MétaCan
Menu
Back to cohort
Record W3196194646 · doi:10.32920/ryerson.14654949.v1

Power Line Communication For Automotive Applications

2021· preprint· en· W3196194646 on OpenAlexaff
Xiaoguang Huang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsToronto Metropolitan University
FundersNational Institute of Mental HealthShandong Academy of Sciences
KeywordsAutomotive industryPower-line communicationAutomotive engineeringNoise (video)Power (physics)EngineeringElectric power transmissionElectrical engineeringBattery (electricity)Electronic engineeringLine (geometry)Computer science

Abstract

fetched live from OpenAlex

The following thesis is an investigation on adopting Power Line Communication (PLC) technology from AC (Alternating Current) lines to DC (Direct Current) lines and possibility to apply it on automotive applications. Prototype modules are built with existing PLC chip to verify communication performance over the battery DC lines. In order to ensure reliable data transmission over the vehicle DC power lines, research is expanded to analyze the characteristics of vehicle DC-bus network. Typical automotive components are selected and tested for interference and noise analysis. Detailed studies on impulsive noise and its statistic distribution are presented. As well, the characteristics of Lead-acid battery are reviewed on the possible impacts to carrier frequency. Overall, PLC technology is promising for automotive applications. But the test result shows there is limitation to apply existing PLC product to automotive DC applications. Directly adopting the control method used in AC application to DC is not trivial. Further study and future research areas are recommended to be conducted to mature the PLC technology being utilized on automotive systems.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.935

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 science0.0010.001
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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicPower Line Communications and NoiseFrench-language works237,207