From Sensing Technology towards Digital Twin in Applications

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13157

Special Issue Editor

Special Issue Information

Dear Colleagues,

Sensing technology drives the motivation of innovation in digital technology, especially the technology in data acquisition, machine learning, digital twin, and sustainable materials-based electronics. With the aid of the new technologies in sensing, human–machine connection, data handling, machine learning, and digital twin in the industrial area, a general conclusion on these technologies needs to be concluded for researchers in further thinking. Therefore, sensing towards digital twin technology has been studied and integrated into many fields in extensive application in traditional industrial fields, such as automatic driving, smart city, medical care, intelligent robot, etc. 

This Special Issue is aimed at providing selected contributions on advances in physical sensing in machine learning, human–machine interface, data confusion, and various potential applications from sensors towards digital twin innovation. Potential topics include, but are not limited to:

  • Physical sensing technologies, such as wearable sensors, nano/microsensors;
  • Data confusion and machine learning;
  • Modeling and simulation technology of virtual space;
  • Human–computer interaction and collaboration between virtual models and physical entities;
  • Intelligent decision-making based on digital twin technology;
  • Human factors analysis and optimization related to a digital twin.

Special thanks to Dr. Wenyu Wu for his assistance and great support in this Special Issue.

Dr. Jianxiong Zhu
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

  • physical sensing
  • machine learning
  • data management
  • human–computer interaction and collaboration
  • dynamic evaluation
  • intelligent decision-making
  • visualization
  • digital twin
  • smart system

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Published Papers (9 papers)

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Editorial

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4 pages, 172 KiB  
Editorial
From Sensing Technology towards Digital Twin in Applications
by Jianxiong Zhu, Bairong Sun, Luyu Jia and Haibing Hu
Inventions 2024, 9(2), 43; https://doi.org/10.3390/inventions9020043 - 17 Apr 2024
Viewed by 230
Abstract
Sensing technology drives innovation in digital technology, especially in data acquisition [...] Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)

