ياسر حاتم علوان الرُبيعي

A cable driven parallel robot (CDPR) is a specific kind of parallel robot that utilizes cables as opposed to traditional rigid links to perform their functions. This gives it the advantages of being lightweight, having a wide operational workspace, and being easier and less expensive to implement than alternatives since it only requires cables operated by motors and fixed to a structure suitable for a certain application, allowing them to move and rotate an end-effector. The concept of this robot is not new though using a fully automated version of it was only started in the mid- 1980s and witnessed extensive research in the past two decades. This paper gives a review of this robot, its types, applications, modelling and control which is helpful in shortening the time for researchers in this area. CDPRs are still a fruitful area for research like exploiting it in new applications or improving and designing new control strategies.

A suspended cable-driven parallel robot is a type of lightweight large-span parallel robot. The stability and control of this multi-input multi-output robot are studied in this work to overcome its inherited vulnerability to disturbance. An adaptive fuzzy synergetic controller is proposed to overcome these issues, combining synergetic control theory with adaptive fuzzy logic to ensure robust trajectory tracking. The parameters of the controller are optimized using the Dragonfly Algorithm, a metaheuristic technique known for its simplicity and fast convergence. The adaptive fuzzy synergetic controller is tested on a suspended cable-driven parallel robot model under both disturbance-free and disturbed conditions, demonstrating global asymptotic stability and superior tracking accuracy compared to existing controllers. Simulation results show the proposed controller achieves minimal tracking error and improved robustness in the presence of dynamic uncertainties, validating its practical applicability in industrial scenarios. The findings highlight the effectiveness of integrating synergetic control, fuzzy logic adaptation, and optimization for enhancing the performance and reliability of suspended cable-driven parallel robots.

The kinematics and dynamics of a suspended cable-driven parallel robot with spatial of three degrees of freedom are presented. This robot has many applications, though, because of its structure, it is vulnerable to disturbance and uncertainties. This paper presents a proposed robust controller to deal with this issue. First, a fuzzy proportional–integral–derivative (PID) controller, which combines the merits of a fuzzy logic controller and the conventional widely used PID controller, is chosen as the main controller. Dragonfly algorithm (DA), as a relatively new metaheuristic algorithm, is known to have advantages over other classical ones. It is utilized to optimize PID gains until the required tracking error is achieved. Then, the chosen controller is incorporated with the robust classical sliding mode controller (SMC) in a way that balances the performance and robust-ness. DA is further exploited to optimize the parameters of SMC. Mathematical stability calculations and code modeling show the effectiveness of the proposed controller in performance and robustness.

This study presents a fast multi-face detection and recognition system suitable for surveillance applications using a self-supervised dataset generation approach. The face detection subsystem employs pretrained frontal and profile Haar cascade classifier (HCC) that are preprocessed for light normalization and postprocessed for eliminating duplicate detections. The detector is assisted by skin-tone postprocessing and a Kernelized Correlation Filter (KCF) tracker. To achieve a significant speed gain, HCC runs sparsely while KCF maintains face tracking-detection in the skipped frames. This hybrid HCC achieves F1-score improvements of 0.029 to 0.245 over baseline HCC and approximately 12× speed gain. Although Multi-Task Convolutional Neural Network (MTCNN) and YOLOv8 achieve higher F1 scores, hybrid HCC maintains superior frame rates, operating approximately 2-9× faster than these advanced models. Detected faces are extracted, augmented, and organized into a dataset (66.6% training, 33.3% testing split), enabling a custom lightweight Convolutional Neural Network (CNN) to successfully handle face recognition and achieve a perfect F1-score in a prediction test. The complete system demonstrates an effective solution for real-time surveillance applications in resource constrained environments.

In this paper, an unsupervised change detection technique is used for obtaining the change mask of forest fire depending on bi-temporal images, pre-event, and post-event. While there are many techniques for change detection, they don't give the same result accuracy for different data types. Here, two mid-resolution remotely sensed RGB images are used with combined methods to estimate the change that resulted from a burn scar. Lack of enough resolution is a key factor in this scenario as it could be the only data to deal with. Linear regression is utilized to find a linear relationship between the two images and use the linear parameters for constructing the background scene of the post-event image. The post-event image, the background image and the difference between them, in addition to greenness index images of pre-event and post-event with their difference, are processed by Principal Component Analyses (PCA) to obtain the most descriptive principal component of the change. A simple greenness function is proposed here depending on segmentation in HSV (hue, saturation, and value or intensity) color space to indicate the vegetation index. The results of this combined method confirm that it is more accurate in comparison with the methods it combines each alone.

In this paper the Jeffery prior information and the extension of Jeffery prior information for estimating the parameter Weibull distribution is presented. Through simulation study the performance of this estimator was compared to the standard Bayes with Jeffery prior information with respect to the mean square error (MSE) and mean percentage error (MPE). In the results, The new estimator with extension of Jeffery prior information is the best estimator for Weibull Distribution, when compared it with standard Bayes with Jeffery prior information. Also depending on MSE and MPE, the is the best survival function for Weibull distribution when compared it with survival function based on posterior distribution. We can easily conclude that MSE and MPE of Bayes estimators decrease with an increased of sample size.