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If appropriately implemented, open databases of instruction material may help teaching and learning by providing content for teaching activities, scaffolding, and self-assessment. The paper presents the current results of the development and implementation of a database that is expressly built for promoting exchange of questions and exercises, together with the associated solutions among teachers for STEM subjects. Besides presenting and motivating the initiative (together with reporting its current status), the manuscript lists a series of lessons that have been learned while executing the project - including the need for proper management of authorship and version control of the uploaded material. Moreover, the manuscript describes which features any open database of instruction material should implement to aid improved usability, together with a series of nontrivial theoretical and practical problems for future scientific investigations (e.g., developing taxonomies for indexing the difficulty levels of the instruction material uploaded in the database that do not suffer from the subjective interpretability associated with the existing taxonomies).

Not available, Journal of Preventive Medicine and Hygiene, Vol. 61 No. 4 (2020): 2020614

This paper presents a new MRAS speed observer for high performance FOC induction motor drives which employs the flux error for estimating the rotor speed, but overcomes the pure integration problems by using a novel adaptive integration method based on neural adaptive filtering. A linear neuron (the ADALINE) is employed for the estimation of both the rotor speed and the rotor flux-linkage using a recursive TLS (Total Least Squares) algorithm (the TLS EXIN neuron) for on-line training. This neural model is also used as a predictor with no feedback loops between the output of the neural network and its input. The proposed scheme has been implemented in a test setup and compared with an MRAS OLS (Ordinary Least Squares) speed estimation with low-pass filter integration and with the well- known Schauder's scheme. The experimental results show the in the high and medium speed ranges with and without load, the three algorithms give practically the same results, while in low speed ranges (i.e. below 10 rad/s ) the TLS based algorithm outperforms the other two algorithms. Experiments have also been made to test the robustness of the algorithm to load perturbations and to test its performance at zero speed operation.

This paper deals with the command of inverters in DG (distributed generation) systems by use of linear neural networks in such a way that, with a slight upgrade of their control software, they can be used also to compensate for the harmonic distortion in the node where they are connected (local compensation), that is in the in the point of common coupling (PCC). To this purpose a neural estimator based on linear neurons (ADALINEs) has been developed which is able to act as a selective noise cancellers for each harmonic of the node voltage. The use of linear neurons permits the drawbacks of classical neural networks to be overcome and moreover the neural estimator is easy to implement, thus allowing the same inverter to be used also for active filtering. The paper presents and discusses the results obtained by simulation of a small size distribution network, which in itself contains the distorted voltages coming both from the utility and from a nonlinear load and an inverter supplied by an array of photovoltaic panels with its DC/DC internal converter.

Many computer vision techniques, above all for structure from motion problems, require a segmentation of the images captured by one or more cameras. This paper deals the segmentation based on the motion information, but can be easily extended to other cases (color, texture and so on). A new neural network, the EXIN Segmentation Neural Network (EXIN SNN) is here introduced. It is incremental, self-organizing and considers its task as the solution of a pattern recognition problem. This original approach overcomes the limits of the traditional segmentation techniques, namely the need of a spatial support for the image objects and the translation parallel to the image plane for the objects in the scene. Examples are given both for synthetic and real images.

The aim of the paper is to define a probabilistic methodology for the definition of a stochastic safety index, and to show a possible estimation method based upon Bayesian inference. As an important application of the proposed methodology, the attention is focused upon cable-way plant design. The method was applied to data froma a real cable-way plant, situated at the Department of Electrical Engineering of the University of Palermo. This paper presents and discusses the results of a first series of experimental tests for system safety evaluation. For this purpose, a stochastic index for the braking system is first presented and discussed; then, its Bayesian estimation is developed and its feasibility and efficiency shown, with reference to field data and extensive Monte Carlo Simulation. Finally, studies on the robustness of the estimate have been performed with excellent results.