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Wang S, Du M, Liu Z, Luo Y, Xiong X. Design and Implementation of an Ontology for Measurement Terminology in Digital Calibration Certificates. SENSORS (BASEL, SWITZERLAND) 2024; 24:3989. [PMID: 38931773 PMCID: PMC11207306 DOI: 10.3390/s24123989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
Digital Calibration Certificates (DCCs) are a key focus in metrology digitalization, necessitating that they satisfy the criteria for machine readability and understandability. Current DCCs are machine-readable, but they are still missing the essential semantic information required for machine understandability. This shortfall is particularly notable in the lack of a dedicated semantic ontology for measurement terminologies. This paper proposes a domain ontology for measurement terminologies named the OMT (Ontology for Measurement Terminology), using a foundation of metrological terms from standards like the International Vocabulary of Metrology (VIM), the Guide to the Expression of Uncertainty in Measurement (GUM), and JJF1001. It also incorporates insights from models such as the SI Reference Point, the Simple Knowledge Organization System (SKOS), and the DCC Schema. The methodology was guided by Stanford's Seven-Step Method, ensuring a systematic development process tailored to the needs of metrological semantics. Through semantic expression capability verification and SPARQL query validations, the OMT has been confirmed to possess essential machine readability and understandability features. It has been successfully integrated into version 3.2.1 of DCCs across ten representative domains. This integration demonstrates an effective method for ensuring that DCCs are machine-readable and capable of interoperating within digital environments, thereby advancing the research in metrology digitization.
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Affiliation(s)
- Shuaizhe Wang
- National Institute of Metrology, Beijing 100029, China; (S.W.); (Z.L.)
- Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Refulation, Beijing 100029, China
| | - Mingxin Du
- The College of Information Engineering, China Jiliang University, Hangzhou 310018, China; (M.D.); (Y.L.)
| | - Zilong Liu
- National Institute of Metrology, Beijing 100029, China; (S.W.); (Z.L.)
- Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Refulation, Beijing 100029, China
| | - Yuqi Luo
- The College of Information Engineering, China Jiliang University, Hangzhou 310018, China; (M.D.); (Y.L.)
| | - Xingchuang Xiong
- National Institute of Metrology, Beijing 100029, China; (S.W.); (Z.L.)
- Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Refulation, Beijing 100029, China
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Shelke S, Veerubhotla K, Lee Y, Lee CH. Telehealth of cardiac devices for CVD treatment. Biotechnol Bioeng 2024; 121:823-834. [PMID: 38151894 DOI: 10.1002/bit.28637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 12/29/2023]
Abstract
This review covers currently available cardiac implantable electronic devices (CIEDs) as well as updated progress in real-time monitoring techniques for CIEDs. A variety of implantable and wearable devices that can diagnose and monitor patients with cardiovascular diseases are summarized, and various working mechanisms and principles of monitoring techniques for Telehealth and mHealth are discussed. In addition, future research directions are presented based on the rapidly evolving research landscape including Artificial Intelligence (AI).
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Affiliation(s)
- Sushil Shelke
- Division of Pharmacology and Pharmaceutics Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - Krishna Veerubhotla
- Division of Pharmacology and Pharmaceutics Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - Yugyung Lee
- Division of Computer Science, School of Science and Engineering, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - Chi H Lee
- Division of Pharmacology and Pharmaceutics Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri, USA
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Junaid SB, Imam AA, Balogun AO, De Silva LC, Surakat YA, Kumar G, Abdulkarim M, Shuaibu AN, Garba A, Sahalu Y, Mohammed A, Mohammed TY, Abdulkadir BA, Abba AA, Kakumi NAI, Mahamad S. Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 2022; 10:healthcare10101940. [PMID: 36292387 PMCID: PMC9601636 DOI: 10.3390/healthcare10101940] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
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Affiliation(s)
| | - Abdullahi Abubakar Imam
- School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
- Correspondence: (A.A.I.); or (A.O.B.)
| | - Abdullateef Oluwagbemiga Balogun
- Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
- Correspondence: (A.A.I.); or (A.O.B.)
