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Record W2387275848

Design and Implementation of Single-chip Intelligent Automobile Based on MC9S12DG128

2008· article· en· W2387275848 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMicrocomputer applications · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceControl unitPhotoelectric sensorComputer hardwareHall effect sensorAutomotive industryPulse-width modulationChipServo controlServomotorServoDC motorElectronic circuitPower (physics)Electrical engineeringTelecommunicationsArtificial intelligenceEngineeringVoltage
DOInot available

Abstract

fetched live from OpenAlex

This designed automobile,besides using single-chip computer(MC9S12DG128)as the core,is composed of motor, servo,photoelectric sensors and other power circuits.The reflecting infrared sensor will collect signals and transport them to the core control unit.After distinguishing the signals by the core control unit,PWM4 generating module will give out PWM waves to control the steering motor and DC motor,and thus realize the steering and advancement.The Hall sensors on the rear wheels of intelligent automobiles are responsible for collecting the pulse signals.Through the collection and processing of signals in the forward path,the auto can preferably carry out moving control on the drive in the backward path,the processing display, and the audible and visual alarms of relevant information.RPR220 reflective photo sensor will seek the trace.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.403

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 head0.016
GPT teacher head0.235
Teacher spread0.218 · 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