This experimental study aims to investigate the effects of the strengthening arrangement on the behavior and the performance of strengthened beams, according to the Near-Surface Mounted (NSM) and the Externally Bonded Reinforcement (EBR) techniques. In total five rectangular beams including a Control Beam (CB) and four Carbon Fiber Reinforced Polymer (CFRP) strengthened beams with NSM and EBR techniques. The beams were tested to failure in Four-Point Bending (FPB) test. The experimental program comprises two beams strengthened by one and two strips according to the NSM technique. Two other beams were strengthened by the same configuration with the EBR, whereas the last beam was un-strengthened and considered as the CB. The responses of control and strengthened beams were compared. The efficiency and the effectiveness of different CFRP configurations were evaluated. The test results showed that the flexural load capacity, the deflection, the ductility and the stiffness of strengthened beams increased with increasing of plates distribution. This increase was more significant for the EBR technique than the NSM. This paper also highlights the beams failure modes due to the different configurations of strengthening. The obtained results revealed that the crack patterns were affected by the arrangement of the strips.

This experimental study aims to investigate the effects of the strengthening arrangement on the behavior and the performance of strengthened beams, according to the Near-Surface Mounted (NSM) and the Externally Bonded Reinforcement (EBR) techniques. In total five rectangular beams including a Control Beam (CB) and four Carbon Fiber Reinforced Polymer (CFRP) strengthened beams with NSM and EBR techniques. The beams were tested to failure in Four-Point Bending (FPB) test. The experimental program comprises two beams strengthened by one and two strips according to the NSM technique. Two other beams were strengthened by the same configuration with the EBR, whereas the last beam was un-strengthened and considered as the CB. The responses of control and strengthened beams were compared. The efficiency and the effectiveness of different CFRP configurations were evaluated. The test results showed that the flexural load capacity, the deflection, the ductility and the stiffness of strengthened beams increased with increasing of plates distribution. This increase was more significant for the EBR technique than the NSM. This paper also highlights the beams failure modes due to the different configurations of strengthening. The obtained results revealed that the crack patterns were affected by the arrangement of the strips.

Seismic fragility curves are considered an effective tool for the evaluation of the behavior of interaction of the soil-pile-structure (ISPS) subjected to earthquake loading. In this research, in order to better understand the ISPS effect, a nonlinear static analysis is applied with a variation of the vertical load, the diameter of pile, and finally the longitudinal steel ratio of the pile in different types of sand (loose, medium, dense) to obtain the capacity curves of each parameter for elaborating the curves of fragility. After a comparison of fragility curves of these parameters, it appears that the effect of the ISPS system is advantageous with respect to the vertical axial load and the diameter of pile, while the longitudinal ratio of the pile depending on the ductility and the lateral resistance of the ISPS system. The proposed equation is intended to help engineers in the design and performance of the soil-pile-structure interaction. The results of this equation provided a convergence with the results of the fragility curves.

Seismic fragility curves are considered an effective tool for the evaluation of the behavior of interaction of the soil-pile-structure (ISPS) subjected to earthquake loading. In this research, in order to better understand the ISPS effect, a nonlinear static analysis is applied with a variation of the vertical load, the diameter of pile, and finally the longitudinal steel ratio of the pile in different types of sand (loose, medium, dense) to obtain the capacity curves of each parameter for elaborating the curves of fragility. After a comparison of fragility curves of these parameters, it appears that the effect of the ISPS system is advantageous with respect to the vertical axial load and the diameter of pile, while the longitudinal ratio of the pile depending on the ductility and the lateral resistance of the ISPS system. The proposed equation is intended to help engineers in the design and performance of the soil-pile-structure interaction. The results of this equation provided a convergence with the results of the fragility curves.

