Experimental Study for Efficient Use of RFID in Construction
Bibliographic record
Abstract
Experimental Study for Efficient Use of RFID in Construction Ali Montaser, Roya Azram, Osama Moselhi Pages 618-625 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: This paper presents an experimental study conducted to facilitate the use of RFID on construction job sites. The study focuses on deployment settings to provide data acquisition with higher accuracy for indoor location sensing. It provides extension to the state-of-the-art in this field as it addresses the impact of metal media proximity to RFID tags, the reasonable duration for data capturing, number of RFID tags employed and the distance between them. Low cost passive RFID tags were used in the experiments where each tag is used as a reference point with a known location. Five hundred and forty (514) experiments were conducted in lab environment using a 3m by 3m test bed that is dynamically rearranged to generate 15 test beds and a total of 67713 data sets were collected and analyzed. The collected data were captured from nine locations for each test bed at four time intervals. Received Signal Strength Indicator (RSSI) was used as the main attribute for signal measurement to process the captured data. Results of the data analysis performed are studied under four main categories: duration, number of tags, locations of tags and metal interference. The best duration was found to be the 15 second in the test bed with the least number of tags; as the short amount of time to capture data did not allow creation of a lot of interference among the emitted signals. Within each test bed, errors occurred most at points where the received signals were not well distributed in a 360 degree vicinity of the data capturing point. It means that the center point of each test bed resulted in lowest errors and the points located on the extremities led to the highest errors. Finally, metal objects were found to have major impact on the accuracy of the captured data; to the level where reliable values for errors could not be calculated in the test beds attached to metal objects. In summary, the results of the experimental study and related findings are expected to provide guidelines to the users of RFID technology for localization in building construction. Keywords: RFID, Indoor location sensing, Experimental study, Deployment settings, RSSI, Proximity method DOI: https://doi.org/10.22260/ISARC2013/0067 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".