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Record W2145787620 · doi:10.1109/tvlsi.2003.819320

A model for battery lifetime analysis for organizing applications on a pocket computer

2003· article· en· W2145787620 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBattery (electricity)MaximizationComputer scienceComputationLithium-ion batterySimulationReliability engineeringAutomotive engineeringEngineeringPower (physics)Algorithm

Abstract

fetched live from OpenAlex

A battery-powered portable electronic system shuts down once the battery is discharged; therefore, it is important to take the battery behavior into account. A system designer needs an adequate high-level battery model to make battery-aware decisions targeting the maximization of the system's online lifetime. We propose such a model that allows a designer to analytically predict the battery time-to-failure for a given load. Our model also allows for a tradeoff between the accuracy and the amount of computation performed. The quality of the proposed model is evaluated using typical pocket computer applications and a detailed low-level simulation of a lithium-ion electrochemical cell. In addition, we verify the proposed model against actual measurements taken on a real lithium-ion battery.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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