<p><span>To remedy the difficulties encountered by Moroccan students in geometrical optics during the transition from secondary to higher education and for an efficient education system, we propose this study to investigate the causes of these difficulties as well as their impact on the quality of the secondary/university transition, and come up with a remediation device to overcome them. For this survey, we adopted a mixed method to collect both qualitative and quantitative data. The research tools used are semi-directive interviews with thirty high school and university teachers; questionnaires and exercises were administered to 120 of first year university students. The results of this study revealed the persistence of difficulties in geometrical optics and particularly in geometric construction whose origins are attributed to the misrepresentations and low prerequisites among students as well as a break in the curriculum and teaching methods during the transition from secondary to higher education.</span></p>

Controlling the polarization of the light output from single-mode fiber systems is very important for connecting it to polarization-dependent integrated optical circuits, while applications using a heterodyne detection system. Polarization controller using fiber squeezer is attractive for a low-loss, low-penalty coherent optical fiber trunk system. However, for polarization controllers using electromagnetic fiber squeezer, the stability problem due to the saturation of their magnetic circuit must be studied. In fact, in their conventional configuration, open-loop stability affects performance and limits applications. First at all, this effect has been analyzed and a feedback circuit with correctors has been proposed to improve stability performance. Then a simulation study is proposed to examine the influence of the system parameters on the corrector constants. The results of the simulation show that if the system parameters change the constants Kp, Ki and Kd of the PID corrector must be adjusted to keep an optimized dynamic response.

As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.

Solenoid valves represent indispensable elements in various engineering systems. Their failure can lead to unexpected problems. This failure may be caused by fluctuations in the coil resistance of the electromagnetic solenoid (EMS) which actuates these solenoid valves. Hence the need to monitor this parameter for a preventive maintenance of these actuators. The proposed method consists to use supervised machine learning to monitor coil resistance of the EMS valve. The EMS valve is coupled to an optical fiber squeezer which, acts as a force sensor. The solenoid armature applies a mechanical force to the optical fiber and changes the polarization state of the light that travels through the optical fiber and then this force infects the power of the light. A Simulink model is used to determine the open loop system step response. The identification of the system allows obtaining its transfer function, which depends on the parameters of the EMS and in particular on its coil resistance. By varying the coil resistance while fixing the other physical parameters of the EMS, we generate a database whose elements are the coefficients of the transfer function of the solenoid open loop and the electrical resistance of its coil. The generated database is used for training several supervised machine learning models whose predictors are the elements of the transfer function; the response is the coil resistance. The Gaussian process for regression allows to predict the variations of the coil resistance with the smallest relative error although it takes a relatively long time for the training compared to the other models used.

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