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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

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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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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost.

Haizhao Lu1, Lieping Zhang1, Hongyuan Chen1

  • 1College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

This study introduces an improved indoor positioning algorithm using weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost). The new method significantly enhances accuracy and stability compared to traditional approaches.

Keywords:
WKNNWiFi fingerprintXGBoostgenetic algorithmindoor localization

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Machine Learning

Background:

  • Traditional indoor positioning systems often suffer from low accuracy and poor stability.
  • Existing machine learning algorithms face challenges in precise indoor localization.

Purpose of the Study:

  • To develop a novel indoor fingerprint positioning algorithm with enhanced accuracy and stability.
  • To improve upon traditional machine learning methods for indoor positioning.

Main Methods:

  • Implemented a hybrid approach combining weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost).
  • Utilized Gaussian filtering to remove outliers and enhance data reliability.
  • Employed a genetic algorithm (GA) for dynamic parameter optimization of XGBoost.
  • Integrated WKNN nearest neighbor sets into the XGBoost model for weighted fusion.

Main Results:

  • Achieved an average positioning error of 1.22 meters.
  • Demonstrated a 20.26-45.58% reduction in positioning error compared to traditional algorithms.
  • Showcased faster convergence in the cumulative distribution function (CDF) curve, indicating superior performance.

Conclusions:

  • The proposed WKNN-XGBoost algorithm offers significantly improved indoor positioning accuracy and stability.
  • This hybrid approach effectively addresses limitations of traditional machine learning methods in indoor environments.
  • The optimized algorithm provides a reliable and efficient solution for precise indoor localization.