Automated Classification of Moisture Content in Wood Chips Using UWB Radio Transmission Signals

Accurately identifying moisture content in wood chips is crucial for optimizing energy production. This paper presents an automated classification approach using ultrawideband (UWB) radio transmission data. We collect data from 1,923 samples across four power plants and extract seven key features based on zero-crossings and amplitude information. To enhance classification performance, we apply Chi-square feature selection to identify the most significant features. These selected features are then fed into a classifier to determine moisture content levels. Our approach achieves a classification accuracy of 85.26%, demonstrating the effectiveness of the extracted features and the proposed methodology.

• Publication year

  2025

• Venue

  CSI International Symposium on Artificial Intelligence and Signal Processing

• Publication date

  2025-11-22

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/AISP68263.2025.11396133
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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