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Microcontroller-Based Auto Lock Ignition System for Vehicle Overload Prevention

2024· article· en· W4399530140 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEmbedded Systems and FPGA Design
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsLock (firearm)MicrocontrollerComputer scienceIgnition systemAutomotive engineeringEmbedded systemEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

New technologies are being developed for more efficient overload screening and enforcement. Weigh-in-Motion (WIM) technologies allow trucks to be weighed in the traffic flow, without any disruption to operations. Much progress has been made recently to improve and implement Load cell based overload detection systems, which can contribute to safer and more efficient operation of trucks. In this work, we are developing an Auto Lock ignition System. The input for the system is from Load cell or any other mechanism. The controller keeps looking for the output from these sensors. If there are any traces of over load above the set limit, then the system will lock the Engine. In our work Microcontroller based Monitoring system monitors the vehicle overload. When the reference voltage from load cell exceeds the set limit the controller cut off the power supply to spark plug thus it stops the ignition in engine.

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.

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)0.000
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: none
Teacher disagreement score0.983
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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