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Record W3042328353 · doi:10.35940/ijeat.c6054.029320

Underwater Object Localization using the Spinning Propeller Noise of Ships Based on the Wittekind Model

2020· article· en· W3042328353 on OpenAlexaff
Mojgan Mirzaei Hotkani, Seyed Alireza Seyedin, Jean‐François Bousquet

Bibliographic record

VenueInternational Journal of Engineering and Advanced Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsUnderwaterAcousticsPropellerHydrophoneNoise (video)Reflection (computer programming)SonarAnechoic chamberComputer scienceNarrowbandMultipath propagationChannel (broadcasting)Marine engineeringEngineeringGeologyPhysicsComputer visionTelecommunications

Abstract

fetched live from OpenAlex

The purpose of this article is to localize underwater objects based on the noise reflection of the propeller rotation in cavitation mode. In the proposed method, the propeller noise, which plays the role of pings in active sonar, is modeled by the Wittekind method. As such, an echo is continuously received by a vertical and uniform linear hydrophone array due to reflection from the underwater targets. The challenges associated with the underwater channels are simulated by the ocean model in COMSOL. Specifically, to model the propagation of underwater acoustic in this channel, the Helmholtz equation is solved using COMSOL. Finally, localization is performed by comparing the Delay & Sum algorithm and the multiple signal classification (MUSIC) algorithm in MATLAB. According to the simulation results, the proposed method is able to detect the position of the target and the propeller approximately, although the multipath phenomenon causes adverse effects on the results. The narrowband MUSIC algorithm is used in the proposed method at the frequency of the strongest intensity.

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: none
Teacher disagreement score0.767
Threshold uncertainty score0.212

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 head0.024
GPT teacher head0.230
Teacher spread0.206 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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