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Record W4220842920 · doi:10.18280/i2m.210105

Design and Measurement of a Modern Charging System Based on IoT

2022· article· en· W4220842920 on OpenAlexvenueno aff
Ahmed M. Saheb, Bashar Sakeen Farhan

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Internet of ThingsComputer scienceCloud computingElectric power systemMobile deviceElectrical engineeringPower (physics)Embedded systemReal-time computingEngineeringOperating system

Abstract

fetched live from OpenAlex

This paper presents a modern charging system to improve the reliability of locating the closest and available free charging slot to charge low-power smart-devices. Also, the presented system is based on the cloud and the used network is based on the internet of things technology. The basic idea of the charging system is to provide a public charging place for all individuals who wish to charge their smart devices when it is close to running out. First and foremost, the charging system was designed and implemented to have multiple power sources in the event that one of the system's power sources failed. Secondly, the charging system provides a special smart mobile application that has also been designed and implemented, which allows the user to know the locations of the charging systems on a map and choose the nearest available system near the user. After selecting the nearest charging system, the user will be able to know whether or not there is an available charging port and the number of associated devices in each system. The study successfully built this system in practical life.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.219
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

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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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