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Machine Learning, Big Data and Artificial Intelligence Enabled Sensing Systems
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Machine Learning, Big Data and Artificial Intelligence Enabled Sensing Systems

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

Deadline for manuscript submissions: closed (1 September 2023) | Viewed by 7677

Special Issue Editors

Dr. Wen Shen

E-Mail Website
Guest Editor
Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA
Interests: biosensors; electrochemical sensors; wearable sensors; implantable sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Jessica Koehne

E-Mail Website
Guest Editor
National Aeronautics and Space Administration, Washington, DC, USA
Interests: implantable and wearable sensors
Dr. Ye Sun

E-Mail Website
Guest Editor
Mechanical and Aerospace Engineering, Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA
Interests: wearable sensors; smart and connected health; machine learning
Dr. Dazhong Wu

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Central Florida, 12760 Pegasus Drive, Orlando, FL 32816, USA
Interests: smart manufacturing; additive manufacturing; engineering design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern sensing technologies have a wide range of applications in various industries, including agriculture, medicine, medical devices, manufacturing, aerospace, automobiles, and infrastructure. Over the past few years, machine learning, big data, and artificial intelligence have increasingly been applied to analyze sensor-generated data for the development of intelligent sensors. This Special Issue will focus on novel industrial sensors, biosensors, wearable sensors, and wireless sensors, as well as machine-learning- and artificial-intelligence-enabled sensing systems. Topics of interest include, but are not limited to:

  • Machine-learning-guided biosensing;
  • Data-driven manufacturing process monitoring;
  • Artificial-intelligence-assisted structural health monitoring;
  • Biosensors and wearable sensors for smart healthcare applications;
  • Wireless and embedded sensors for real-time condition monitoring.

Dr. Wen Shen
Dr. Jessica Koehne
Dr. Ye Sun
Dr. Dazhong Wu
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

  • industrial sensors
  • biosensors
  • wearable sensors
  • wireless sensors
  • machine learning
  • artificial intelligence
  • big data

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

Published Papers (3 papers)

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Research

18 pages, 6010 KiB  
Article
Control Method of Cold and Hot Shock Test of Sensors in Medium
by Jinming Tian, Yue Zeng, Linhai Ji, Huimin Zhu and Zu Guo
Sensors 2023, 23(14), 6536; https://doi.org/10.3390/s23146536 - 20 Jul 2023
Cited by 2 | Viewed by 1706
Abstract
In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of [...] Read more.
In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and −40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test. Full article
(This article belongs to the Special Issue Machine Learning, Big Data and Artificial Intelligence Enabled Sensing Systems)
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30 pages, 1474 KiB  
Article
Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach
by Nour Mostafa, Yehia Kotb, Zakwan Al-Arnaout, Samer Alabed and Ahmed Younes Shdefat
Sensors 2023, 23(10), 4639; https://doi.org/10.3390/s23104639 - 10 May 2023
Cited by 1 | Viewed by 2123
Abstract
The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when [...] Read more.
The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid. Full article
(This article belongs to the Special Issue Machine Learning, Big Data and Artificial Intelligence Enabled Sensing Systems)
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14 pages, 1983 KiB  
Article
Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model
by Yupeng Wei
Sensors 2023, 23(5), 2587; https://doi.org/10.3390/s23052587 - 26 Feb 2023
Cited by 6 | Viewed by 3030
Abstract
State-of-health (SOH) is a measure of a battery’s capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant [...] Read more.
State-of-health (SOH) is a measure of a battery’s capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery’s health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery’s degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery. Full article
(This article belongs to the Special Issue Machine Learning, Big Data and Artificial Intelligence Enabled Sensing Systems)
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