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Record W2910708815 · doi:10.1155/2019/1323105

Design and Analysis of Collaborative Unmanned Surface-Aerial Vehicle Cruise Systems

2019· article· en· W2910708815 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Advanced Transportation · 2019
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCruiseSoftware deploymentWeightingUnmanned surface vehicleCruise controlComputer scienceAeronauticsControl (management)SimulationAerospace engineeringEngineeringMarine engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

With the increasing application of unmanned surface vehicle-unmanned aerial vehicles (USV-UAVs) in maritime supervision, research on their deployment and control is becoming vitally important. We investigate the application of USV-UAVs for synergistic cruising and evaluate the effectiveness of the proposed collaborative model. First, we build a collaborative model consisting of the cruise vehicles and communication, detection, and command-and-control networks for the USV-UAV. Second, based on an analysis of the problems faced by collaborative USV-UAV systems, we establish a model to evaluate the effectiveness of such synergistic cruises. Third, we propose a weighting method for each evaluation factor. Finally, a model consisting of one UAV and four USVs is employed to validate our synergistic cruise model.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.288

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 head0.006
GPT teacher head0.223
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