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Record W4384135220 · doi:10.23977/acss.2023.070514

Optimization and Performance Analysis of a Vehicle Hydraulic Power Generation System Based on Energy Regulation Technology

2023· article· en· W4384135220 on OpenAlexvenueno aff
Yuanzhi Huang

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy conservationSustainable developmentEfficient energy useHydraulic machineryEnvironmental economicsEnvironmental pollutionSustainable transportElectricity generationPower (physics)Environmental scienceEngineeringSustainabilityEnvironmental protectionMechanical engineering

Abstract

fetched live from OpenAlex

With the development and growth of society and economy, the transportation industry is continuously expanding. However, traditional transportation methods suffer from energy waste and environmental pollution. Improving transportation efficiency and reducing environmental impact have become one of the current research hotspots. The vehicle hydraulic power generation system is a technology that converts wasted hydraulic energy during vehicle operation into electrical energy. It enables the recovery and utilization of energy during transportation, thus improving energy utilization efficiency and reducing environmental impact. Therefore, studying the design optimization and performance analysis of the vehicle hydraulic power generation system is of great significance in promoting sustainable development and energy conservation in transportation.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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