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Selected Papers from the 2021 IEEE International Workshop on Metrology...
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Selected Papers from the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT

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

Deadline for manuscript submissions: closed (10 September 2021) | Viewed by 12687

Special Issue Editors

Dr. Carlo Massaroni

E-Mail Website
Guest Editor
Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
Interests: wearable sensors; sensors; physiological monitoring; algorithms for data processing including machine learning; applications of sensors in clinical, occupational, and sports fields
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Ferrari

E-Mail Website
Guest Editor
UNIBS-DIE, Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
Interests: sensor network; distributed measurement systems; industrial communication; real-time ethernet; clock synchronization; industrial IoT; industrial security; wireless sensors; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Bellitti

E-Mail Website
Guest Editor
Department of Information Engineering, University of Brescia, 25126 Brescia, Italy

Special Issue Information

Dear Colleagues,

The 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT (http://www.metroind40iot.org/home) will be held in Rome, Italy, on 7–9 June 2021.

The authors of papers presented at the workshop related to Sensors are invited to submit extended versions of their work to this Special Issue for publication.

MetroInd4.0&IoT aims to discuss the contributions of metrology for the development of Industry 4.0 and IoT and the new opportunities offered by Industry 4.0 and IoT for the development of new measurement methods and apparatuses. We aim to provide a platform to gather people who work in the development of instrumentation and measurement methods for Industry 4.0 and IoT. Particular areas of focus include, but are not limited to, new technology for metrology-assisted production in Industry 4.0 and IoT, Industry 4.0 and IoT component measurements, sensors and associated signal conditioning for Industry 4.0 and IoT, and calibration methods for electronic tests and measurement for Industry 4.0 and IoT.

Topics:

  • Industrial sensors;
  • Virtual sensors and sensor interfacing;
  • IoT-enabled sensors and measurement systems;
  • Measurement applications based on IoT;
  • Industrial IoT, Factory of Things, and Internet of Things;
  • Wireless sensor networks and IoT;
  • Wearables and body sensor networks;
  • Sensor data management;
  • Localization technologies.

Dr. Carlo Massaroni
Prof. Dr. Paolo Ferrari
Dr. Paolo Bellitti
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.

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Published Papers (4 papers)

