are linear with ensured stability. The objective of the FAT is to estimate the cubic nonlinear restoring force vector using the linear parameterization of weighting and orthogonal basis function matrices. Orthogonal Chebyshev polynomials are used as strong approximators for adaptive schemes. The proposed control architecture is applied to a thin plate with a large deflection that stimulates the axial loadin

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Publication Date
Sun Oct 01 2023
Journal Name
Ieee Transactions On Industrial Electronics
Singular Perturbation-Based Adaptive Integral Sliding Mode Control for Flexible Joint Robots
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The flexible joint robot (FJR) typically experiences parametric variations, nonlinearities, underactuation, noise propagation, and external disturbances which seriously degrade the FJR tracking. This article proposes an adaptive integral sliding mode controller (AISMC) based on a singular perturbation method and two state observers for the FJR to achieve high performance. First, the underactuated FJR is modeled into two simple second-order fast and slow subsystems by using Olfati transformation and singular perturbation method, which handles underactuation while reducing noise amplification. Then, the AISMC is proposed to effectively accomplish the desired tracking performance, in which the integral sliding surface is designed to reduce cha

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Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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Publication Date
Mon Feb 01 2021
Journal Name
Iop Conference Series: Materials Science And Engineering
On modelling and adaptive control of a linear smart beam model interacting with fluid
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Abstract<p>This paper deals with modelling and control of Euler-Bernoulli smart beam interacting with a fluid medium. Several distributed piezo-patches (actuators and/or sensors) are bonded on the surface of the target beam. To model the vibrating beam properly, the effect of the piezo-patches and the hydrodynamic loads should be taken into account carefully. The partial differential equation PDE for the target oscillating beam is derived considering the piezo-actuators as input controls. Fluid forces are decomposed into two components: 1) hydrodynamic forces due to the beam oscillations, and 2) external (disturbance) hydrodynamic loads independent of beam motion. Then the PDE is discretized usi</p> ... Show More
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Publication Date
Tue Oct 01 2024
Journal Name
Separation And Purification Technology
A comprehensive review on the use of Ti3C2Tx MXene in membrane-based water treatment
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Publication Date
Thu Sep 01 2022
Journal Name
Precision Radiation Oncology
Assessment of dose‐volume histogram statistics using three‐dimensional conformal techniques in breast cancer adjuvant radiotherapy treatment
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Abstract<sec><title>Objective

Breast cancer (BC) is first of the top 10 malignancies in Iraq. Dose‐volume histograms (DVHs) are most commonly used as a plan evaluation tool. This study aimed to assess DVH statistics using three‐dimensional conformal radiotherapies in BC in an adjuvant setting.

Methods

A retrospective study of 70 histologically confirmed women diagnosed with BC was reviewed. The study was conducted between November 2020 and May 2021, planning for treatment with adjuvant three‐dimensional conformal radiotherapies. The treatment plan used for each woman was based on an analysis of the volumetric dose, inclu

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Publication Date
Thu Nov 08 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Evaluation of nurses’ knowledge toward pain management of leukemic child under chemotherapy
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Objective(s): To evaluate nurses' knowledge toward pain management of leukemic child in oncology wards
how were receiving chemotherapy.
Methodology: A descriptive study was conducted in two hospitals on (40) nurses, who provided care for the
children with leukemia in oncology wards (2) hospitals (Children Welfare Teaching Hospital and Child’s Central
Teaching Hospital) in Baghdad city from October 2010 up to the 27th of October 2011 for the purpose of
evaluating their knowledge towards pain management for leukemic child. A purposive "non-probability
sample" was selected that consisted of (40) nurse who are working in oncology wards. A questionnaire format
was used which consist of (2) parts, the first part includes

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Publication Date
Tue May 01 2018
Journal Name
2018 2nd Ieee Advanced Information Management,communicates,electronic And Automation Control Conference (imcec)
Hybrid Regressor and Approximation-Based Adaptive Control of Robotic Manipulators with Contact-Free Motion
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Publication Date
Fri Jan 01 2021
Journal Name
Fme Transactions
FAT-based adaptive backstepping control of an electromechanical system with an unknown input coefficient
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This paper is focused on orthogonal function approximation technique FAT-based adaptive backstepping control of a geared DC motor coupled with a rotational mechanical component. It is assumed that all parameters of the actuator are unknown including the torque-current constant (i.e., unknown input coefficient) and hence a control system with three motor control modes is proposed: 1) motor torque control mode, 2) motor current control mode, and 3) motor voltage control mode. The proposed control algorithm is a powerful tool to control a dynamic system with an unknown input coefficient. Each uncertain parameter/term is represented by a linear combination of weighting and orthogonal basis function vectors. Chebyshev polynomial is used

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