Seismic fragility curves are considered an effective tool for the evaluation of the behavior of interaction of the soil-pile-structure (ISPS) subjected to earthquake loading. In this research, in order to better understand the ISPS effect, a nonlinear static analysis is applied with a variation of the vertical load, the diameter of pile, and finally the longitudinal steel ratio of the pile in different types of sand (loose, medium, dense) to obtain the capacity curves of each parameter for elaborating the curves of fragility. After a comparison of fragility curves of these parameters, it appears that the effect of the ISPS system is advantageous with respect to the vertical axial load and the diameter of pile, while the longitudinal ratio of the pile depending on the ductility and the lateral resistance of the ISPS system. The proposed equation is intended to help engineers in the design and performance of the soil-pile-structure interaction. The results of this equation provided a convergence with the results of the fragility curves.

Seismic fragility curves are considered an effective tool for the evaluation of the behavior of interaction of the soil-pile-structure (ISPS) subjected to earthquake loading. In this research, in order to better understand the ISPS effect, a nonlinear static analysis is applied with a variation of the vertical load, the diameter of pile, and finally the longitudinal steel ratio of the pile in different types of sand (loose, medium, dense) to obtain the capacity curves of each parameter for elaborating the curves of fragility. After a comparison of fragility curves of these parameters, it appears that the effect of the ISPS system is advantageous with respect to the vertical axial load and the diameter of pile, while the longitudinal ratio of the pile depending on the ductility and the lateral resistance of the ISPS system. The proposed equation is intended to help engineers in the design and performance of the soil-pile-structure interaction. The results of this equation provided a convergence with the results of the fragility curves.

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

This work deals with the study and performance improvement of the direct power control of DFIG based on backstepping Controller. Direct power control using hysteresis regulator has certain disadvantages such as significant powers ripples, variable switching frequency and sensitivity to parametric variations. To surmount these disadvantages, we present a robust controller such as the backstepping-based direct power control using SVM. A comparison study was made between the classic direct power control and the backstepping controller. The simulation results show that the backstepping controller provides good results reduces powers ripples.

This work deals with the study and performance improvement of the direct power control of DFIG based on backstepping Controller. Direct power control using hysteresis regulator has certain disadvantages such as significant powers ripples, variable switching frequency and sensitivity to parametric variations. To surmount these disadvantages, we present a robust controller such as the backstepping-based direct power control using SVM. A comparison study was made between the classic direct power control and the backstepping controller. The simulation results show that the backstepping controller provides good results reduces powers ripples.

This work deals with the study and performance improvement of the direct power control of DFIG based on backstepping Controller. Direct power control using hysteresis regulator has certain disadvantages such as significant powers ripples, variable switching frequency and sensitivity to parametric variations. To surmount these disadvantages, we present a robust controller such as the backstepping-based direct power control using SVM. A comparison study was made between the classic direct power control and the backstepping controller. The simulation results show that the backstepping controller provides good results reduces powers ripples.

This work deals with the study and performance improvement of the direct power control of DFIG based on backstepping Controller. Direct power control using hysteresis regulator has certain disadvantages such as significant powers ripples, variable switching frequency and sensitivity to parametric variations. To surmount these disadvantages, we present a robust controller such as the backstepping-based direct power control using SVM. A comparison study was made between the classic direct power control and the backstepping controller. The simulation results show that the backstepping controller provides good results reduces powers ripples.

In this investigation, state vector estimation of the Permanent Magnet Synchronous machine (PMSM) using the nonlinear Kalman estimator (Extended Kalman Filter) is considered. The considered states are the speed of the rotor, its angular position, the torque of the load and the resistance of the stator. Since the extended Kalman filter contains some free parameters, it will be necessary to optimize them in order to obtain a better efficiency. The free parameters of EKF are the covariance matrices of state noise and measurement noise. These later will be auto adjusted by a new metaheuristic optimization technique called Biogeographical-based optimization (BBO). As far as we know, BBO–EKF optimization for PMSM state was not treated in the literature. The suggested estimation tuning approach is demonstrated using a computer simulation of a PMSM. Simulated experimentations show the robustness and effectiveness of the proposed scheme. In addition, a detailed comparative study with conventional methods like Particle Swarm Optimization and Genetic Algorithms will be given.