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Record W3207270703 · doi:10.17762/de.vi.5340

Using Internet of Things application for Monitoring Photo-Voltaic Panel Based on Ask Sensors Cloud

2021· article· en· W3207270703 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDesign Engineering · 2021
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsnot available
Fundersnot available
KeywordsCloud computingPhotovoltaic systemMicrocontrollerScope (computer science)Computer scienceInternet of ThingsData loggerThe InternetElectrical engineeringReal-time computingEmbedded systemTelecommunicationsSystems engineeringEngineeringWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

Internet of Things (IOT) devices and monitoring systems are essential for the accurate, reliable, and proper functioning of photovoltaic (PV) solar energy systems. The data acquisition and monitoring system ensure adequate operation and ascertain system malfunctions before they become significant problems. The IOT integrates ordinary items using modern technology in the physical scope. It is all feasible to utility voltage, current, temperature, and humidity sensors. The current research focuses on developing a cost-effective, accessible, and steadfast data monitoring system. A microcontroller-based data logger is stored in a cloud. The data can be downloaded directly from the website using the IoT technique because photovoltaic (PV) panels are built-in faraway locations and humans cannot reach them. The suggested system demonstrates the online utilization of solar PV power.

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 categoriesnone
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 score0.559
Threshold uncertainty score0.656

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 head0.053
GPT teacher head0.249
Teacher spread0.196 · 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