A sensorless position detection technique for a permanent magnet (PM) motor with non-salient rotor is presented. It is shown that the phase self inductances and resistances measured at higher frequencies vary slightly with the rotor position. The origin of the variations is found in the iron B-H hysteresis characteristics. In order to exploit these small variations for the rotor position detection, a measurement setup is proposed. The technique is tested and shows good results and promises further developments.

This article is focused on the automatic classification of passing vehicles through an experimental platform using optical sensor arrays. The amount of data generated from various sensor systems is growing proportionally every year. Therefore, it is necessary to look for more progressive solutions to these problems. Methods of implementing artificial intelligence are becoming a new trend in this area. At first, an experimental platform with two separate groups of fiber Bragg grating sensor arrays (horizontally and vertically oriented) installed into the top pavement layers was created. Interrogators were connected to sensor arrays to measure pavement deformation caused by vehicles passing over the pavement. Next, neural networks for visual classification with a closed-circuit television camera to separate vehicles into different classes were used. This classification was used for the verification of measured and analyzed data from sensor arrays. The newly proposed neural network for vehicle classification from the sensor array dataset was created. From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended.

The paper presents a detailed analysis and comparison of two types of non-axial windings used for slotless permanent magnet (PM) motors: zigzag and rhombic ones. The motor mathematical model is verified by 3D FEM software. Finally, the motor prototype is presented.

Internet of Things (IoT) becomes indispensable for transport and automotive industry to advance functions in on-road traffic monitoring. Indeed, smart management tools and machine learning concepts are inevitable in vehicle categorization systems. However, existing systems are only built on individual platforms, and for a most part, the classification accuracy remains limited. In this work, these challenges are tackled by designing a novel convolutional neural network (CNN) that substantially improves on-road vehicle classification. In particular, we experimentally harness, to the best of our knowledge for the first time, two different datasets from separated technological platforms based on close-circuit television (CCTV) and fiber Bragg grating (FBG) sensors, respectively. Standard neural networks in single FBG platform yield limited classification accuracy of only 34% - 62% for AlexNet , 51% - 77% for GoogleNet , 57% - 78% for ResNet-50 , and 59% - 86% for ResNet-101 . In contrast, hybrid CNN classification with individual CCTV and FBG datasets substantially improves detection levels, reaching in-class accuracy up to 90% - 97%. Moreover, this classification concept includes an intrinsic back-up verification with respect to each platform compensating the shortcomings of individual technologies. Our demonstration can make key advances towards near-unity accuracy in vehicle classifications for IoT systems, capitalizing on cost-effective and well-established platforms.

In recent years, the technology of optical fibers has rapidly gained ground in many areas of science and industry, including the construction industry. In this article, the technology of optical fibers based on a fiber Bragg grating (FBG) was used to determine tensile forces acting in a basal reinforcement of a scaled down physical model, which included piled embankment and basal reinforcement. Installing FBG sensors on the geogrid made monitoring of axial strains possible, thus allowing determination of the behavior of the basal reinforcement of the piled embankment. On the basis of three tests performed on the physical model, numerical model calibration with the physical model was carried out using the software PLAXIS 3D Tunnel 2.4. The results showed accurate predictions, especially for the low and middle part of the measured deformations where the numerical analysis proposed a solution that can be considered as safe. Installing FBG sensors on biaxial geogrids was a bold idea that was not easy to implement. However, other possibilities have been successfully tested, such as high-frequency measurements of the response of reinforced soil structure under dynamic loading.

We present an analysis of an electromechanical implantable glucose sensor for continuous glucose monitoring. An efficient 3D numerical model to accurately compute the torques, forces and the induced voltages for ironless actuators of any geometrical form is developed. The algorithm is based on the generalized Biot-Savart law and the permanent magnet equivalent current density model