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Record W2801173680 · doi:10.1109/access.2018.2831911

A UAV Detection Algorithm Based on an Artificial Neural Network

2018· article· en· W2801173680 on OpenAlexaff
Hao Zhang, Conghui Cao, Lingwei Xu, T. Aaron Gulliver

Bibliographic record

VenueIEEE Access · 2018
Typearticle
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsUniversity of Victoria
FundersGovernment of Shandong ProvinceNatural Science Foundation of Shandong ProvinceFundamental Research Funds for the Central UniversitiesQingdao Municipal Science and Technology BureauChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsChinaWork (physics)Foundation (evidence)Natural scienceChinese academy of sciencesLibrary scienceArtificial neural networkComputer scienceEngineeringEngineering managementOperations researchArtificial intelligencePolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

This work was supported in part by the National Natural Science Foundation of China under Grant 61701462 and Grant 41527901, in part by the Qingdao National Laboratory for Marine Science and Technology under Grant 2017ASKJ01, in part by the Qingdao Science and Technology Plan under Grant 17-1-1-7-jch, in part by the Fundamental Research Funds for the Central Universities under Grant 201713018, in part by the Shandong Province Natural Science Foundation under Grant ZR2017BF023, in part by the China Postdoctoral Science Foundation funded project under Grant 2017M612223, and in part by the Shandong Province Postdoctoral Innovation Project under Grant 201703032.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.063
GPT teacher head0.324
Teacher spread0.261 · 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 designSimulation or modeling
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

Citations93
Published2018
Admission routes1
Has abstractyes

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