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Record W2809049549 · doi:10.1061/9780784480311.005

Tracking of “Smart” Debris Location Based on the RFID Technique

2017· article· en· W2809049549 on OpenAlexafffund
Nils Goseberg, Ioan Nistor, Jacob Stolle

Bibliographic record

VenueCoastal Structures and Solutions to Coastal Disasters 2015 · 2017
Typearticle
Languageen
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsUniversity of Ottawa
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of New South WalesUniversity of Ottawa
KeywordsComputer scienceLocation trackingTracking (education)DebrisComputer securityReal-time computingGeographyMeteorology

Abstract

fetched live from OpenAlex

Determining the location of floating or partially submerged objects during an extreme hydrodynamic event is an important task the investigation of impact loading resulting from debris impacts. This study investigates the application of a novel tracking system which is based on the radio frequency identification (RFID) technology and exploits the measured angles of arrival and time of arrival of radio waves used to locate an object in space. The system is deployed in a carefully controlled laboratory environment to analyze the performance and accuracy of the system. The standard error and standard deviation are used as metrics for the system’s performance. During testing, the system is subjected to linear and oscillatory motions. Good accuracy and repeatability is found for the tests conducted; however, a number of factors can compromise its accuracy and precision, such as a cluttered environment exhibiting solid obstacles, protruding walls or other disturbing items in the tracking area. In hydraulic and coastal engineering, this RFID technology has significant potential for use in laboratory investigations involving not only the tracking of debris but also in tracking the elements of the coastal structures’ armor layers and also for locating and recording of vessel motions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.547

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 studies0.0010.000
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 head0.017
GPT teacher head0.243
Teacher spread0.227 · 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 designObservational
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

Citations1
Published2017
Admission routes2
Has abstractyes

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