In automotive and industrial domains, the “health monitoring” or “condition monitoring” of electronic devices is gradually playing a key role in manufacturing processes and innovation roadmaps. The concept of health monitoring is often related to the so-called “residual lifetime” of the monitored system. In this work, the authors have designed a deep learning system for the health monitoring of power devices in Silicon Carbide (SiC) technology used in the Traction Inverter Systems of the latest generation electric cars. A Temporal Fusion Transformer embedding such layers of Temporal Convolutional Network with a Multi-Head Attention block for the robust lifetime assessment of SiC power devices, is proposed. Specifically, the designed system predicts such future samples of the ON-state voltage between drain and source of the low-side part of the SiC power module $Vds_{LS}$ , in half-bridge configuration. Extensive literature confirmed that the $Vds_{LS}$ signal can be efficiently used as a robust predictive device-degradation marker. Through the learning of the temporal feature relationships at different scales and the intelligent selection of relevant input features, the proposed solution will discard unnecessary input dynamics building a multi-step predictive model of the $Vds_{LS}$ signal, significantly more performing than the existing state-of-the-art architectures. The proposed deep pipeline has been tested on several ACEPACK ^TM DRIVE SiC power modules delivered by STMicroelectronics, with an average error of about $0.2\%$ , confirming the effectiveness of the proposed system.

This chapter combines traditional artificial intelligence (AI) concepts, i.e., computational ontologies, with more recent trends, i.e., deep learning for content-based semantic retrieval in Cultural Heritage. More specifically, the proposed AI-empowered system employs computational ontologies for modelling photographs of religious historical buildings. The ontology, besides supporting data-modelling and concept-level annotation, guides a learning process – implemented through Convolutional Neural Network (CNN) – for automated image categorization and retrieval. The whole system has been tested on the ruins of the church of Santa Maria delle Grazie in Misterbianco, Catania, Italy, showing satisfactory performance. Request access from your librarian to read this chapter's full text.

In this paper we present a method/tool for integrating heterogeneous data, assessing and visualizing sentiments related to big impact events in geographically confined populations. The data employed are official statistical information provided by governments, news web sites and user submitted georeferenced comments retrieved from various social networks. Sentiment analysis is applied to the retrieved comments and results are visualized on interactive maps, thus providing an effective tool for decision makers and analysts to evaluate how socioeconomic factors can influence the mood and the opinion of a specific area. The method is designed to assist city planners, business managers or social scientists for strategic planning and decision making. Furthermore, the experimental evaluation showed that the proposed method is robust, achieves state-of-the-art performance and allows an easy-exploration of big, distributed and heterogeneous information.

Modern semiconductor manufacturing production lines include several technological processes such as diffusion, wafer cleaning and epitaxy and therefore they are subject to degradation phenomena that must be carefully monitored. The processed semiconductor wafer consequently requires a very accurate testing phase which allows to detect the presence of defects to be traced back to the production line and which can have an impact on the downstream yield. The electric wafer testing phase (EWS i.e. Electrical Wafer Sorting) is certainly one of the key stages in semiconductor wafer monitoring in both classical silicon-based technology and modern silicon carbide production lines. The EWS validation phase includes the execution of several electrical tests directly on the integrated circuits (dies) over the wafer. The EWS electrical assessment outcome can be reported in a 2D binary image-map enabling the formation of specific visual patterns in accordance with the defect statistics found in the testing phase. Specifically, from this intelligent EWS analysis it is possible to early-assess defects in the production lines, electrical defects in the devices and finally a proper wafer-yield forecasting estimation. The classification of the EWS image-map visual patterns is a very challenging and time-consuming task. In that context, ad-hoc deep learning solution has been developed in order to perform supervised/unsupervised EWS image-maps patterns recognition and tracking. The related pipeline is composed by ad-hoc convolutional deep architecture with an innovative augmentation system based on spatiotemporal preprocessing of input EWS image-maps. The implemented architecture is able to classify the input EWS binary maps into a well-known pattern-classes whose features at the production line level and the impact on yield are known. The designed deep network is able to identify new EWS patterns not included in the already known classes, so as to address the production-engineer accordingly. The proposed pipeline shows very promising results as it is able to classify the tested EWS with an average accuracy close to 97%.