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Record W2974383744 · doi:10.1109/tie.2019.2941141

A Hybrid-Driven Elevator System With Energy Regeneration and Safety Enhancement

2019· article· en· W2974383744 on OpenAlexaff
Bin Zhao, Zhongyi Quan, Yunwei Li, Long Quan, Yunxiao Hao, Li Ding

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicElevator Systems and Control
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Shanxi ProvinceNational Natural Science Foundation of China
KeywordsElevatorHydraulic accumulatorAutomotive engineeringAccumulator (cryptography)TorqueEnergy consumptionHydraulic machineryHydraulic motorEnergy (signal processing)DissipationEngineeringComputer scienceHybrid systemControl engineeringMechanical engineeringElectrical engineeringStructural engineering

Abstract

fetched live from OpenAlex

Elevator has been the most critical equipment for vertical transportation and one of the largest energy consumption sources in buildings. The main cause of energy waste in elevator is the dissipation of the energy regenerated by the motor. To achieve energy saving, this article proposes an electro-hydraulic hybrid driving elevator system. With an auxiliary hydraulic system, the energy generated by the motor can be stored in the form of hydraulic accumulator pressure, which can be reused to assist motor start in the form of supporting torque. In addition, due to the additional torque provided by the hydraulic system, elevator safety can be enhanced. Simulation results have been obtained, demonstrating that large amount of energy can be saved with the proposed system. Field test results with elevator in a real building environment are also presented to verify the efficacy of the proposed idea.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.172
Teacher spread0.166 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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