The present article focuses on the observation of flux for SynRM! (SynRM!) in the presence of magnetic saturation and cross-saturation. In this context, the proposed observer is able to provide flux estimation based on the measured current only. The designed observer makes it possible to propose a flux-based control which, in presence of saturation, is simpler to implement than a classic current-based control. Simulation results, with a model obtained from FEA! (FEA!) using FEMM! (FEMM!), show the performance of the proposed approach.
This study investigates the long-term impact of dynamic overvoltage stress on GaN HEMTs using a newly designed test circuit, UIS3, a variant of classic UIS, which isolates key stress factors. Devices were subjected to short-duration repetitive overvoltage stress near and below their dynamic breakdown voltage. Characterization before and after stress reveals permanent degradation in CDS, IDSS and IGSS, suggesting deep-trapping or structural damage within the device. A distinct alteration in the CDS curve is observed, may indicate less spreading of the electric-field within the device. RDS,on degradation is also noted, likely due to trapping effects, with partial recovery at room temperature. Higher stress levels accelerate failure. Waveform analysis and post-failure characterization indicate a short-circuit failure mode, likely due to partial dielectric breakdown during overvoltage events. These results provide new insights into GaN HEMT degradation mechanisms under high-voltage stress.
Pneumatic Artificial Muscles (PAMs) are complex nonlinear systems characterized by hysteresis, making them challenging to model with classical system identification methods. While deep learning has emerged as a powerful tool for modeling nonlinear systems from data, purely neural networkbased models often lack interpretability and are prone to overfitting. To address these challenges, this study explores several hybrid approaches that combine analytical models with neural networks to model PAM behavior more effectively. The results demonstrate that hybrid models significantly outperform both purely analytical and black-box neural network models, particularly in terms of generalization and dynamic accuracy. Among the approaches, the Physics-Informed Neural Network (PINN) unsupervised model shows the most robust performance, capturing complex PAM dynamics while maintaining computational efficiency. These findings suggest that hybrid modeling is a promising and scalable solution for accurately representing the intricate behavior of PAMs.
The integration of Artificial Intelligence (AI) and Ambient Intelligence (AmI) has emerged as a promising approach to creating responsive and contextually aware environments. AmI creates contextually aware environments by seamlessly integrating intelligent technologies, while AI develops algorithms for autonomous learning and decision-making. However, embedding AI within AmI environments faces challenges due to limited resources and energy constraints. While recent research on embedded AI has primarily focused on specific tasks of AmI, our goal is to develop a comprehensive framework encompassing all the necessary components for practical use cases. Through this endeavor, we aim to explore power-aware designs and distributed learning as fundamental approaches to address limited computational resources, energy constraints, and dynamic context variations challenges.
Learning medical gestures requires regular hands-on training to acquire the dexterity needed to perform them without injuring patients. For obvious ethical reasons, this training cannot be carried out directly on the patient. In this context, the use of cadavers has long been the preferred method of training, despite the difficulty of obtaining them and the fact that cadavers deteriorate rapidly. For several years, technologies have led to the development of medical training simulators that combine a numerical simulation (reproducing the organs' behavior during the gesture) with haptic devices (reproducing tactile sensations). As designers of several haptic training simulators, we aim to impart our expertise by detailing in this paper an empirical design methodology for the development of such simulators.
