Bayesian updating for nuclear containment buildings using both mechanical and hydraulic monitoring data

Rossat, D.; Baroth, Julien; Briffaut, Matthieu; Dufour, Frédéric; Masson, Benoît; Monteil, A.; Michel-Ponnelle, Sylvie

doi:10.1016/j.engstruct.2022.114294

Cited by 10 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regardless of what the product is, as a specific solution or being integrated into other products, such a feature would be well suited to meet the flexibility requirements of the market. To address the problem of heterogeneous data access by third parties, a mapping table was designed in the database design work [ 8 ]. Since the reported data and the internal AIOps data may differ in terms of device types and metrics.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

AIOps Architecture in Data Center Site Infrastructure Monitoring

Dong

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

AIOps (artificial intelligence for IT operations) has been growing rapidly in recent years. However, it can be seen that the vast majority of AIOps applications are implemented in the IT domain. In contrast, there are few applications in the data center infrastructure domain. Many real-world practices show that a working architecture or algorithm cannot be directly replicated from other domains due to completely different business scenarios and different data characteristics. In this paper, an AIOps architecture for the data center infrastructure monitoring domain is presented. A proven working architecture is given in terms of core modules, such as technical architecture, machine learning algorithms, big data, and business applications, and details are designed in practice. This paper focuses on the technical part and not on each part of the architecture work. In other words, NFRs (nonfunctional requirements), such as performance, availability, manageability, and security, will not be discussed in this paper.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To address the problem of heterogeneous data access by third parties, a mapping table was designed in the database design work [ 8 ]. Since the reported data and the internal AIOps data may differ in terms of device types and metrics.…”

Section: Methodsmentioning

confidence: 99%

AIOps Architecture in Data Center Site Infrastructure Monitoring

Dong

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…To address the aforementioned issues, various methods have been developed globally to predict future trends in accident growth. Among them, BP neural network method, regression analysis method, time series prediction method, grey theory prediction method, and Markov prediction method are all common methods [10]. These prediction methods each have their own characteristics and can be selected and applied according to different application scenarios and needs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

Wang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

View full text Add to dashboard Cite

Traditional highway tunnel management systems often face problems such as independent and decentralized management, inflexible monitoring, and low visualization of disease details. Especially in the face of traffic accidents, lane foreign objects, vehicle congestion, and vehicle and pedestrian congestion in tunnels, traditional warning systems often fail to provide timely and accurate warning information, posing great risks to the safe operation of tunnels. In response to the above issues, this article proposes an intelligent early warning system for highway tunnel safety monitoring data based on Markov model (MM). This system fully utilizes the advantages of MM in describing random processes, and achieves dynamic monitoring and early warning of tunnel safety status through real-time analysis of tunnel monitoring data, combined with Internet of Things (IoT) technology. The experimental results show that the intelligent early warning system for highway tunnel safety monitoring data based on MM has high accuracy and reliability. It can not only achieve real-time monitoring and early warning of tunnel safety status, but also provide rich visual information to help managers better understand the operation status of tunnels and improve the level of tunnel safety management.

show abstract