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Record W2359451902

Vehicle Voice Navigation System's Research Based on SPCE061A Microcomputer

2009· article· en· W2359451902 on OpenAlexvenueno aff
Liu Chunrong

Bibliographic record

VenueMicrocomputer applications · 2009
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceRadio-frequency identificationDedicated short-range communicationsNavigation systemRadio navigationIdentification (biology)Real-time computingIntelligent transportation systemMicrocomputerTelecommunicationsGlobal Positioning SystemEmbedded systemComputer securityWirelessTransport engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

Integration system of radio frequency identification(RFID)with voice navigation which applied at the vehicle for vehicle monitoring and guidance is put forward in the paper.This system has the function of processing cell radio frequency(RF)on the roadside and sound broadcast which is based on DSRC communication protocols,which achieved providing real-time voice navigation for travelers.The voice navigation built-in vehicle system stands out active intelligent management,which system integrated vehicle identification,electronic charge and providing services for travelers,and it can change the current passive situation of road system for guidance.Actually,this built-in vehicle system offers one applied built-in vehicle plane for travelers' services system which is the sub-system of intelligent traffic system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.025
GPT teacher head0.300
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2009
Admission routes1
Has abstractyes

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