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Record W4403905455 · doi:10.59934/jaiea.v4i1.634

Design and Build Automatic Fan Control Based Internet of Things

2024· article· en· W4403905455 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) · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsInternet of ThingsComputer scienceControl (management)Fan-outThe InternetWorld Wide WebComputer securityAdvertisingBusinessTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

This research aims to create and develop an automatic fan control system based on the Internet of Things (IoT) that can regulate the room temperature well. The NodeMCU ESP8266 serve as a microcontroller for this system. The DHT22 sensor detects temperature, the L298N Driver Motor controls the speed of the 12V DC fan, and the 16x2 LCD displays temperature information in realtime. When the room temperature exceeds the threshold, the fan will automatically turn on and off when the temperature returns to normal. The Blynk app also allows users to monitor and control fans through smartphones. It allows users to manually control the fan and monitor it remotely. The results of the study show that this system works well and improves user comfort by managing the room temperature. The system also offers high flexibility with IoT integration, allowing for further development for wider applications in home automation and energy management.

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 score0.971
Threshold uncertainty score0.482

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 head0.015
GPT teacher head0.237
Teacher spread0.223 · 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