DOI: 10.5061/dryad.12jm63z87
title: Data from: Application of piezoelectric intelligent materials in pipa adaptive tuning system and its influence on performance stability

description: 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.
description: 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.
description: # Data from: Application of piezoelectric intelligent materials in pipa
 adaptive tuning system and its influence on performance stability
 [https://doi.org/10.5061/dryad.12jm63z87](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](https://github.com/Zikou80/Pipa_Tuning_Stability) ## Access information Other publicly accessible locations of the data: * Github: [https://github.com/Zikou80/Pipa_Tuning_Stability](https://github.com/Zikou80/Pipa_Tuning_Stability)