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Record W4382072433 · doi:10.59934/jaiea.v2i3.210

Distribution Of Lightweight File Delivery Using IoT

2023· article· en· W4382072433 on OpenAlexaff
Anggi Muhammad Tanjung, Akim Manaor Hara Pardede

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2023
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsVoltRobotMicrocontrollerAndroid (operating system)Computer scienceVoltageMotor controllerComputer hardwareEmbedded systemDC motorElectrical engineeringSimulationEngineeringReal-time computingPower (physics)Operating systemArtificial intelligence

Abstract

fetched live from OpenAlex

A lightweight file lifter car distribution device using Iot has been designed. This robot system uses the NodeMCU ESP8266 microcontroller where the NodeMCU ESP8266 functions as a data processor, and also a WI-FI network receiver emitted by a WI-FI network system. This robot car system uses a control system using an Android smartphone to control the movement of the wheels, this robot car uses a WI-FI network communication system so that the robot car system and Android smartphone can be connected, in this robot car system it uses a DC motor rotation direction drive driver which is The driver functions to move the direction of rotation of the DC motor on the wheels of the robot car. The power supply for this robot uses 3 3.7 Volt Li-Ion batteries which are arranged in series to get a 12 volt voltage, the 12 volt battery voltage goes first to the 7805 regulator IC circuit to get an output voltage of 5 volts, 5 volts voltage. this is what functions to supply the robot system so that it can be operated

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.244
Teacher spread0.217 · 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
Published2023
Admission routes1
Has abstractyes

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