Cyber-physical systems for structural health monitoring: sensing technologies and intelligent computing

Doghri, Wael; Saddoud, Ahlem; Fourati, Lamia Chaari

doi:10.1007/s11227-021-03875-5

Cited by 24 publications

(7 citation statements)

References 80 publications

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“…1.2. AI has been widely applied to sensing, control, communication, and data processing in a variety of applications such as biomedical monitoring, robotics systems, and digital twins [48,42,21,15]. In these CPS applications, AI not only serves as a technology for reasoning, planning, learning, and processing, but also enables the manipulation of physical objects.…”

Section: Incorporating Ai Stack Into Mission Stackmentioning

confidence: 99%

An Introduction of System-Scientific Approaches to Cognitive Security

Huang¹,

Zhu²

2023

Preprint

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Human cognitive capacities and the needs of human-centric solutions for "Industry 5.0" make humans an indispensable component in Cyber-Physical Systems (CPSs), referred to as Human-Cyber-Physical Systems (HCPSs), where AI-powered technologies are incorporated to assist and augment humans. The close integration between humans and technologies in Section 1.1 and cognitive attacks in Section 1.2.4 poses emerging security challenges, where attacks can exploit vulnerabilities of human cognitive processes, affect their behaviors, and ultimately damage the HCPS.Defending HCPSs against cognitive attacks requires a new security paradigm, which we refer to as "cognitive security" in Section 1.2.5. The vulnerabilities of human cognitive systems and the associated methods of exploitation distinguish cognitive security from "cognitive reliability" and give rise to a distinctive CIA triad, as shown in Sections 1.2.5.1 and 1.2.5.2, respectively. Section 1.2.5.3 introduces cognitive and technical defense methods that deter the kill chain of cognitive attacks and achieve cognitive security. System scientific perspectives in Section 1.3 offer a promising direction to address the new challenges of cognitive security by developing quantitative, modular, multi-scale, and transferable solutions. Fig. 1.1 illustrates the structure of Chapter 1.

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Section: Incorporating Ai Stack Into Mission Stackmentioning

confidence: 99%

An Introduction of System-Scientific Approaches to Cognitive Security

Huang¹,

Zhu²

2023

Preprint

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“…An important research direction that is also currently being explored is full understanding of data collected by currently existing strain sensing techniques and the assessment of capabilities of strain measurements to be used for the prediction of future structural behaviors using data-driven approaches (statistics and machine learning, e.g., [ 126 , 127 ]). While this direction does not directly address the development of sensors, it is extremely important as it emphasizes the importance of sensors beyond simple strain measurement and has the potential of enabling smart structures and transforming them into cyber-physical systems (e.g., [ 128 ]).…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years

Glišić

2022

Sensors

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Strain is one of the most frequently monitored parameters in civil structural health monitoring (SHM) applications, and strain-based approaches were among the first to be explored and applied in SHM. There are multiple reasons why strain plays such an important role in SHM: strain is directly related to stress and deflection, which reflect structural performance, safety, and serviceability. Strain field anomalies are frequently indicators of unusual structural behaviors (e.g., damage or deterioration). Hence, the earliest concepts of strain sensing were explored in the mid-XIX century, the first effective strain sensor appeared in 1919, and the first onsite applications followed in the 1920′s. Today, one hundred years after the first developments, two generations of strain sensors, based on electrical and fiber-optic principles, firmly reached market maturity and established themselves as reliable tools applied in strain-based SHM. Along with sensor developments, the application methods evolved: the first generation of discrete sensors featured a short gauge length and provided a basis for local material monitoring; the second generation greatly extended the applicability and effectiveness of strain-based SHM by providing long gauge and one-dimensional (1D) distributed sensing, thus enabling global structural and integrity monitoring. Current research focuses on a third generation of strain sensors for two-dimensional (2D) distributed and quasi-distributed sensing, based on new advanced technologies. On the occasion of strain sensing centenary, and as an homage to all researchers, practitioners, and educators who contributed to strain-based SHM, this paper presents an overview of the first one hundred years of strain sensing technological progress, with the objective to identify relevant transformative milestones and indicate possible future research directions.

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“…Finally, average n-2 similarity features concerning n-2 viewpoints are taken as the similarity features between the two tweet documents. This method is most accurate for visualizing the number of clusters for the set of n tweet documents [ 30 ]. The approach for the similarity feature computation between any two documents for the set of five tweet documents is shown in Fig.…”

Section: Literature Of Visual Techniques For Precluster Assessmentmentioning

confidence: 99%

A novel sampling-based visual topic models with computational intelligence for big social health data clustering

et al. 2022

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Twitter is a popular social network for people to share views or opinions on various topics. Many people search for health topics through Twitter; thus, obtaining a vast amount of social health data from Twitter is possible. Topic models are widely used for social health-care data clustering. These models require prior knowledge about the clustering tendency. Determining the number of clusters of given social health data is known as the health cluster tendency. Visual techniques, including visual assessment of the cluster tendency, cosine-based, and multiviewpoint-based cosine similarity features VAT (MVCS-VAT), are used to identify social health cluster tendencies. The recent MVCS-VAT technique is superior to others; however, it is the most expensive technique for big social health data cluster assessment. Thus, this paper aims to enhance the work of the MVCS-VAT using a sampling technique to address the big social health data assessment problem. Experimental is conducted on different health datasets for demonstrating an efficiency of proposed work. Accuracy of social health data clustering is improved at a rate of 5 to 10% in the proposed S-MVCS-VAT when compared to MVCS-VAT. From obtained results, it also proved that the proposed S-MVCS-VAT is a faster and memory efficient for discovering social health data clusters.

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Cyber-physical systems for structural health monitoring: sensing technologies and intelligent computing

Cited by 24 publications

References 80 publications

An Introduction of System-Scientific Approaches to Cognitive Security

An Introduction of System-Scientific Approaches to Cognitive Security

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years

A novel sampling-based visual topic models with computational intelligence for big social health data clustering

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