| | | | | | - Ganesh Kumar
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | - Muhammad Abdulkarim
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Aliyu Nuhu Shuaibu
- Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
| | - Aliyu Garba
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Yusra Sahalu
- SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
| | - Abdullahi Mohammed
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | | | | | | | - Nana Aliyu Iliyasu Kakumi
- Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo Governorate 4473303, Egypt
| | - Saipunidzam Mahamad
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
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Lu X, Tian G, Wang Z, Li W, Yang D, Li H, Wang Y, Ni J, Zhang Y. Research on the Time Drift Stability of Differential Inductive Displacement Sensors with Frequency Output. SENSORS (BASEL, SWITZERLAND) 2022; 22:6234. [PMID: 36015994 PMCID: PMC9413745 DOI: 10.3390/s22166234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital converter (LDC) demonstrated significant advantages in comparison with conventional sensors with analog-to-digital converter (ADC) interfaces. In order for the differential inductive frequency output displacement (DIFOD) sensor to meet the high-stability requirements of segmented mirror astronomical telescopes, it is important to understand the factors for time drift of the sensor. This paper focuses on the investigation of key factors of sensor structure and material, signal conditioning and interface, and fixtures for time drift to permanently installed applications. First, the measurement principle and probe structural characteristics of the sensor are analyzed. Then, two kinds of signal conditioning and digitalization methods using resonance circuits and LDC chips are implemented and compared. Finally, the time drift stability experiments are performed on the sensors with different signal conditioning methods and fixtures under controlled temperature. Experimental results show that the magnetic shield ring effectively improves the sensitivity and quality factor of the sensors, the time drift stability of the sensor using the signal conditioning based on resonance circuits is better than that of the sensors using LDC chips, and the root mean square (RMS) of the sensor time drift meets the requirement of 0.01 μm/24 h. This study will help further development of high-stability of frequency output sensors and IoT-based systems for scaled-up applications in the future.
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Affiliation(s)
- Xiaolong Lu
- School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
| | - Guiyun Tian
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zongwen Wang
- School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
| | - Wentao Li
- School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
| | - Dehua Yang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Haoran Li
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - You Wang
- National Astronomical Observatories, Nanjing Institute of Astronomical Optics and Technology, Chinese Academy of Sciences, Nanjing 210042, China
| | - Jijun Ni
- National Astronomical Observatories, Nanjing Institute of Astronomical Optics and Technology, Chinese Academy of Sciences, Nanjing 210042, China
| | - Yong Zhang
- National Astronomical Observatories, Nanjing Institute of Astronomical Optics and Technology, Chinese Academy of Sciences, Nanjing 210042, China
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Barbosa CRH, Sousa MC, Almeida MFL, Calili RF. Smart Manufacturing and Digitalization of Metrology: A Systematic Literature Review and a Research Agenda. SENSORS (BASEL, SWITZERLAND) 2022; 22:6114. [PMID: 36015873 PMCID: PMC9460109 DOI: 10.3390/s22166114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/07/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Smart manufacturing comprises fully integrated manufacturing systems that respond in real time to meet the changing demands and conditions in industrial activities, supply networks and customer needs. A smart manufacturing environment will face new challenges, including those concerning metrological issues, i.e., analysis of large quantities of data; communication systems for digitalization; measurement standards for automated process control; digital transformation of metrological services; and simulations and virtual measurement processes for the automatic assessment of measured data. Based on the assumption that the interplay between smart manufacturing and digitalization of metrology is an emerging research field, this paper aims to present a systematic literature review (SLR) based on a bibliographic data collection of 160 scientific articles retrieved from the Web of Science and Scopus databases over the 2016-2022 time frame. The findings presented in this review and recommendations for building a research agenda can help policy makers, researchers and practitioners by providing directions for the evolution of digital metrology and its role in the digitalization of the economy and society.
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Javaid M, Haleem A, Singh RP, Rab S, Suman R. Exploring impact and features of machine vision for progressive industry 4.0 culture. SENSORS INTERNATIONAL 2022. [DOI: 10.1016/j.sintl.2021.100132] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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Tambare P, Meshram C, Lee CC, Ramteke RJ, Imoize AL. Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review. SENSORS 2021; 22:s22010224. [PMID: 35009767 PMCID: PMC8749653 DOI: 10.3390/s22010224] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022]
Abstract
The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.
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Affiliation(s)
- Parkash Tambare
- Water Resources & Applied Mathematics Research Lab, Nagpur 440027, Maharashtra, India;
| | - Chandrashekhar Meshram
- Department of Post Graduate Studies and Research in Mathematics, Jaywanti Haksar Govt. Post-Graduation College, College of Chhindwara University, Betul 460001, Madhya Pradesh, India
- Correspondence: (C.M.); (C.-C.L.)
| | - Cheng-Chi Lee
- Department of Library and Information Science, Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University, New Taipei 24205, Taiwan
- Department of Computer Science and Information Engineering, Asia University, Wufeng Shiang, Taichung 41354, Taiwan
- Correspondence: (C.M.); (C.-C.L.)
| | - Rakesh Jagdish Ramteke
- School of Computer Sciences, KBC North Maharashtra University, P.B. No.80, Umavinagar, Jalgaon 425001, Maharashtra, India;
| | - Agbotiname Lucky Imoize
- Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria;
- Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany
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9
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Javaid M, Abid Haleem, Pratap Singh R, Rab S, Suman R. Upgrading the manufacturing sector via applications of Industrial Internet of Things (IIoT). SENSORS INTERNATIONAL 2021. [DOI: 10.1016/j.sintl.2021.100129] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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