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Record W4387364801 · doi:10.59934/jaiea.v3i1.308

Design of an Automatic Water Faucet System Using the IOT Based HC-SR04 Sensor

2023· article· en· W4387364801 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
KeywordsMicrocontrollerInternet of ThingsComputer scienceContext (archaeology)Container (type theory)ArduinoSoftwareNode (physics)RelayEmbedded systemEngineeringOperating system

Abstract

fetched live from OpenAlex

Water is a vital element for humans, many activities are related to water. In everyday life, the use of water becomes very important and necessary in various fields, such as households, industry and agriculture. But their use is often inefficient and has the potential to waste valuable resources. In this context, the concept of the Internet of Things (IoT) emerges as a potential solution by connecting physical objects via the internet. This research designs and builds an IoT-based automatic water faucet system using the HC-SR04 sensor to measure the water level in a container. Hardware components such as Node mcu Esp8266, Infrared Sensor, 2 channel 5v Relay, and others are used to control the system automatically. The software used includes the Arduino IDE. This system aims to intelligently monitor and control water flow, prevent water wastage, and incorporate the advantages of IoT technology to create an automatic water faucet system that is responsive to water levels and the presence of objects in front of it.

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)0.001
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: none
Teacher disagreement score0.867
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.266
Teacher spread0.219 · 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