Showing papers by "Sabah M. Ahmed published in 2022"

Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging

Abstract: Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses state-of-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy.

The Effect of Coil Geometry and Winding Method on the Electromagnetic Launcher Performance

Abstract: Electromagnetic launchers become spread in many applications. This type of launcher is used to accelerate a slug made of ferromagnetic material. Many researchers compete to enhance the performance of this type. The launcher consists of simple components such as a capacitor bank, electronic switches, launcher coil, and charging circuit. In this paper, the authors have physically built the launcher system and reinforced the launcher performance by changing the winding method and making the trigger coil in a conical shape to reduce the deceleration effect of the last half of the trigger, this modification has proven its superiority over the straight coil.

Predicting the Motion of the End Effector in a Flexible Interconnected Manipulator with Neural Networks

Abstract: The estimation of the position of flexible robotic manipulators is a challenging task, especially for parallel and interconnected robots. This paper proposes a method to estimate the position of the end-effector of a flexible interconnected manipulator based on a virtual sensor principle. By using MSC ADAMS software, we developed a virtual prototype of the flexible interconnected manipulator and devised all feasible neural networks that map nonlinear relationships between angles of the active/passive joints to the position of the end-effector. The results indicated that it is possible to use the neural network estimate the position of the end-effector with a single passive joint and with high accuracy in both training-testing, with Mean Squared Error in the scale of 10−3 m, and unseen environments, with error bounded by less than 0.2 mm. The obtained results show the proficiency of the nonlinear relationships based on neural networks to predict the motion/position of the flexible manipulator with a promising and desirable capability.

End-to-End Mobile Robot Navigation using a Residual Deep Reinforcement Learning in Dynamic Human Environments

Abstract: Safe navigation through human crowds is key to enabling practical mobility ubiquitously. The Deep Reinforcement Learning (DRL) and the End-to-End (E2E) approaches to goal-oriented robot navigation have the potential to render policies able to tackle localization, path planning, obstacle avoidance, and adaptation to change in unison. In this paper, we report an architecture based on convolutional units and residual blocks being able to enhance adaptability to unseen and dynamic human environments. In particular, our scheme outperformed the state-of-the-art baselines SOADRL and NAVREP by about 13% and 18% on average success rate, respectively, throughout 27 unseen and dynamic navigation instances. Furthermore, our approach avoids the explicit encoding of positions and trajectories of moving humans compared to the standard models. Our results show the potential to render adaptive and generalizable policies for unknown and dynamic human environments.

End-Effector Position Estimation and Control of a Flexible Interconnected Industrial Manipulator Using Machine Learning

Abstract: The control of flexible robot manipulators is a challenging task, especially when one considers parallel and interconnected manipulators under flexibility considerations. This paper proposes a method to estimate the position of the end-effector of a flexible interconnected manipulator based on a virtual sensor principle and function approximation schemes. By using SolidWorks/MSC ADAMS software, we developed a virtual prototype of a flexible interconnected manipulator, and rigorously evaluated the feasibility of using function approximation schemes such as Neural Networks (NN), Support Vector Machines (SVM), and Gaussian Process (GP) in estimating the deflection error arising due to the flexibility of the robot structure. Our rigorous computational experiments have shown that: (1) the NN, SVM, and GP models were are able to attain the promising and reasonable prediction accuracy, (2) a feedforward NN with 535 neurons and an Ascending distribution of its nodes achieves the best prediction and generalization to unseen environments (the upper bound of the error was 0.15 × 10-3 m); implying the robust estimation of the position of the end-effector under flexibility considerations, and (3) the control based on the inverse Jacobian and a NN-based estimator was able to follow a sinusoidal trajectory with reasonable tracking and error performance in MSC ADAMS & MATLAB/Simulink co-simulation. Our results show the feasibility and effectiveness of the nonlinear relationships learned by NN, SVM, and GP in aiding estimation and control of the position of the end-effector of the flexible manipulator with a promising/desirable capability.