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Record W4387401647 · doi:10.59934/jaiea.v3i1.332

Design Of Electric Switching Systems For Electronic Equipment In Home Based Internet of Thing (IoT)

2023· article· en· W4387401647 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2023
Typearticle
Languageen
FieldComputer Science
TopicIoT-based Control Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMicrocontrollerComputer scienceAndroid (operating system)Internet of ThingsEmbedded systemElectronicsIncandescent light bulbComputer hardwareMobile deviceThe InternetElectrical engineeringOperating systemEngineering

Abstract

fetched live from OpenAlex

The tool for designing an IoT-based electronic equipment switch system has been designed. This system uses the NodeMCU ESP 8266 microcontroller where the NodeMCU ESP8266 functions as a data processor, and also as a receiver for wi-fi networks emitted by wi-fi network systems. This tool system uses a control system using an Android smartphone to turn on and off switches for electronic devices at home, this tool uses a Wi-Fi network communication system so that the device system and Android smartphone can be connected, in this tool system it uses a DC fan and incandescent lamps as outputs. connected to an electrical switch system, and the switch can turn on and turn off electronic devices on designed devices. The smartphone application used in this design is the Blynk application which can be downloaded at Playstore or Google.com. This tool is expected to help and facilitate humans in controlling electronic equipment at home both at home and when traveling.

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)0.002
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: none
Teacher disagreement score0.955
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.256
Teacher spread0.228 · 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