MétaCan
Menu
Back to cohort
Record W1983270888 · doi:10.1109/mesa.2008.4735690

Design and Analysis of a Hybrid Backup Power System for a High-Rise and High-Speed Elevator

2008· article· en· W1983270888 on OpenAlexaff
Leon Zhou, Zuomin Dong, Sibo Wang, Zhiping Qi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBackupElevatorModelicaAutomotive engineeringPower (physics)Electric power systemHybrid systemHybrid powerEnergy storageTransient (computer programming)Computer scienceEngineeringControl engineeringMechanical engineeringAerospace engineeringOperating system

Abstract

fetched live from OpenAlex

With increased dependence on elevators in high-rise buildings, a reliable and effective backup power system is in urgent need. In this work, a hybrid backup power system that consists of hydrogen fuel cells, batteries and ultracapacitors is introduced. The electric power system was modeled using the multi-physics/system-dynamics modeling tool, Modelica/Dymola. Power performance of the backup power system was examined using an elevator model built on the same platform. A hybrid energy storage system with ultracapacitors and batteries was designed to deal with the large transient current flow and cost constraint. The size of more cost sensitive fuel cell power system was significantly reduced. This simulation-based design and analysis support the testing and production of the proposed new 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.191
Teacher spread0.180 · 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 designSimulation or modeling
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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

Same topicElectric and Hybrid Vehicle TechnologiesFrench-language works237,207