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Record W1967217574 · doi:10.1109/iecon.2012.6389000

Battery and ultra-capacitor hybrid energy storage system and power management scheme for solar-powered Wireless Sensor Nodes

2012· article· en· W1967217574 on OpenAlexaff
Jordan Varley, Matthew Martino, Shahab Poshtkouhi, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnergy storageBattery (electricity)Energy harvestingPower managementElectrical engineeringCapacitorWireless sensor networkComputer scienceNode (physics)Computer data storageWirelessPower (physics)Sensor nodePhotovoltaic systemEmbedded systemElectronic engineeringEngineeringKey distribution in wireless sensor networksComputer hardwareVoltageWireless networkTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

This paper presents a Wireless Sensor Node (WSN) architecture with solar power generation and a hybrid energy storage scheme. The WSN is composed of three key modules: Energy Harvesting, Energy Storage, and the Control/Processing unit. The harvesting module consists of a miniature 179 mW solar array and MPPT hardware. A rechargeable 350 mAh Lithium-Ion battery and an ultra-capacitor are used as the energy storage elements. The low-ESR ultra-capacitor efficiently supplies the load power, which can reach as high as 295 mW peak, while the battery provides high-density storage. These elements are interfaced through a digitally controlled bi-directional dc-dc converter, which efficiently regulates the power-flow in the WSN. Multiple sensors and circuitry are implemented to measure positional and environmental data, as well as receiving and transmitting data via RF communication. A long-term test of the WSN is conducted to demonstrate the effective system functionality.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
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
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.185
Teacher spread0.177 · 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 teacher head, not a consensus.

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

Citations20
Published2012
Admission routes1
Has abstractyes

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