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Record W2384813845

Smart Car's Software Design Based on S12 Microcomputer

2010· article· en· W2384813845 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 · 2010
Typearticle
Languageen
FieldEngineering
TopicEmbedded Systems and FPGA Design
Canadian institutionsnot available
Fundersnot available
KeywordsRudderComputer scienceMicrocomputerSoftwareMicroprocessorPID controllerFilter (signal processing)Computer hardwareDC motorSoftware designEmbedded systemReal-time computingControl engineeringChipComputer visionSoftware developmentTelecommunicationsElectrical engineeringOperating system
DOInot available

Abstract

fetched live from OpenAlex

In this paper,It will describe the details software including road recognition and motion control arithmetic.with the Freescale 16-bit single-chip MC9S12xs128 as its control microprocessor to finish software design about smart car.The car get the road information and abstract the black line,then control the car on a way of closed-loop in shortest time.①Use dimage-sensor module based on camera to obtain lane image information,taken action get correct image information.for instance.filter is a best way.②Used PID to control rudder,advanced the speed of rudder response,reduced error at controlled.③Used fuzzy control to control the motor of smart car.by ceaseless experiments to enrich fuzzy control library.④Compiled procedure about wireless module,feedback information to computer,it is convenient for testing.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.685
Threshold uncertainty score1.000

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)0.0000.001

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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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