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Record W4388573512 · doi:10.18280/i2m.220504

Design and Evaluation of a Spider Web-Like Single-Axis Micro-Electro-Mechanical Systems Accelerometer with High Sensitivity and Fast Response

2023· article· en· W4388573512 on OpenAlexvenueno aff
Haider Al‐Mumen

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAccelerometerSensitivity (control systems)SpiderComputer scienceMaterials sciencePhysicsEngineeringElectronic engineeringOperating system

Abstract

fetched live from OpenAlex

This research delves into the design, simulation, and evaluation of a high-sensitivity, swiftresponse single-axis Micro-Electro-Mechanical Systems (MEMS) accelerometer, inspired by the intricate design of a spider web.The accelerometer, characterized by a circular silicon structure of 1 mm radius, embraces a proof mass with a radius and thickness of 60 µm and 2 µm, respectively.A comprehensive study was undertaken to scrutinize the electrical properties, notably the time response, and mechanical properties, specifically the stress effects on the device.An outstanding sensitivity of 0.047 µm/g and a brisk rise time of 0.1 ms within the acceleration range of -50 to 50 g were observed.A signal conditioning and processing circuit, composed of a bridge circuit, two operational amplifiers functioning as a difference amplifier, and a filter, was meticulously designed using MATLAB and integrated with the accelerometer sensor, facilitating the derivation of a DC output voltage representative of the sensed acceleration.The simplicity, ease of modification, swift response, and high sensitivity of the proposed MEMS accelerometer underscore its promising application potential.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.275
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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