A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties.
Recently Tobit Quantile Regression(TQR) has emerged as an important tool in statistical analysis . in order to improve the parameter estimation in (TQR) we proposed Bayesian hierarchical model with double adaptive elastic net technique and Bayesian hierarchical model with adaptive ridge regression technique .
in double adaptive elastic net technique we assume different penalization parameters for penalization different regression coefficients in both parameters λ1and λ2 , also in adaptive ridge regression technique we assume different penalization parameters for penalization different regression coefficients i
... Show MoreRobots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel
... Show MoreRobots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel
... Show MoreBackground: The treatment of articular cartilage defects is one of the most clinical challeng for orthopedic surgeons. Articular cartilage is a highly organized tissue with complex biomechanical properties and substantial durability. However, it has a poor ability for healing, and damage from trauma or degeneration can result in morbidity and functional impairment. debilitating joint pain, dysfunction, and degenerative arthritis &nbs
... Show MoreBackground: The treatment of articular cartilage defects is one of the most clinical challeng for orthopedic surgeons. Articular cartilage is a highly organized tissue with complex biomechanical properties and substantial durability. However, it has a poor ability for healing, and damage from trauma or degeneration can result in morbidity and functional impairment. debilitating joint pain, dysfunction, and degenerative arthritis Objectives: The purpose of study is to show effectiveness of micro fracture arthroscopy as a method of treatment for such problem . Type of the study: Cros
... Show MoreBackground: Knee osteoarthritis (KOA) is a common joint disorder leading to considerable pain and locomotor disability in lower limb function. Locomotor disability, which is difficulty in activities of daily living related to lower limb function, can be the consequence of KOA, so early diagnosis and management may improve quality of life.
Objective: To assess the contribution of radiological osteoarthritis of the knees to disability in the activities of daily living related to lower limb function.
Methods: One hundred twenty Iraqi KOA patients (104 females and 16 males) who were attending to Rheumatology Unit, Full history was taken and complete clinical exami
... Show MoreIntroduction: Elite football performance hinges on rapid tactical decision-making under physical and cognitive strain. While peripheral fatigue’s effects on motor output are well documented, the neurophysiological markers of mental fatigue and their impact on in-game decision making remain underexplored. Objective: To determine how EEG-derived central fatigue indices—frontal theta power and the theta/alpha ratio—relate to tactical decision accuracy and speed in elite football players. Methodology: Twenty male national-level footballers (age 22.4 ± 2.1 years; ≥ 5 years’ experience) completed the Yo-Yo Intermittent Recovery Test Level 1 while wearing an 8-channel dry-electrode frontal EEG headset. Frontal theta
... Show MoreResearchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat
... Show MoreThe objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.
The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed sign