Water level identification with laser sensors, inertial units, and machine learning

Ranieri, Caetano M.; Foletto, Angelo V.K.; Garcia, Rodrigo D.; Matos, Saulo N.; Medina, Maria M.G.; Marcolino, Leandro S.; Ueyama, Jó

doi:10.1016/j.engappai.2023.107235

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2023.107235

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Water level identification with laser sensors, inertial units, and machine learning

Caetano M. Ranieri,

Angelo V.K. Foletto,

Rodrigo D. Garcia

et al.

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2024

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Cited by 4 publications

References 66 publications

(106 reference statements)

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Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

Heidari,

Amiri,

Jamali

et al. 2024

Concurrency and Computation

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SummaryWireless Sensor Networks (WSNs) are critical for communication within a mile radius and industrial applications. These networks are very prone to failure due to their enormous number of nodes and their unique hardware and software restrictions. To make sure network performance, a lot of study needs to be done to improve failure tolerance and stability. This study looks at how to judge the availability and dependability of WSNs that have long‐term issues. The suggested method checks how well a network works in various failure cases by using fault trees and Markov chain analysis. Such methods help us find and study possible failure scenarios and how they might impact the network's dependability in a planned way. The results show that WSNs have major flaws and give useful suggestions for making the systems work better. The findings show that using these evaluation methods may greatly enhance the ability to handle faults, lower the risk of damage, and allow developers of WSNs to make smart choices.

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Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

Heidari,

Amiri,

Jamali

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

A deep learning workflow enhanced with optical flow fields for flood risk estimation

Ranieri,

Souza,

Nishijima

et al. 2024

Appl Intell

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An Evaluation of Iron Ore Characteristics Through Machine Learning and 2-D LiDAR Technology

Matos,

Pinto,

Domingues

et al. 2024

IEEE Trans. Instrum. Meas.

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Water level identification with laser sensors, inertial units, and machine learning

Cited by 4 publications

References 66 publications

Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

A deep learning workflow enhanced with optical flow fields for flood risk estimation

An Evaluation of Iron Ore Characteristics Through Machine Learning and 2-D LiDAR Technology

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