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Found 5 papers, 1 papers with code
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques
Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes.
Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components.
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal.
Machine learning methods for modelling and analysis of time series signals in geoinformatics
Reliable and fast knowledge about residential energy consumption at appliance level becomes increasingly important nowadays and it is an important mitigation measure to prevent energy wastage.
Creating a Baseline for Robust Energy Savings Estimation in Households
In this paper we provide an analytical methodology to handle such scenarios and create year-long hourly-based consumption baseline.
