Artificial Intelligence and Intelligent Signal Processing in Precision Measurement

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 14 June 2026 | Viewed by 390

Special Issue Editors

School of Electrical Engineering, Guizhou University, Guiyang 550025, China
Interests: machine vision inspection; laser measurement; vibration measurement; motion control and testing
Institute of Microelectronics Chinese Academy of Sciences, Beijing, China
Interests: image analysis and pattern recognition; artificial intelligence

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence and intelligent signal processing technologies, significant progress has been made in precision measurement and instruments, and they have been widely applied in intelligent manufacturing, structural health monitoring, fault diagnosis, and intelligent medical fields.

Artificial intelligence and intelligent signal processing, as the core of intelligent detection instruments and equipment, through in-depth analysis and mining of data features, form data-driven intelligent detection methods and systems, which possess excellent accuracy and repeatability and demonstrate unprecedented advantages. This Special Issue addresses data differences in various engineering applications by highlighting theories and methods based on artificial intelligence and signal processing. A complete process system integrating data anomaly judgment, preprocessing, feature extraction and enhancement, model selection and construction, and training and validation is established, aiming to meet the intelligent and reliable detection requirements of different engineering applications.

This Special Issue focuses on the theories, methods and technologies of artificial intelligence and intelligent signal processing, as well as their applications in precision measurement and instruments. It provides a platform for the exchange of innovative ideas and intelligent methods and technologies across different disciplines and promotes the application of precision measurement and instruments in various fields.

Dr. Ming Yang
Dr. Ying Wang
Guest Editors

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Keywords

  • neural network
  • deep learning
  • machine learning
  • expert system
  • computer application
  • intelligent signal processing
  • sensing measurement
  • reliable metrology
  • intelligent instrumentation

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Published Papers (2 papers)

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Research

19 pages, 810 KB  
Article
An Improved NSGA-II Based Multi-Objective Optimization Model for Electric Vehicle Charging Station Selection
by Jingxuan Li, Hezhong Tang, Pengcheng Li, Zehao Li and Chengbin Liang
Mathematics 2025, 13(23), 3855; https://doi.org/10.3390/math13233855 (registering DOI) - 1 Dec 2025
Abstract
Facing the challenge of balancing electric vehicle (EV) user experience with distribution network security, this paper develops a multi-objective optimization model for charging station selection that simultaneously considers user-side costs and grid-side stability indicators, including voltage deviation and system power loss. To solve [...] Read more.
Facing the challenge of balancing electric vehicle (EV) user experience with distribution network security, this paper develops a multi-objective optimization model for charging station selection that simultaneously considers user-side costs and grid-side stability indicators, including voltage deviation and system power loss. To solve this complex problem, an improved NSGA-II algorithm with enhanced constraint handling is introduced. Case studies on the IEEE 33-bus system demonstrate that the proposed approach effectively limits maximum voltage deviation to 2% with only a minimal 3% increase in user cost and reduces network losses by 12%. This achieves an optimal balance between user satisfaction and grid security, providing quantitative support for coordinated charging management and infrastructure planning. Full article
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15 pages, 2868 KB  
Article
An IPNN-Based Parameter Identification Method for a Vibration Sensor Sensitivity Model
by Honglong Li, Zhihua Liu, Chenguang Cai, Kemin Yao, Jun Pan and Ming Yang
Mathematics 2025, 13(22), 3609; https://doi.org/10.3390/math13223609 - 11 Nov 2025
Viewed by 281
Abstract
Vibration sensors, as critical components in motion control and measurement systems, have dynamic characteristics that directly affect measurement accuracy. However, existing sensitivity models, due to structural simplifications and parameter uncertainties, hinder conventional vibration and shock calibration methods from fully characterizing their dynamic performance. [...] Read more.
Vibration sensors, as critical components in motion control and measurement systems, have dynamic characteristics that directly affect measurement accuracy. However, existing sensitivity models, due to structural simplifications and parameter uncertainties, hinder conventional vibration and shock calibration methods from fully characterizing their dynamic performance. In addition, traditional parameter identification approaches are often noise-sensitive and lack interpretability, making them inadequate for high-precision applications. To address these challenges, this study proposes an Algorithm-Unrolled Interpretable Physics-Informed Neural Network (IPNN) for parameter identification of a vibration sensor sensitivity model. By integrating the physical characteristics of the sensors with vibration calibration data, the method enables high-precision parameter identification and interpretable dynamic modeling. Comparative experimental results show that the proposed IPNN reduces the RMSE of sensor voltage predictions by over 60% compared with GRU and LSTM and decreases the average full-frequency relative deviation from laser interferometry calibration results by approximately 65% relative to LSM. Full article
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