ArticleSensors (Basel, Switzerland)2022
Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- AI Methods in Sensor Calibration.Sensors (Basel, Switzerland) · 2026Review
- Neural Network Methods in the Development of MEMS Sensors.Micromachines · 2024Review
- Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion.Sensors (Basel, Switzerland) · 2023Article
- Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10
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Registered trials
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