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Record W2753327067 · doi:10.1109/asicon.2017.8252678

Design and development of a self-contained and non-invasive integrated system for electricity monitoring applications

2017· preprint· en· W2753327067 on OpenAlexaff
Sid Zarabi, Egon Fernandes, Isabel Rua, Armaghan Salehian, Hélène Debéda, David Nairn, Lan Wei

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrocontrollerWireless sensor networkDuty cycleBluetoothElectricityWirelessEnergy harvestingNode (physics)Electrical engineeringSensor nodeInterface (matter)Computer scienceControl unitController (irrigation)VoltageEnergy (signal processing)Embedded systemWireless networkEngineeringKey distribution in wireless sensor networksTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

This paper reports the design and integration of a self-contained system intended for real-time monitoring of electricity usage within residential and commercial buildings. Consisting of an Energy Harvester (EH), an electric current sensor, a Micro-controller Unit (MCU), and a wireless communication device, the proposed system is self-powered and non-invasive, which provides a promising solution as a node in a wireless sensor network. In this work, a customized interface circuitry is designed to collect and regulate the energy from the EH. A Wireless MCU is programmed to acquire, process, and transmit the data from the sensor to the central hub via Bluetooth Low Energy connectivity. The real-time data obtained can be used to measure the amount of power consumed by individual appliances within the building. In this paper, the system design and testing results are reported. Attached to an electric wire carrying currents in the range of 7.6A to 30A, the state-of-the-art system can achieve a read-transmit duty cycle from <;1min to 2.5min. The unit is capable of reading a 60 HZ AC sensor signal with a peak voltage in the range of 100 mV to 900 mV with an accuracy of 91.4%.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.441
Threshold uncertainty score0.994

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 head0.030
GPT teacher head0.249
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2017
Admission routes1
Has abstractyes

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