Evidence map›Paper›PMID 39203700›Full record

ArticleMicromachines2024

Resonant MEMS Accelerometer with Low Cross-Axis Sensitivity-Optimized Based on BP and NSGA-II Algorithms.

Jiaqi Miao, Pinghua Li, Mingchen Lv, Suzhen Nie, Yang Liu, Ruimei Liang, Weijiang Ma, Xuye Zhuang

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Jiaqi MiaoCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Pinghua LiCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Mingchen LvCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Suzhen NieCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Yang LiuCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Ruimei LiangCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Weijiang MaCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Xuye ZhuangCollege of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.

Funding

Natural Science Foundation of Shandong Province ZR2021MF042Taishan Scholars Program of Shandong Province tsqn201909108
6 · The paper itself

Abstract

This article proposes a low cross-axis sensitivity resonant MEMS(Micro-Electro-Mechanical Systems) accelerometer that is optimized based on the BP and NSGA-II algorithms. When resonant accelerometers are used in seismic monitoring, automotive safety systems, and navigation applications, high immunity and low cross-axis sensitivity are required. To improve the high immunity of the accelerometer, a coupling structure is introduced. This structure effectively separates the symmetric and antisymmetric mode frequencies of the DETF resonator and prevents mode coupling. To obtain higher detection accuracy and low cross-axis sensitivity, a decoupling structure is introduced. To find the optimal dimensional parameters of the decoupled structure, the BP and NSGA-II algorithms are used to optimize the dimensional parameters of the decoupled structure. The optimized decoupled structure has an axial stiffness of 6032.21 N/m and a transverse stiffness of 6.29 N/m. The finite element analysis results show that the sensitivity of the accelerometer is 59.1 Hz/g (

Indexed as

BP and NSGA-II algorithmcross-axis sensitivityMEMS resonant accelerometermulti-objective optimization

Identifiers

PMID39203700
PMCPMC11356109

What OpenQuestion holds

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Registered trials

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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.