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Record W4403905610 · doi:10.59934/jaiea.v4i1.631

Design of an Automatic Trash Can Wheel Robot with Bluetooth Navigation Control Through Smartphone Application

2024· article· en· W4403905610 on OpenAlexaff
Irfan Yusuf, Yani Maulita, Rusmin Saragih

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBluetoothComputer scienceRobotEmbedded systemControl (management)Human–computer interactionReal-time computingSimulationArtificial intelligenceWirelessTelecommunications

Abstract

fetched live from OpenAlex

Environmental cleanliness is an important aspect that must be maintained, especially in this modern era. The use of appropriate technology can help increase awareness of cleanliness, one of which is by developing automatic trash cans. This research designs and builds a mobile robot in the form of an automatic trash can that can be controlled via an Arduino Uno microcontroller using Bluetooth and controlled via a smartphone application. This research designs a mobile robot in the form of an automatic trash can that can be controlled via an Arduino Uno microcontroller with Bluetooth and a smartphone application. The system receives navigation commands from the app and uses ultrasonic sensors to detect objects in front of the trash can. The microcontroller processes the command and sensor data to drive the DC motor and servo motor. The servo motor opens and closes the trash can lid automatically when an object is detected at a certain distance. The test results show that this robot can move according to instructions from a smartphone and successfully open and close the trash can lid automatically when detecting objects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.239
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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