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Record W1972897708 · doi:10.1109/ccece.2010.5575118

Power management with energy harvesting devices

2010· article· en· W1972897708 on OpenAlexaff
D. Pimentel, Petr Musı́lek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnergy harvestingComputer scienceEnergy managementPower managementPower (physics)Energy (signal processing)Battery (electricity)Electrical engineeringSoftwareEnergy sourceEnergy management systemEngineeringRenewable energyOperating system

Abstract

fetched live from OpenAlex

Traditional power management for battery powered devices does not meet the requirements of energy harvesting systems. Transducers that extract energy from the environment significantly differ from batteries in that their power output is limited. Besides, the energy source can be of variable nature and energy availability can be virtually infinite. Electronic systems that rely on energy harvesting sources have to be harvesting aware designed, both from the hardware and software perspectives. Proper Harvesting Aware Power Management (HAPM) should allow a harvesting system to operate indefinitely and within the expected operational utility. An energy neutral mode of operation can guarantee that the system will operate forever, but not that the utility meets the desired utility. This paper presents an introduction to HAPM, including topologies of Energy Harvesting Systems, the Energy Neutrality Principle and Power Management Techniques.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Citations31
Published2010
Admission routes1
Has abstractyes

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