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Paper ThA2ML.2

Susto, Gian Antonio (University of Padova), Vettore, Leonardo (University of Padova), Zambonin, Giuliano (Electrolux), Altinier, Fabio (Electrolux), Beninato, Daniele (Electrolux), Girotto, Terenzio (Electrolux), Rampazzo, Mirco (Universita degli Studi di Padova), Beghi, Alessandro (Università di Padova)

A Machine Learning-Based Soft Sensor for Laundry Load Fabric Typology Estimation in Household Washer-Dryers

Scheduled for presentation during the Regular Session "Machine Learning and Human-Centric Applications" (ThA2ML), Thursday, August 22, 2019, 11:10−11:30,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on November 29, 2021

Keywords Emerging areas, Expert systems, Learning, adaptation and evaluation

Abstract

Fabric care manufactures are striving to make more energy efficient and more user-friendly products. The aim of this work is to develop a Soft Sensor (SS) for a householdWasher-Dryer (WD) that is able to distinguish between different fabrics loaded in the machine; the knowledge of load composition may lead to a more accurate drying, faster processed and lower energy consumption without increasing the production costs. Moreover, automatic classification of load fabric will lead to an enhanced user experience, since user will be required to provide less information to the WD to obtain optimal drying processes. The SS developed in this work exploits sensors already in place in a commercial WD and, on an algorithmic point of view, it exploit regularization methods and Random Forests for classification. The efficacy of the proposed approach has been tested on real data in heterogeneous conditions.

 

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