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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Optimized LightGBM Power Fingerprint Identification Based on Entropy Features.

Lin Lin1, Jie Zhang1, Na Zhang2

  • 1College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China.

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Summary
This summary is machine-generated.

This study introduces an optimized LightGBM method for power fingerprint identification, addressing data imbalance and transmission issues in IoT. The approach enhances recognition accuracy and efficiency for large-scale systems.

Keywords:
Boruta algorithmLightGBMOptuna algorithmentropy featurepower fingerprint

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Massive power fingerprint data presents challenges with unbalanced categories and limited data transmission rates for Internet of Things (IoT) communications.
  • Efficient extraction and identification of power fingerprints are crucial for large-scale IoT systems.

Purpose of the Study:

  • To propose an optimized LightGBM (Light Gradient Boosting Machine) method for power fingerprint extraction and identification.
  • To address data imbalance and reduce data transmission volume in IoT environments.
  • To improve the accuracy and efficiency of power fingerprint recognition.

Main Methods:

  • Extracted voltage and current signals using time-domain and V-I trajectory features, constructing a 56-dimensional feature set with six entropy features.
  • Employed the Boruta algorithm with LightGBM for feature selection, identifying a 23-dimensional optimal feature subset with five entropy features.
  • Utilized the Optuna algorithm to optimize LightGBM hyperparameters and improve the loss function for imbalanced datasets.

Main Results:

  • Successfully reduced the computational complexity of feature extraction.
  • Significantly decreased the amount of power fingerprint data transmission.
  • Achieved high recognition accuracy and efficiency, meeting the demands of massive power fingerprint identification systems.

Conclusions:

  • The proposed optimized LightGBM method effectively handles imbalanced datasets and reduces data transmission in power fingerprint identification.
  • The method demonstrates superior performance in terms of accuracy and efficiency for large-scale IoT applications.
  • This approach provides a viable solution for practical power fingerprint recognition systems with limited resources.