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Recent Advances in Intelligent MEMS Sensors
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Recent Advances in Intelligent MEMS Sensors

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 1848

Special Issue Editor

Prof. Dr. Changhui Jiang

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: multi-sensor navigation; under water remote sensing; underwater LiDAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the development of the Internet of Things, intelligent manufacturing, digital twins, autonomous driving and other fields has required urgent advances in intelligent MEMS sensor technology. Classical MEMS sensors can only output the measured signal, and cannot verify and diagnose the quality and authenticity of the measured signal by themselves; they also predict future development trends with difficulty. Intelligent MEMS sensors are the product of a fusion between sensor technology and artificial intelligence, and are capable of performing information collection, processing, and storage functions. Moreover, they are able to think logically, make judgments and determine corresponding treatments for special situations.

In addition to the ability of MEMS sensors to self-compensate the environmental temperature and intelligently reduce and filter noise, intelligent technologies such as fault self-diagnosis, detection range self-adjustment, sensitivity self-calibration, and zero-point self-adjustment are all key methods with which to improve the measurement accuracy and reliability of MEMS sensors. Meanwhile, the health management, array, integration, and signal processing of sensors are also key technologies that support intelligent MEMS sensors. This Special Issue aims to provide researchers with a platform for academic exchange in order to conduct in-depth discussions on intelligent MEMS sensors, seek high-quality work, and gain insights into the latest research results, including, but not limited to, the following topics:

  • MEMS sensor fault diagnosis and prediction technology;
  • MEMS sensor self-calibration method;
  • MEMS sensor residual life prediction technology;
  • MEMS detection method for abnormal values in sensor signals;
  • MEMS self-compensation technology for sensor environmental interference;
  • MEMS sensor health management;
  • MEMS multi-sensor integration and fusion technology;
  • MEMS array sensor signal processing technology;
  • New technologies for manufacturing the technology of MEMS sensors;
  • New effects and principles of MEMS sensors.

Prof. Dr. Changhui Jiang
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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (1 paper)

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Research

15 pages, 3273 KiB  
Article
Gaussian-Linearized Transformer with Tranquilized Time-Series Decomposition Methods for Fault Diagnosis and Forecasting of Methane Gas Sensor Arrays
by Kai Zhang, Wangze Ning, Yudi Zhu, Zhuoheng Li, Tao Wang, Wenkai Jiang, Min Zeng and Zhi Yang
Appl. Sci. 2024, 14(1), 218; https://doi.org/10.3390/app14010218 - 26 Dec 2023
Viewed by 1115
Abstract
Methane is considered as a clean energy that is widely used in places with high environmental requirements. The increasing demand for methane exploration in polar and deep sea extreme environments has a positive role in carbon neutrality policies. As a result, there will [...] Read more.
Methane is considered as a clean energy that is widely used in places with high environmental requirements. The increasing demand for methane exploration in polar and deep sea extreme environments has a positive role in carbon neutrality policies. As a result, there will be a gradual increase in exploration activities for deep sea methane resources. Methane sensors require high reliability but are prone to faults, so fault diagnosis and forecasting of gas sensors are of vital practical significance. In this work, a Gaussian-linearized transformer model with a tranquilized time-series decomposition method is proposed for fault diagnosis and forecasting tasks. Since the traditional transformer model requires more computational expense with time complexity of O (N2) and is not applicable to continuous-sequence prediction tasks, two blocks of the transformer are improved. First, a Gaussian-linearized attention block is modified for fault-diagnosis tasks so that its time complexity can be changed to O (N), which can reduce computational resources. Second, a model with proposed attention for fault forecasting replaces the traditional embedding block with a decomposed block, which can input the continuous sequence data to the model completely and preserve the continuity of the methane data. Results show that the Gaussian-linearized transformer improves the accuracy of fault diagnosis to 99% and forecasting with low computational cost, which is superior to that of traditional methods. Moreover, the least mean-square-error loss of fault forecasting is 0.04, which is lower compared with the traditional time series prediction models and other deep learning models, highlighting the great potential of the proposed transformer for fault diagnosis and fault forecasting of gas sensor arrays. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent MEMS Sensors)
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