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Record W4415349539 · doi:10.59934/jaiea.v5i1.1614

IoT-Based Ship Tank Leak Detection System Prototype to Enhance Security

2025· article· W4415349539 on OpenAlex
Naufal Hanif Mutawakil, Yusfrizal, Ratih Puspadini

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) · 2025
Typearticle
Language
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBuzzerMicrocontrollerALARMLeak detectionLeakVolume (thermodynamics)

Abstract

fetched live from OpenAlex

This research discusses the design and development of a Prototype Ship Tank Leak Detection System Based on the Internet of Things (IoT) to enhance safety and operational efficiency of ships. This system utilizes the ESP32 microcontroller connected to an ultrasonic sensor to detect changes in liquid volume within the tank. The detection results are displayed via an LCD and sent in real-time to the Blynk application on smartphones, accompanied by an alarm in the form of a buzzer as an early warning. The implementation results show that the system is capable of detecting leaks effectively and provides notifications quickly when there is a significant decrease in liquid volume.

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 categoriesMeta-epidemiology (narrow)
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 score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.267
Teacher spread0.255 · 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