Data from: Application of piezoelectric intelligent materials in pipa adaptive tuning system and its influence on performance stability
Data files
Dec 11, 2025 version files 47.06 KB
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NFIS_training_data_2000_rows.csv
45.68 KB
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README.md
1.38 KB
Abstract
A dataset of 2,000 samples was collected, including voltage (0.1–5 V), temperature (10–40°C), and humidity (20–90% RH) values, along with corresponding output adjustments. The NFIS utilised Gaussian membership functions to categorise sensor inputs into linguistic terms (e.g., “High Voltage,” “Medium Temperature”), and a comprehensive rule base of 40 rules was established for adaptive tuning. Training of the NFIS was conducted using gradient-descent backpropagation with a learning rate of 0.01 and L2 regularisation, validated through 5-fold cross-validation. Real-time performance data was transmitted via an ESP32 microcontroller to an AWS IoT Core database, with user adjustments and data visualisation provided through a mobile application.
https://doi.org/10.5061/dryad.12jm63z87
Description of the data and file structure
A dataset of 2,000 samples was collected, including voltage (0.1–5 V), temperature (10–40 °C), and humidity (20–90 % RH) values, along with corresponding output adjustments. The NFIS utilised Gaussian membership functions to categorise sensor inputs into linguistic terms (e.g., “High Voltage,” “Medium Temperature”), and a comprehensive rule base of 40 rules was established for adaptive tuning. Training of the NFIS was conducted using gradient-descent backpropagation with a learning rate of 0.01 and L2 regularisation, validated through 5-fold cross-validation. Real-time performance data was transmitted via an ESP32 microcontroller to an AWS IoT Core database, with user adjustments and data visualisation provided through a mobile application.
Code/software
Python, all code available on github: https://github.com/Zikou80/Pipa_Tuning_Stability
Access information
Other publicly accessible locations of the data:
Materials used in the study included; Lead Zirconate Titanate (PZT) Sensors (Model PZT-5H, APC International, USA); Light Aluminum Non-Invasive Adjustable Attachment Brackets (Misumi Corporation, Japan); Velcro Straps (Velcro USA Inc., USA); Shielded Flexible Cables (Alpha Wire, USA); Cable Ties or Clips (Panduit Corporation, USA); DS18B20 Digital Temperature Sensors (Maxim Integrated, USA); Torque Wrench (Tohnichi, Japan); 24-bit ADC Module (Model ADS1256, Texas Instruments, USA); Butterworth Low-Pass Filter (Custom component, configured with parts from Texas Instruments, USA); High-Pass Filter (Custom component, configured with parts from Texas Instruments, USA); 0.1 µF Ceramic Capacitors (Murata Manufacturing Co., Ltd., Japan); 10 µF Electrolytic Capacitors (Nichicon Corporation, Japan)
A dataset of 2,000 samples was collected, including voltage (0.1–5 V), temperature (10–40°C), and humidity (20–90% RH) values, along with corresponding output adjustments. The NFIS utilised Gaussian membership functions to categorise sensor inputs into linguistic terms (e.g., “High Voltage,” “Medium Temperature”), and a comprehensive rule base of 40 rules was established for adaptive tuning. Training of the NFIS was conducted using gradient-descent backpropagation with a learning rate of 0.01 and L2 regularisation, validated through 5-fold cross-validation. Real-time performance data was transmitted via an ESP32 microcontroller to an AWS IoT Core database, with user adjustments and data visualisation provided through a mobile application.