This paper presents a multipurpose UAV (unmanned aerial vehicle) for mountain rescue operations. The multi-rotors based flying platform and its embedded avionics are designed to meet environmental requirements for mountainous terrain such as low temperatures, high altitude and strong winds, assuring the capability of carrying different payloads (separately or together) such as: avalanche beacon (ARTVA) with automatic signal recognition and path following algorithms for the rapid location of snowcovered body; camera (visible and thermal) for search and rescue of missing persons on snow and in woods during the day or night; payload deployment to drop emergency kits or specific explosive cartridge for controlled avalanche detachment. The resulting small (less than 5 kg) UAV is capable of full autonomous flight (including takeoff and landing) of a pre-programmed, or easily configurable, custom mission. Furthermore, the autopilot manages the sensors measurements (i.e. beacons or cameras) to update the flying mission automatically in flight. Specific functionalities such as terrain following were developed and implemented. Ground station programming of the UAV is not needed, except compulsory monitoring, as the rescue mission can be accomplished in a full automatic mode.

The centrifugal softening effect is an alleged and elusive reduction of the natural frequencies of a rotating system with increasing speed which is sometimes found in finite element rotordynamics. This reduction may, in some instances, be large enough to cause some of the natural frequencies to vanish, leading to a sort of elastic instability. Some doubts can, however, be cast on the phenomenon itself and on the mathematical models causing it to appear. The aim of the present work is to shed some light on centrifugal softening and to discuss the assumptions that are at the basis of three-dimensional FEM modeling in rotordynamics. One and two degrees of freedom models, such as the ones introduced by Rankine and Jeffcott, are first studied and then the classical rotating beam, ring, disk, and membrane are addressed. Some numerical models, built using the FEM, are then solved using both dedicated and general purpose codes. In all cases no strong centrifugal softening is found.

The vibration control of rotors for gas or steam turbines is usually performed using passive dampers when hydrodynamic bearings are not used. In layouts where the rotating parts are supported by rolling bearings, the damping is usually provided by squeeze film dampers. Their passive nature and the variability of their performances with temperature and frequency represent the main disadvantages. Dampers with magnetorheological and electrorheological fluid allow solving only a part of the abovementioned drawbacks. Active magnetic bearings (AMBs) are promising since they are very effective in controlling the vibration of the rotor and offering the possibility of monitoring the rotor’s behavior using their displacement sensors. However they show serious drawbacks related to their stiffness. Electromagnetic dampers seem to be a valid alternative to visco-elastic, hydraulic dampers due to, among the others, the absence of all fatigue and tribology issues resulting from the absence of contact, the small sensitivity to the working environment, the wide possibility of tuning even during operation, the predictability of the behavior, the smaller mass compared with AMBs, and the failsafe capability. The aim of the present paper is to describe a design methodology adopted to develop electromagnetic dampers to be installed in aero-engines. The procedure has been validated using a reduced scale laboratory test rig. The same approach has then been adopted to design the electromagnetic dampers for real civil aircraft engines. The results in terms of achievable vibration reductions, mass, and overall dimensions are hence presented. A trade-off between the various proposed solutions has been carried out evaluating quantitative performance parameters together with qualitative aspects that this “more electric” technology implies.

Cloud Robotics is an emerging paradigm in which robots, seen as abstract agents, have the possibility to connect to a common network and share on a complex infrastructure the information and knowledge they gather about the physical world; or conversely consume the data collected by other agents or made available on accessible database and repositories. In this paper we propose an implementation of an emergency-management service exploiting the possibilities offered by cloud robotics in a smart city scenario. A high-level cloud-platform manages a number of unmanned aerial vehicles (quadrotor UAVs) with the goal of providing aerial support to citizens that require it via a dedicated mobile app. The UAV reaches the citizen while forwarding a realtime video streaming to a privileged user (police officer),connected to the same cloud platform, that is allowed to teleoperate it by remote.

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation.