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The Intelligent Design of Structure Dynamics and Sensors
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The Intelligent Design of Structure Dynamics and Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 2844

Special Issue Editor

Dr. Kai Yang

E-Mail Website
Guest Editor
School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: vibro-acoustics energy harvesting and control; nonlinear dynamics; machine learning in control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of advanced technologies such as artificial intelligence, the Internet of Things (IoT) and other intelligent systems have become indispensable components of modern industrial systems. Technologies surrounding the structural design, data processing, and sensor design of IoT and intelligent systems have gradually transitioned from traditional design methods, such as structural dynamics and data processing, to design methods that rely on advanced AI algorithms like machine learning and reinforcement learning. Consequently, advanced technologies such as AI-based structural optimization, sensor design, and data processing will be crucial research directions in the future design of IoT and intelligent systems, possessing significant research value.

This Special Issue aims to collect and report the latest and highest quality scientific research advancements in these fields. This Special Issue primarily features papers that include, but are not limited to, the following topics: research on AI algorithms based on structural dynamics design or optimization, research on advanced AI data processing algorithms, research on advanced sensor designs based on AI, and so on. The following topics are also included:

  • Internet of Things;
  • Smart sensors;
  • Sensors for industry;
  • AI algorithms for sensors;
  • Intelligent systems;
  • Sensing stability;
  • Vibration detection;
  • Vibration control.

Dr. Kai Yang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • smart sensors
  • sensors for industry
  • AI algorithms for sensors
  • intelligent systems
  • sensing stability
  • vibration detection
  • vibration control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 4319 KiB  
Article
A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates
by Muhammad Haris Yazdani, Muhammad Muzammil Azad, Salman Khalid and Heung Soo Kim
Sensors 2025, 25(3), 826; https://doi.org/10.3390/s25030826 - 30 Jan 2025
Viewed by 1127
Abstract
Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature [...] Read more.
Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures. Full article
(This article belongs to the Special Issue The Intelligent Design of Structure Dynamics and Sensors)
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11 pages, 3986 KiB  
Communication
The Design and Validation of a High-Precision Angular Vibration Calibration System Based on a Laser Vibrometer
by Xinghan Lin, Zhigang Huang, Keyou Guo and Gang Li
Sensors 2024, 24(19), 6228; https://doi.org/10.3390/s24196228 - 26 Sep 2024
Viewed by 1059
Abstract
This paper presents the design and validation of a high-precision angular vibration calibration system based on a laser vibrometer, aimed at meeting the high-precision requirements for measuring small angular vibrations. The system primarily consists of a self-driving angular vibration platform and a laser [...] Read more.
This paper presents the design and validation of a high-precision angular vibration calibration system based on a laser vibrometer, aimed at meeting the high-precision requirements for measuring small angular vibrations. The system primarily consists of a self-driving angular vibration platform and a laser vibrometer. The platform is isolated from ground interference via an air-floating platform and uses a split-type motor to control the platform, generating specific angular vibrations. Detailed simulations of the platform’s modal characteristics and the stability of the spring plates were conducted using the finite element analysis software ANSYS 11. Moreover, fundamental frequency testing and measurement accuracy testing were conducted on the system. Experimental results demonstrate that the system has a fundamental frequency of 2.69 Hz and a maximum measurement error of 0.00172″, confirming the system’s effectiveness in dynamic characteristics, stability, and measurement accuracy. This research provides essential technical support for high-precision angular vibration control in spacecraft. Full article
(This article belongs to the Special Issue The Intelligent Design of Structure Dynamics and Sensors)
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