Research

Jump to: Editorial

30 pages, 34635 KiB  
Article
Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment
by Laurențiu-Florin Constantinoiu, António Tavares, Rui Miguel Cândido and Eugen Rusu
Inventions 2024, 9(1), 20; https://doi.org/10.3390/inventions9010020 - 05 Feb 2024
Cited by 1 | Viewed by 1418
Abstract
Shallow water bathymetry is a topic of significant interest in various fields, including civil construction, port monitoring, and military operations. This study presents several methods for assessing shallow water bathymetry using maritime uncrewed systems (MUSs) integrated with advanced and innovative sensors such as [...] Read more.
Shallow water bathymetry is a topic of significant interest in various fields, including civil construction, port monitoring, and military operations. This study presents several methods for assessing shallow water bathymetry using maritime uncrewed systems (MUSs) integrated with advanced and innovative sensors such as Light Detection and Ranging (LiDAR) and multibeam echosounder (MBES). Furthermore, this study comprehensively describes satellite-derived bathymetry (SDB) techniques within the same geographical area. Each technique is thoroughly outlined with respect to its implementation and resultant data, followed by an analytical comparison encompassing their accuracy, precision, rapidness, and operational efficiency. The accuracy and precision of the methods were evaluated using a bathymetric reference survey conducted with traditional means, prior to the MUS survey and with cross-comparisons between all the approaches. In each assessment of the survey methodologies, a comprehensive evaluation is conducted, explaining both the advantages and limitations for each approach, thereby enabling an inclusive understanding for the reader regarding the efficacy and applicability of these methods. The experiments were conducted as part of the Robotic Experimentation and Prototyping using Maritime Unmanned Systems 23 (REPMUS23) multinational exercise, which was part of the Rapid Environmental Assessment (REA) experimentations. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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9 pages, 3819 KiB  
Article
Design of a Novel Magnetic Induction Switch with a Permalloy Film and a Trans-Impedance Amplifier Circuit
by Shubin Zhang, Qi Jiang and Yanfeng Jiang
Inventions 2024, 9(1), 4; https://doi.org/10.3390/inventions9010004 - 27 Dec 2023
Viewed by 1199
Abstract
At present, magnetic induction switches are widely used in industrial automation control and biological sensing systems. A core module composed of a magnetic sensing device and a signal conditioning circuit is designed and analyzed in this paper. Utilizing a permalloy film with the [...] Read more.
At present, magnetic induction switches are widely used in industrial automation control and biological sensing systems. A core module composed of a magnetic sensing device and a signal conditioning circuit is designed and analyzed in this paper. Utilizing a permalloy film with the anisotropic magneto-resistance (AMR) effect, the novel magnetic induction switch shows its ability to correctly detect the direction of magnetic fields. Furthermore, an interfacial circuit based on a trans-impedance amplifier (TIA) is designed to measure and regulate the output signal of the sensing device. Accurate simulation results show the gain of the TIA reaches up to 51.36 dB with a bandwidth of 1.3 GHz and a power consumption of 3.65 mW. The outstanding performance of the proposed module demonstrates the possibility of solving the problems induced by high input impedance, high frequency, and parasitic effects in magnetic induction switches. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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11 pages, 2849 KiB  
Article
A Surface-Enhanced Raman Spectroscopic Sensor Pen
by Zejiang Song, Zhijie Li, Weishen Zhan, Wanli Zhao, Hsiang-Chen Chui and Rui Li
Inventions 2023, 8(6), 156; https://doi.org/10.3390/inventions8060156 - 12 Dec 2023
Viewed by 1271
Abstract
Surface-enhanced Raman spectroscopy (SERS) is widely used as a detection method in scientific research fields. However, the method for creating SERS substrates often requires expensive equipment and involves a complex process. Additionally, preserving and effectively utilizing SERS substrates in the long term poses [...] Read more.
Surface-enhanced Raman spectroscopy (SERS) is widely used as a detection method in scientific research fields. However, the method for creating SERS substrates often requires expensive equipment and involves a complex process. Additionally, preserving and effectively utilizing SERS substrates in the long term poses a challenging problem. In order to address these issues, we propose a new method for creating SERS substrates on various types of paper using a combination of a ballpoint pen and 3D printing. This method ensures a high enhancement factor and maximizes the utilization of the substrate. We achieved an enhancement factor of up to 8.2 × 108 for detecting R6G molecules, with a relative standard deviation of 11.13% for the Raman peak at 612 cm−1 of R6G, demonstrating excellent SERS sensitivity and spectral reproducibility. Furthermore, we successfully detected thiram at a concentration as low as 10−8, which is lower than both the Chinese national standard and European standard. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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28 pages, 9646 KiB  
Article
Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach
by Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa and Jang-Wook Hur
Inventions 2023, 8(6), 140; https://doi.org/10.3390/inventions8060140 - 02 Nov 2023
Cited by 3 | Viewed by 1559
Abstract
In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction. The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. To [...] Read more.
In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction. The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (AE-LSTM) model, to ensure that all important characteristics of the system are utilized. Fast independent component analysis (FastICA) is employed to fuse the data signals from both sensors while retaining their characteristics. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the AE-LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. The study used robust global metric evaluation techniques, such as accuracy, F1-score, and confusion metrics, to thoroughly evaluate the model’s dependability and efficiency. LSTM was additionally employed for anomaly detection to further emphasize the adaptability and interoperability of the methodology. This modification yielded a remarkable accuracy of 89.67%, offering additional validation of the model’s reliability and competence. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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15 pages, 5351 KiB  
Article
K-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence
by Brijesh Patel, Zih Fong Huang, Chih-Ho Yeh, Yen-Ru Shih and Po Ting Lin
Inventions 2023, 8(6), 132; https://doi.org/10.3390/inventions8060132 - 24 Oct 2023
Viewed by 1486
Abstract
This paper describes a non-destructive detection method for identifying cable defects using K-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, K-mer frequency encoding, [...] Read more.
