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An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

Scientific data, 2017-01, Vol.4 (1), p.160122-160122, Article 160122

2017

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On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

IEEE access, 2016, Vol.4, p.1784-1799

2016

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Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation

Sensors (Basel, Switzerland), 2020-12, Vol.21 (1), p.2

2020

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Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing

IEEE access, 2018, Vol.6, p.53944-53959

2018

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Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring

Sensors (Basel, Switzerland), 2023-05, Vol.23 (10), p.4845

2023

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Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation

Sensors (Basel, Switzerland), 2020-11, Vol.20 (22), p.6628

2020

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Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation

Sensors (Basel, Switzerland), 2023-09, Vol.23 (19), p.8164

2023

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Synthetic Data Generation for Deep Learning-Based Inversion for Velocity Model Building

Remote sensing (Basel, Switzerland), 2023-06, Vol.15 (11), p.2901

2023

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Human Activity Recognition of Individuals with Lower Limb Amputation in Free-Living Conditions: A Pilot Study

Sensors (Basel, Switzerland), 2021-12, Vol.21 (24), p.8377

2021

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Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability

Energies (Basel), 2022-03, Vol.15 (6), p.2200

2022

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A Machine Learning-Driven Approach to Uncover the Influencing Factors Resulting in Soil Mass Displacement

Geosciences (Basel), 2024-08, Vol.14 (8), p.220

2024

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Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Sensors (Basel, Switzerland), 2022-12, Vol.23 (1), p.243

2022

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Smart Dimmable LED Lighting Systems

Sensors (Basel, Switzerland), 2022-11, Vol.22 (21), p.8523

2022

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Low-complexity energy disaggregation using appliance load modelling

AIMS energy, 2016, Vol.4 (1), p.1-21

2016

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Author Correction: The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

Scientific data, 2024-09, Vol.11 (1), p.1001-1, Article 1001

2024

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The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

Scientific data, 2024-04, Vol.11 (1), p.376-17, Article 376

2024

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Undirected Graphs: Is the Shift-Enabled Condition Trivial or Necessary?

IEEE access, 2021, Vol.9, p.75082-75089

2021

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Non-Intrusive Load Disaggregation Using Graph Signal Processing

IEEE transactions on smart grid, 2018-05, Vol.9 (3), p.1739-1747

2018

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An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem

Applied energy, 2023-07, Vol.341, p.121078, Article 121078

2023

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Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

Applied energy, 2019-03, Vol.238, p.796-805

2019

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Human Upper Limb Motion Analysis for Post-Stroke Impairment Assessment Using Video Analytics

IEEE access, 2016, Vol.4, p.650-659

2016

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Non-intrusive load disaggregation solutions for very low-rate smart meter data

Applied energy, 2020-06, Vol.268, p.114949, Article 114949

2020

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Toward Transparent Load Disaggregation-A Framework for Quantitative Evaluation of Explainability Using Explainable AI

IEEE transactions on consumer electronics, 2024-02, Vol.70 (1), p.4345-4356

2024

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Human in the loop active learning for time-series electrical measurement data

Engineering applications of artificial intelligence, 2024-07, Vol.133, p.108589, Article 108589

2024

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Deep-Based Conditional Probability Density Function Forecasting of Residential Loads

IEEE transactions on smart grid, 2020-07, Vol.11 (4), p.3646-3657

2020

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An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation

Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing

Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring

Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation

Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation

Synthetic Data Generation for Deep Learning-Based Inversion for Velocity Model Building

Human Activity Recognition of Individuals with Lower Limb Amputation in Free-Living Conditions: A Pilot Study

Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability

A Machine Learning-Driven Approach to Uncover the Influencing Factors Resulting in Soil Mass Displacement

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Smart Dimmable LED Lighting Systems

Low-complexity energy disaggregation using appliance load modelling

Author Correction: The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

Undirected Graphs: Is the Shift-Enabled Condition Trivial or Necessary?

Non-Intrusive Load Disaggregation Using Graph Signal Processing

An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem

Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

Human Upper Limb Motion Analysis for Post-Stroke Impairment Assessment Using Video Analytics

Non-intrusive load disaggregation solutions for very low-rate smart meter data

Toward Transparent Load Disaggregation-A Framework for Quantitative Evaluation of Explainability Using Explainable AI

Human in the loop active learning for time-series electrical measurement data

Deep-Based Conditional Probability Density Function Forecasting of Residential Loads

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