An optimisation-based energy disaggregation algorithm for low frequency smart meter data

An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives

Miglioramento di algoritmi di elaborazione di immagini da scanner 3D tramite Simulated Annealing

Nel lavoro si descrive l’applicazione di tecniche di IA per l’elaborazione e l’allineamento di immagini provenienti da scanner 3D, in grado di generare modelli digitali tridimensionali. Al fine di allineare più correttamente i fotogrammi acquisiti, in scansioni successive, dal dispositivo e ricostruire accuratamente il modello tridimensionale digitale, si è integrata la tecnica di Simulated Annealing, con un algoritmo di elaborazione di immagini (Iterative Closest Point, ICP) che allinea un nuovo fotogramma al modello già generato tramite roto-traslazioni consecutive

Challenges and opportunities in deploying a mobility platform integrating public transport and car-pooling services

This paper introduces a new mobility platform that favours reducing individual car use, by combining car flexibility with advantages offered by public transport, such as punctuality, comfort, safety and low environmental impact. Such platform services are delivered by means of a smartphone app that, thanks to advanced artificial intelligence algorithms, performs multi- modal vehicle routing by accounting for walking, public transport and car-pooling rides. To explore citizens’ attitudes and perceptions towards SocialCar, and assess its overall business potential, we tested a prototype version in Canton Ticino (Southern Switzerland), engaging common citizens and their everyday mobility needs. In this paper we first present the app and the route planning algorithms we developed to match travel demand and offer, commenting on the challenges to be addressed when using real-life data (shortcomings in mapping, public transport and car-pooling data). Then, we describe the methodology used to assess the SocialCar overall potential, based on focus group meetings run before and after the field test, and summarize the results obtained, in terms of strengths, weaknesses, threats and opportunities for a large-scale diffusion of the SocialCar platform. Finally, we comment on the lessons learnt and provide recommendations for future similar "mobility as a service" platforms

Digital pain extent is associated with pain intensity but not with pain-related cognitions and disability in people with chronic musculoskeletal pain:a cross-sectional study

BACKGROUND: To evaluate whether digital pain extent is associated with an array of psychological factors such as optimism, pessimism, expectations of recovery, pain acceptance, and pain self-efficacy beliefs as well as to analyse the association between digital pain extent and pain intensity and pain-related disability in people with chronic musculoskeletal pain. METHODS: A descriptive cross-sectional study conducted in a primary health care setting was carried out including 186 individuals with chronic musculoskeletal pain. Patient-reported outcomes were used to assess psychological factors, pain intensity, and pain-related disability. Digital pain extent was obtained from pain drawings shaded using a tablet and analysed using novel customized software. Multiple linear regression models were conducted to evaluate the association between digital pain extent and the aforementioned variables. RESULTS: Digital pain extent was statistically significantly associated with pain intensity. However, digital pain extent was not associated with any psychological measure nor with pain-related disability. DISCUSSION: The results did not support an association between digital pain extent and psychological measures