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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Few-Shot Learning for Image-Based Nonintrusive Appliance Signal Recognition.

L Matindife1, Y Sun1, Z Wang2

  • 1Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park 2006, South Africa.

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Summary
This summary is machine-generated.

This study demonstrates effective appliance recognition using a reduced dataset and computer vision deep learning. Few-shot learning shows promise for nonintrusive load monitoring with minimal data.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Energy Systems

Background:

  • Deep learning models for nonintrusive load monitoring (NILM) require extensive data, posing challenges in acquisition, storage, and computation.
  • Appliance signal data often suffers from imbalance due to varying activation periods.

Purpose of the Study:

  • To develop and evaluate a computer vision deep learning approach for recognizing disaggregated appliance signals using a reduced dataset.
  • To address the high data demands of deep learning in NILM applications.

Main Methods:

  • Utilized Siamese and prototypical few-shot classification algorithms on a reduced dataset.
  • Implemented a similarity test to ensure data quality before deep learning model input.
  • Focused on few-shot learning techniques to handle limited data samples.

Main Results:

  • Achieved acceptable performance in appliance signal recognition.
  • Demonstrated the viability of Siamese networks for one-shot recognition and prototypical networks for handling data imbalance.
  • Validated the effectiveness of the approach with limited data samples.

Conclusions:

  • Few-shot learning is a promising strategy for nonintrusive load monitoring with reduced datasets.
  • The developed computer vision approach offers an efficient alternative to traditional data-intensive deep learning methods in NILM.
  • This method can significantly lower the barriers to implementing NILM systems.