Autonomous Robot System for Pavement Crack Inspection Based Cnn Model - Sheta, A.; Mokhtar, S.A. (Journal of Theoretical and Applied Information Technology, 2022)
Maintaining the excellent state of the road is critical to secure driving and is an obligation of both transportation and regulatory maintenance authorities. For a safe driving environment, it is essential to inspect road surfaces for defects or degradation frequently. This process is found to be labor-intensive and necessitates primary expertise. Therefore, it is challenging to examine road cracks visually; thus, we must effectively employ computer visualization and robotics tools to support this mission. This research provides our initial idea of simulating an Autonomous Robot System (ARS) to perform pavement assessments. The ARS for crack inspection is a camera-equipped mobile robot (i.e., an Android phone) to collect images on the road. The proposed system is simulated using an mBot robot armed with an Android phone that gathers video streams to be processed on a server that has a pre-training Convolutional Neural Networks (CNN) that can recognize crack existence. The proposed CNN model attained 99.0% accuracy in the training case and 97.5% in the testing case. The results of this research are suitable for application with a commercial mobile robot as an autonomous platform for pavement inspections. © 2022 Little Lion Scientific.
Keywords: Automatic Detection; Autonomous Robot System; Crack Detection; Deep Neural Network; Pavement Crack; Road Maintenance; dept.csc; peer-reviewed; ⛔ No DOI found

Enhanced cuckoo search algorithm for industrial winding process modeling - Braik, M.; Sheta, A.; Al-Hiary, H.; Aljahdali, S. (Journal of Intelligent Manufacturing, 2022)
Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods. © 2022, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
Keywords: Cuckoo search algorithms; Developed model; Industrial systems; Industrial winding process; Learning algorithms; Linear modeling; Model method; Model structures; Non-linear modelling; Nonlinear systems; Optimization; Parameter estimation; Performance; Process-models; Winding; Winding process; dept.csc; peer-reviewed

Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems - Braik, M.; Al-Zoubi, H.; Ryalat, M.; Sheta, A.; Alzubi, O. (Artificial Intelligence Review, 2022)
Crow Search Algorithm (CSA) is a promising meta-heuristic method developed based on the intelligent conduct of crows in nature. This algorithm lacks a good representation of its individuals’ memory, and as with many other meta-heuristics it faces a problem in efficiently balancing exploration and exploitation. These defects may lead to early convergence to local optima. To cope with such issues, we proposed a Memory based Hybrid CSA (MHCSA) with the use of Particle Swarm Optimization (PSO) algorithm. This hybridization approach was proposed to reinforce the diversity ability of CSA and balance its search abilities for promising solutions to achieve robust search performance. The memory element of MHCSA was initialized with the best solution (pbest) of PSO to exploit the most promising search areas. The best positions of the CSA’s individuals are improved using the best solution found so far (gbest) and (pbest) of PSO. Another flaw of CSA is the use of fixed flight length and awareness probability for crows to control exploration and exploitation features, respectively. This issue was circumvented here by replacing these constants with adaptive functions in order to provide a better balance between exploration and exploitation over the course of iterations. The competence of MHCSA was revealed by testing it on seventy-three standard and computationally complex benchmark functions. Its applicability was substantiated by solving seven engineering design problems. The results showed that the problem of early convergence was eliminated by MHCSA and that the balance of exploration and exploitation was further improved. Further, MHCSA ranked first among CSA, PSO, robust variants of CSA and other strong competing methods in terms of accuracy and stability. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Keywords: Algorithm for solving; Balancing; Constrained global optimization; Constrained optimization; Crow search algorithm; Early convergence; Exploration and exploitation; Global optimization; Global optimization problems; Heuristic methods; Learning algorithms; Meta-heuristic methods; Metaheuristic; Optimization techniques; Particle swarm optimization (PSO); Search Algorithms; dept.csc; peer-reviewed