A Large-Scale Sensor Layout Optimization Algorithm for Improving the Accuracy of Inverse Finite Element Method.

Zhenyi Zhao, Kangyu Chen, Yimin Liu, Hong Bao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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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

1 citing paper in PubMed.

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.Sensors (Basel, Switzerland) · 2024
Article

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

4 authors.

Zhenyi ZhaoKey Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.ORCID 0009-0008-1280-4512

Kangyu ChenKey Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.

Yimin LiuKey Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.

Hong BaoKey Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.

Funding

National Natural 51775401National Natural 52275542

6 · The paper itself

Abstract

The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional strain sensor layout optimization problems of complex antenna truss models. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and

Indexed as

cooperative coevolutiongrouping methodinverse finite element methodparticle swarm optimizationstructural health monitoring

Identifiers

PMID37837005

DOI10.3390/s23198176

PMCPMC10574954

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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.