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Peak-shaving in district heating systems through optimal management of the thermal request of buildings
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Microstructure evolution and capacity: Comparison between 2090-T8 aluminium alloy and pure aluminium anodes
No full textFabrication and characterization of erbium doped bioactive glasses, glass ceramics and optical fibers
No full textPhotovoltaic solar systems for smart bus shelters in the urban environment of turin (Italy)
Get PDFThe paper deals with the transformation of the existing bus shelters for the public transport into smart systems equipped with Wi-Fi stations, USB chargers and Air Quality Control Station. The proposed smart shelters are renewable energy producer since they are equipped with PV modules. The location of the most suitable bus shelters is defined by solar irradiation maps based on a GIS tool. Four solutions are proposed with a cost-benefits analysis
Analysis of stator faults in induction machines using growing curvilinear component analysis
No full textFault detection of shorted turns in the stator windings of Induction Motors (IMs) is possible in a variety of ways. As current sensors are usually installed together with the IMs for control and protection purposes, using stator current for fault detection has become a common practice nowadays, as it is much cheaper than installing additional sensors. In this study, stator currents from the healthy and faulty IMs are obtained and analysed via MATLAB® software. The current signatures from healthy and faulty IMs are conditioned using the inbuilt DSP module of the dSPACE prior to analysis using AI techniques. This paper presents a Growing Curvilinear Component Analysis (GCCA) neural network which is able to correctly identify anomalies in the IM and follow the evolution of the stator fault using its current signature, making on-line early fault detection possible
All in a twitter: Self-tuning strategies for a deeper understanding of a crisis tweet collection
No full textNatural disasters have become more frequent during the past 20 years due to significant climate changes. These natural events are hotly debated on social networks like Twitter and a huge amount of short text messages are continuously and promptly exchanged with personal opinions, descriptions of the natural events and their corresponding consequences. The analysis of these large and complex data could help policy-makers to better understand the event as well as to set priorities. However, the correct configuration of the tweet mining process is still challenging due to variable data distribution and the availability of a large number of algorithms with different specific parameters. The analyst need to perform a large number of experiments to identify the best configuration for the overall knowledge discovery process. Innovative, scalable, and parameter-free solutions need to be explored to streamline the analytics process. This paper presents an enhanced version of PASTA (a distributed self-tuning engine) applied to a crisis tweet collection to group a corpus of tweets into cohesive and well-separated clusters with minimal analyst intervention. Experimental results performed on real data collected during natural disasters show the effectiveness of PASTA in discovering interesting groups of correlated tweets without selecting neither the algorithms nor their parameters
Monitoraggio dinamico post-sisma della Basilica di San Nicola da Tolentino
No full textGli eventi sismici che hanno colpito l'Italia Centrale nell'agosto ed ottobre 2016 hanno provocato importanti perdite nei piccoli centri che caratterizzano questi territori. Numerose costruzioni storiche di straordinaria importanza artistica sono crollate o hanno subito danni ingenti. La Basilica di San Nicola da Tolentino, una delle più importanti testimonianze del patrimonio storico ed artistico dell'area, ha subito rilevanti danni nelle sue componenti strutturali principali che indicano una possibile riduzione della capacità sismica. Assume cruciale importanza monitorare la risposta strutturale nel tempo in modo da seguire l'evoluzione dei danni esistenti e determinare come possano incidere sulla stabilità strutturale. Sulla base di ciò il Comune di Tolentino, il Politecnico di Torino e la Nagoya City University hanno siglato un accordo di cooperazione strategica finalizzato al monitoraggio strutturale dinamico della Basilica. Le analisi dei dati acquisiti saranno focalizzate alla identificazione dei parametri dinamici in grado di evidenziare aspetti critici come l'attivazione di meccanismi locali o l'integrità delle connessioni tra i diversi elementi strutturali. Questo articolo descrive il sistema di monitoraggio installato nell'aprile 2017 e mostra i risultati delle analisi preliminari condotte sui dati acquisiti durante i primi mesi
Studiare architettura al castello del Valentino. La Regia Scuola di Applicazione per gli Ingegneri di Torino (1859-1906)
No full textOptimized Upload Strategies for Live Scalable Video Transmission from Mobile Devices
No full textSharing live multimedia content is becoming increasingly popular among mobile users. In this article, we study the problem of optimizing video quality in such a scenario using scalable video coding (SVC) and chunked video content. We consider using only standard stateless HTTP servers that do not need to perform additional processing of the video content. Our key contribution is to provide close to optimal algorithms for scheduling video chunk upload for multiple clients having different viewing delays. Given such a set of clients, the problem is to decide which chunks to upload and in which order to upload them so that the quality-delay tradeoff can be optimally balanced. We show by means of simulations that the proposed algorithms can achieve notably better performance than naive solutions in practical cases. Especially the heuristic-based greedy algorithm is a good candidate for deployment on mobile devices because it is not computationally intensive but it still delivers in most cases on-par video quality compared to the more complex local optimization algorithm. We also show that using shorter video segments and being able to predict bandwidth and video chunk properties improve the delivered video quality in certain cases