This paper describes a non-destructive detection method for identifying cable defects using K-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, K-mer frequency encoding, and artificial intelligence-based identification. A magnetic leakage detection device detects signals via sensors and records their corresponding positions to obtain cable signals. The K-mer frequency encoding method consists of several steps, including cable signal normalization, the establishment of K-mer frequency encoding, repeated sampling of cable signals, and conversion for comparison to derive the K-mer frequency. The K-mer frequency coding method has advantages in data processing and repeated sampling. In the identification step of the artificial intelligence identification model, an autoencoder model is used as the algorithm, and the K-mer frequency coding method is used to introduce artificial parameters. Proper adjustments of these parameters are required for optimal cable defect identification performance in various applications and usage scenarios. Experiment results show that the proposed K-mer frequency encoding method is effective, with a cable identification accuracy rate of 91% achieved through repeated sampling. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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14 pages, 408 KiB  
Article
Improving Multiclass Classification of Fake News Using BERT-Based Models and ChatGPT-Augmented Data
by Elena Shushkevich, Mikhail Alexandrov and John Cardiff
Inventions 2023, 8(5), 112; https://doi.org/10.3390/inventions8050112 - 01 Sep 2023
Cited by 2 | Viewed by 1959
Abstract
Given the widespread accessibility of content creation and sharing, false information proliferation is a growing concern. Researchers typically tackle fake news detection (FND) in specific topics using binary classification. Our study addresses a more practical FND scenario, analyzing a corpus with unknown topics [...] Read more.
Given the widespread accessibility of content creation and sharing, false information proliferation is a growing concern. Researchers typically tackle fake news detection (FND) in specific topics using binary classification. Our study addresses a more practical FND scenario, analyzing a corpus with unknown topics through multiclass classification, encompassing true, false, partially false, and other categories. Our contribution involves: (1) exploring three BERT-based models—SBERT, RoBERTa, and mBERT; (2) enhancing results via ChatGPT-generated artificial data for class balance; and (3) improving outcomes using a two-step binary classification procedure. Our focus is on the CheckThat! Lab dataset from CLEF-2022. Our experimental results demonstrate a superior performance compared to existing achievements but FND’s practical use needs improvement within the current state-of-the-art. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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22 pages, 4771 KiB  
Article
Research on the Design of Virtual Reality Online Education Information Presentation Based on Multi-Sensory Cognition
by Wen-Yu Wu, Jia-Yu Guo, Ying-Jing Li and Ying-Liang Sun
Inventions 2023, 8(2), 63; https://doi.org/10.3390/inventions8020063 - 20 Apr 2023
Viewed by 2019
Abstract
The popularity of the online teaching model increased during the COVID-19, and virtual reality online education is now firmly established as a future trend in educational growth. Human–computer interaction and collaboration between virtual models and physical entities, as well as virtual multi-sensory cognition, [...] Read more.
The popularity of the online teaching model increased during the COVID-19, and virtual reality online education is now firmly established as a future trend in educational growth. Human–computer interaction and collaboration between virtual models and physical entities, as well as virtual multi-sensory cognition, have become the focus of research in the field of online education. In this paper, we analyze the mapping form of teaching information and cue information on users’ cognition through an experimental system and investigate the effects of the presentation form of online virtual teaching information, the length of the material, users’ memory of the information, and the presentation form of information cues on users’ cognitive performance. The experimental results show that different instructional information and cue presentation designs have significant effects on users’ learning performance, with relatively longer instructional content being more effective and users being more likely to mechanically remember the learning materials. By studying the impact of multi-sensory information presentation on users’ cognition, the output design of instructional information can be optimized, cognitive resources can be reasonably allocated, and learning effectiveness can be ensured, which is of great significance for virtual education research in digital twins. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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15 pages, 8367 KiB  
Article
Low-Cost Systematic Methodology for Rapidly Constructing a Physiological Monitoring Interface in ICU
by Ke-Feng Lin, Shih-Sung Lin and Ping-Nan Chen
Inventions 2023, 8(2), 50; https://doi.org/10.3390/inventions8020050 - 22 Feb 2023
Viewed by 1201
Abstract
During the COVID-19 pandemic, which emerged in 2020, many patients were treated in isolation wards because of the high infectivity and long incubation period of COVID-19. Therefore, monitoring systems have become critical to patient care and to safeguard medical professional safety. The user [...] Read more.
During the COVID-19 pandemic, which emerged in 2020, many patients were treated in isolation wards because of the high infectivity and long incubation period of COVID-19. Therefore, monitoring systems have become critical to patient care and to safeguard medical professional safety. The user interface is very important to the surveillance system; therefore, we used web technology to develop a system that can create an interface based on user needs. When the surveillance scene needs to be changed, the surveillance location can be changed at any time, effectively reducing the costs and time required, so that patients can achieve timely and appropriate goals of treatment. ZigBee was employed to develop a monitoring system for intensive care units (ICUs). Unlike conventional GUIs, the proposed GUI enables the monitoring of various aspects of a patient, and the monitoring interface can be modified according to the user needs. A simulated ICU environment monitoring system was designed to test the effectiveness of the system. The simulated environment and monitoring nodes were set up at positions consistent with the actual clinical environments to measure the time required to switch between the monitoring scenes or targets on the GUI. A novel system that can construct ZigBee-simulated graphical monitoring interfaces on demand was proposed in this study. The locations of the ZigBee monitoring nodes in the user interface can be changed at any time. The time required to deploy the monitoring system developed in this study was 4 min on average, which is much shorter than the time required for conventional methods (131 min). The system can effectively overcome the limitations of the conventional design methods for monitoring interfaces. This system can be used to simultaneously monitor the basic physiological data of numerous patients, enabling nursing professionals to instantly determine patient status and provide appropriate treatments. The proposed monitoring system can be applied to remote medical care after official adoption. Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications)
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