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Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2581

Special Issue Editors

Dr. Yipeng Wu

E-Mail Website
Guest Editor
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: piezoelectric sensors; self-powered sensors; ambient energy harvesting; sensing technology
Special Issues, Collections and Topics in MDPI journals
Dr. Lihang Feng

E-Mail Website
Guest Editor
College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Interests: computational sensing; instrumentation and measurement; robotics sensing and perception; AI-enabled sensing technology
Dr. Mingpeng Yang

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China
Interests: wearable sensors; MEMS; microfluidics; sensors of bio-chemical analyte

Special Issue Information

Dear Colleagues,

As high-end equipment structures are becoming lightweight, intelligent and multifunctional, it is necessary to use various sensors and acquire ambient information, provide endless data and power the equipment. The objective of this Special Issue is to provide wide coverage of research on the latest advances in sensors and/or sensing technologies in the field of high-end equipment and structures. The scope of this Special Issue includes but is not limited to the following:

  • Novel sensor design, calibration and data processing;
  • Sensor performance and reliability analysis in high-end equipment;
  • IoT sensor networks and multi-functional sensing techniques;
  • Computational sensing and perception in robotic systems;
  • AI-enabled sensing technology in mechatronics systems;
  • Other sensors techniques in high-end equipment structures.

Dr. Yipeng Wu
Dr. Lihang Feng
Dr. Mingpeng Yang
Guest Editors

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

  • novel sensors
  • sensing technology
  • IoT sensor networks
  • self-powered sensor
  • high-end equipment structure

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Published Papers (4 papers)

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Research

17 pages, 6004 KiB  
Article
Autoencoder-Based System for Detecting Anomalies in Pelletizer Melt Processes
by Mingxiang Zhu, Guangming Zhang, Lihang Feng, Xingjian Li and Xiaodong Lv
Sensors 2024, 24(22), 7277; https://doi.org/10.3390/s24227277 - 14 Nov 2024
Viewed by 331
Abstract
Effectively identifying and preventing anomalies in the melt process significantly enhances production efficiency and product quality in industrial manufacturing. Consequently, this paper proposes a study on a melt anomaly identification system for pelletizers using autoencoder technology. It discusses the challenges of detecting anomalies [...] Read more.
Effectively identifying and preventing anomalies in the melt process significantly enhances production efficiency and product quality in industrial manufacturing. Consequently, this paper proposes a study on a melt anomaly identification system for pelletizers using autoencoder technology. It discusses the challenges of detecting anomalies in the melt extrusion process of polyester pelletizers, focusing on the limitations of manual monitoring and traditional image detection methods. This paper proposes a system based on autoencoders that demonstrates effectiveness in detecting and differentiating various melt anomaly states through deep learning. By randomly altering the brightness and rotation angle of images in each training round, the training samples are augmented, thereby enhancing the system’s robustness against changes in environmental light intensity. Experimental results indicate that the system proposed has good melt anomaly detection efficiency and generalization performance and has effectively differentiated degrees of melt anomalies. This study emphasizes the potential of autoencoders in industrial applications and suggests directions for future research. Full article
(This article belongs to the Special Issue Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure)
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16 pages, 1401 KiB  
Article
Efficient Music Genre Recognition Using ECAS-CNN: A Novel Channel-Aware Neural Network Architecture
by Yang Ding, Hongzheng Zhang, Wanmacairang Huang, Xiaoxiong Zhou and Zhihan Shi
Sensors 2024, 24(21), 7021; https://doi.org/10.3390/s24217021 - 31 Oct 2024
Viewed by 513
Abstract
In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an [...] Read more.
In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an adaptive channel attention mechanism (ECA module) within the convolutional layers, the network significantly improves the extraction of key musical features. Extensive experiments were conducted on the GTZAN dataset, comparing the proposed ECAS-CNN with traditional convolutional neural networks. The results demonstrate that ECAS-CNN outperforms conventional methods across various performance metrics, including accuracy, precision, recall, and F1-score, particularly in handling complex musical features. This study validates the potential of ECAS-CNN in the domain of music genre classification and offers new insights for future research and applications. Full article
(This article belongs to the Special Issue Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure)
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15 pages, 4974 KiB  
Article
High-Precision and Lightweight Model for Rapid Safety Helmet Detection
by Xuejun Jia, Xiaoxiong Zhou, Chunyi Su, Zhihan Shi, Xiaodong Lv, Chao Lu and Guangming Zhang
Sensors 2024, 24(21), 6985; https://doi.org/10.3390/s24216985 - 30 Oct 2024
Viewed by 448
Abstract
This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the [...] Read more.
This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the modified penalty-decay intersection over union (MPDIoU) loss function to achieve more stable and precise bounding box regression. Furthermore, considering the original YOLOv5s model’s large parameter count, we adopt a lightweight design using the MobileNetV3 architecture and replace the original squeeze-and-excitation (SE) attention mechanism with CBAM, significantly reducing computational complexity. These improvements reduce the model’s parameters from 15.7 GFLOPs to 5.7 GFLOPs while increasing the mean average precision (mAP) from 82.34% to 91.56%, demonstrating its superior performance and potential value in practical industrial applications. Full article
(This article belongs to the Special Issue Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure)
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15 pages, 1648 KiB  
Article
Multi-Directional Strain Measurement in Fiber-Reinforced Plastic Based on Birefringence of Embedded Fiber Bragg Grating
by Chunhua Zhou, Changhao Chen, Zilong Ye, Qi Wu and Ke Xiong
Sensors 2024, 24(19), 6190; https://doi.org/10.3390/s24196190 - 24 Sep 2024
Viewed by 805
Abstract
Embedded fiber Bragg gratings are increasingly applied for in-situ strain measurement in fiber-reinforced plastics, integral to high-end aerospace equipment. Existing research primarily focuses on in-plane strain measurement, limited by the fact that fiber Bragg gratings are mainly sensitive to axial strain. However, out-of-plane [...] Read more.
Embedded fiber Bragg gratings are increasingly applied for in-situ strain measurement in fiber-reinforced plastics, integral to high-end aerospace equipment. Existing research primarily focuses on in-plane strain measurement, limited by the fact that fiber Bragg gratings are mainly sensitive to axial strain. However, out-of-plane strain measurement is equally important for comprehending structural deformation. The birefringence of fiber Bragg gratings shows promise for addressing this problem; yet, the strain transfer relationship between composites and optical fibers, along with the decoupling method for multi-directional strains, remains inadequately explored. This study introduces an innovative method for multi-directional strain measurement in fiber-reinforced plastics using the birefringence of a single-fiber Bragg grating. The strain transfer relationship between composites and embedded optical fibers was derived based on Kollar’s analytical model, leading to the development of a multi-directional strain decoupling methodology. This method was experimentally validated on carbon fiber/polyetherimide laminates under thermo-mechanical loading. Its reliability was confirmed by comparing experimental results and finite element simulations. These findings significantly broaden the application scenarios of fiber Bragg gratings, advancing the in-situ measurement technology crucial for the next generation of high-end aerospace equipment. Full article
(This article belongs to the Special Issue Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure)
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