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Scanning Angle Control of MEMS Micro-mirror Used in LIDAR

2022· article· en· W4294672483 on OpenAlexaff
Yonghong Tan, Jianhong Xiong, Ruili Dong, Qingyuan Tan

Bibliographic record

Venue2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) · 2022
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsMicroelectromechanical systemsLidarCompensation (psychology)HysteresisOpticsNonlinear systemLaser scanningMaterials sciencePID controllerLaserControl theory (sociology)Computer sciencePhysicsEngineeringOptoelectronicsControl (management)Artificial intelligenceTemperature controlControl engineering

Abstract

fetched live from OpenAlex

In this paper, a robust-control scheme of scanning angle of MEMS micro-mirror-based LIDAR is proposed. The structure of LIDAR using an electromagnetically driven scanning micro-mirror for laser beam scanning is presented. Then, based on the analysis of the characteristic of the micromirror, a nonlinear hysteresis compensation scheme is used for compensating the effect of hysteresis, then a PID strategy based on robust design is proposed for controling the angular movement. The corresponding experimental results are illustrated.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.883

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 science0.0010.000
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 head0.049
GPT teacher head0.302
Teacher spread0.253 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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