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Record W2009184226 · doi:10.5539/cis.v1n4p72

Design of the Automatic Spreader Control System Based on Embedded System

2008· article· en· W2009184226 on OpenAlexvenueno aff
Yinglin Li, Lianhe Yang, Fang Ma, Yingzhong Li

Bibliographic record

VenueComputer and Information Science · 2008
Typearticle
Languageen
FieldEngineering
TopicEmbedded Systems and FPGA Design
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEnhanced Data Rates for GSM EvolutionProcess (computing)Control systemControl (management)SoftwareMicrocontrollerAutomatic controlWork (physics)Embedded systemComputer hardwareControl engineeringArtificial intelligenceMechanical engineeringOperating systemElectrical engineering

Abstract

fetched live from OpenAlex

The control system of the traditional automatic spreader with complex structure and inconvenient servicing is designed based on MCU, and the new control system is designed based on the embedded system. Based on the analysis of the work process and work principle for the automatic spreader, we put forward the new scheme to improve the design of the control system for traditional spreader and design the hardware structure and relative software. The new control system can not only actualize the automatic control for the spreader and possess many functions such as spreading tier setup and automatic cloth edge alignment, but also possess the functions including network and system extension and effectively reduce the price of automatic spreader, and the improved spreader will possess stronger functions, more convenient operation and simpler maintenance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.185
Teacher spread0.172 · 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 designBench or experimental
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

Citations2
Published2008
Admission routes1
Has abstractyes

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