Tracking of “Smart” Debris Location Based on the RFID Technique
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".