Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.

Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.

Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.

In this paper, numerous experimental tests were carried out to study the behavior of concrete containing glass fibers and confined with glass fiber-reinforced polymer (GFRP). Concrete specimens containing different fiber percentages ( 0.3 wt.%, 0.6 wt.%, 0.9 wt.% or 1.2 wt.%) and with different strengths of concrete (8.5 MPa, 16 MPa and 25 MPa) and different confinement levels (two, four and six layers of GFRP) were used as research parameters. The samples were tested to failure under pure axial compression. The results imply that the confinement effect with GFRP is relatively higher for concrete samples containing glass fiber (GFCC) with a percentage equal to 0.6 wt.%. The theoretical of stress ratios (fcc/fco) estimated by using existing ultimate strength models are found to be close to the experimental results for high strength of GFCC, but not close to the experimental results for low strength of GFCC.

In this paper, numerous experimental tests were carried out to study the behavior of concrete containing glass fibers and confined with glass fiber-reinforced polymer (GFRP). Concrete specimens containing different fiber percentages ( 0.3 wt.%, 0.6 wt.%, 0.9 wt.% or 1.2 wt.%) and with different strengths of concrete (8.5 MPa, 16 MPa and 25 MPa) and different confinement levels (two, four and six layers of GFRP) were used as research parameters. The samples were tested to failure under pure axial compression. The results imply that the confinement effect with GFRP is relatively higher for concrete samples containing glass fiber (GFCC) with a percentage equal to 0.6 wt.%. The theoretical of stress ratios (fcc/fco) estimated by using existing ultimate strength models are found to be close to the experimental results for high strength of GFCC, but not close to the experimental results for low strength of GFCC.

In this paper, numerous experimental tests were carried out to study the behavior of concrete containing glass fibers and confined with glass fiber-reinforced polymer (GFRP). Concrete specimens containing different fiber percentages ( 0.3 wt.%, 0.6 wt.%, 0.9 wt.% or 1.2 wt.%) and with different strengths of concrete (8.5 MPa, 16 MPa and 25 MPa) and different confinement levels (two, four and six layers of GFRP) were used as research parameters. The samples were tested to failure under pure axial compression. The results imply that the confinement effect with GFRP is relatively higher for concrete samples containing glass fiber (GFCC) with a percentage equal to 0.6 wt.%. The theoretical of stress ratios (fcc/fco) estimated by using existing ultimate strength models are found to be close to the experimental results for high strength of GFCC, but not close to the experimental results for low strength of GFCC.