AI-Driven Electrical Fast Transient Suppression for Enhanced Electromagnetic Interference Immunity in Inductive Smart Proximity Sensors.

Silvia Giangaspero, Gianluca Nicchiotti, Philippe Venier, Laurent Genilloud, Lorenzo Pirrami

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Silvia GiangasperoiSIS Institute, HEIA-FR, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland.

Gianluca NicchiottiiSIS Institute, HEIA-FR, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland.ORCID 0000-0002-3190-6708

Philippe VenierContrinex SA, Route du Pâqui 3, 1720 Corminboeuf, Switzerland.

Laurent GenilloudContrinex SA, Route du Pâqui 3, 1720 Corminboeuf, Switzerland.

Lorenzo PirramiiSIS Institute, HEIA-FR, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland.ORCID 0000-0002-2190-3888

Funding

Innosuisse - Swiss Innovation Agency 105.992 IP-ENG

6 · The paper itself

Abstract

Inductive proximity sensors are relevant in position-sensing applications in many industries but, in order to be used in harsh industrial environments, they need to be immune to electromagnetic interference (EMI). The use of conventional filters to mitigate these perturbations often compromises signal bandwidth, ranging from 100 Hz to 1.6 kHz. We have exploited recent advances in the field of artificial intelligence (AI) to study the ability of neural networks (NNs) to automatically filter out EMI features. This study offers an analysis and comparison of possible NN models (a 1D convolutional NN, a recurrent NN, and a hybrid convolutional and recurrent approach) for denoising EMI-perturbed signals and proposes a final model, which is based on gated recurrent unit (GRU) layers. This network is compressed and optimised to meet memory requirements, so that in future developments it could be implemented in application-specific integrated circuits (ASICs) for inductive sensors. The final RNN manages to reduce noise by 70% (MSE

Indexed as

compression techniquesconvolutional neural networksdeep neural networksEMIgated recurrent unitinductive smart proximity sensorslong short-term memoryrecurrent neural networkssignal denoising

Identifiers

PMID39599148

DOI10.3390/s24227372

PMCPMC11598082

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.