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Research

19 pages, 6249 KiB  
Article
In-Process Error-Matching Measurement and Compensation Method for Complex Mating
by Shih-Ming Wang, Ren-Qi Tu and Hariyanto Gunawan
Sensors 2021, 21(22), 7660; https://doi.org/10.3390/s21227660 - 18 Nov 2021
Cited by 2 | Viewed by 2077
Abstract
This study proposed an error-matching measurement and compensation method for curve mating and complex mating. With use of polynomial curve fitting and least squares methods for error analysis, an algorithm for error identification and error compensation were proposed. Furthermore, based on the proposed [...] Read more.
This study proposed an error-matching measurement and compensation method for curve mating and complex mating. With use of polynomial curve fitting and least squares methods for error analysis, an algorithm for error identification and error compensation were proposed. Furthermore, based on the proposed method, an online error-matching compensation system with an autorevising function module for autogenerating an error-compensated NC program for machining was built. Experimental verification results showed that the proposed method can effectively improve the accuracy of assembly matching. In a curve-type mating experiment, the matching error without compensation was 0.116 mm, and it decreased to 0.048 mm after compensation. The assembly accuracy was improved by 28%. In a complex-type mating experiment, the verification results showed that the error reductions after compensation for three mating shapes (straight line, triangle, and curve shape) were 81%, 87%, and 79%, respectively. It showed that the proposed method can improve the assembly accuracy for complex mating shapes, which would also be improved without losing production efficiency. Full article
(This article belongs to the Special Issue Selected Papers from the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT)
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18 pages, 5459 KiB  
Article
Validation and Assessment of a Posture Measurement System with Magneto-Inertial Measurement Units
by Davide Paloschi, Marco Bravi, Emiliano Schena, Sandra Miccinilli, Michelangelo Morrone, Silvia Sterzi, Paola Saccomandi and Carlo Massaroni
Sensors 2021, 21(19), 6610; https://doi.org/10.3390/s21196610 - 3 Oct 2021
Cited by 15 | Viewed by 3935
Abstract
Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would [...] Read more.
Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would be highly beneficial for accurate posture monitoring. This work proposes a system based on three magneto-inertial measurement units (MIMU), placed on the backs of seventeen volunteers on the T3, T12 and S1 vertebrae. The reference system used for validation is a stereophotogrammetric motion capture system. The volunteers performed forward bending and sit-to-stand tests. The measured variables for identifying the posture were the kyphosis and the lordosis angles, as well as the range of movement (ROM) of the body segments. The comparison between MIMU and MoCap provided a maximum RMSE of 5.6° for the kyphosis and the lordosis angles. The average lumbo-pelvic contribution during forward bending (41.8 ± 8.6%) and the average lumbar ROM during sit-to-stand (31.8 ± 9.8° for sitting down, 29.6 ± 7.6° for standing up) obtained with the MIMU system agree with the literature. In conclusion, the MIMU system, which is wearable, inexpensive and easy to set up in non-structured environments, has been demonstrated to be effective in posture evaluation. Full article
(This article belongs to the Special Issue Selected Papers from the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT)
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16 pages, 6390 KiB  
Article
Soft System Based on Fiber Bragg Grating Sensor for Loss of Resistance Detection during Epidural Procedures: In Silico and In Vivo Assessment
by Francesca De Tommasi, Daniela Lo Presti, Francesca Virgili, Carlo Massaroni, Emiliano Schena and Massimiliano Carassiti
Sensors 2021, 21(16), 5329; https://doi.org/10.3390/s21165329 - 6 Aug 2021
Cited by 18 | Viewed by 2629
Abstract
Epidural analgesia represents a clinical common practice aiming at pain mitigation. This loco-regional technique is widely used in several applications such as labor, surgery and lower back pain. It involves the injections of anesthetics or analgesics into the epidural space (ES). The ES [...] Read more.
Epidural analgesia represents a clinical common practice aiming at pain mitigation. This loco-regional technique is widely used in several applications such as labor, surgery and lower back pain. It involves the injections of anesthetics or analgesics into the epidural space (ES). The ES detection is still demanding and is usually performed by the techniques named loss of resistance (LOR). In this study, we propose a novel soft system (SS) based on one fiber Bragg grating sensor (FBG) embedded in a soft polymeric matrix for LOR detection during the epidural puncture. The SS was designed to allow instrumenting the syringe’s plunger without relevant modifications of the anesthetist’s sensations during the procedure. After the metrological characterization of the SS, we assessed the capability of this solution in detecting LOR by carrying it out in silico and in clinical settings. For both trials, results revealed the capability of the proposed solutions in detecting the LOR and then in recording the force exerted on the plunger. Full article
(This article belongs to the Special Issue Selected Papers from the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT)
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26 pages, 9398 KiB  
Article
The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process
by Anton Vladimirovich Milov, Vadim Sergeevich Tynchenko, Sergei Olegovich Kurashkin, Valeriya Valerievna Tynchenko, Vladislav Viktorovich Kukartsev, Vladimir Viktorovich Bukhtoyarov, Roman Sergienko, Viktor Alekseevich Kukartsev and Kirill Aleksandrovich Bashmur
Sensors 2021, 21(12), 4199; https://doi.org/10.3390/s21124199 - 18 Jun 2021
Cited by 3 | Viewed by 2128
Abstract
In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating [...] Read more.
In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating conditions in terms of the accuracy of the data obtained and the reliability of the measurement equipment and duplication in case of a failure. The present article discusses the development of intelligent technology based on a collection of artificial neural networks, which allows a number of problems associated with technological process control when using pyrometric sensors to be solved: assessing the quality of measurements, correcting measurements when non-standard errors are detected, and controlling the process of induction heating in the absence of reliable readings of the measurement instruments. The collection of artificial neural networks is self-configuring with the use of multicriterion genetic algorithms. The use of the proposed intelligent technology made it possible to improve the control quality of the technological process of the induction brazing of waveguide paths of spacecraft: the overregulation was decreased from 0–20 to 0, and the difference in the heating temperatures of the elements of the brazed waveguide assembly was decreased from 20–100 to 0–10. In addition, the overall process duration decreased and became more stable. When using the classical control technology, the time varied in the range of 20–60 s; when using the proposed technology, it stabilized in the range of 30–35 s. Full article
(This article belongs to the Special Issue Selected Papers from the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT)
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