Indoor Localization of RFID-Equipped Movable Assets Using Mobile Reader Based on Reference Tags Clustering
Bibliographic record
Abstract
Indoor Localization of RFID-Equipped Movable Assets Using Mobile Reader Based on Reference Tags Clustering A. Motamedi, M. M. Soltani, A. Hammad Pages 626-634 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities and to deliver personalized and location-based services. Radio Frequency Identification (RFID) based systems, on the other hand, have been widely used in different applications in construction and maintenance. This paper investigates the usage of RFID technology for indoor localization of RFID equipped movable assets during the operation phase of facilities. The location-related data on RFID tags attached to fixed assets are extracted from a Building Information Model (BIM) and can provide context-aware information inside the building which can improve Facilities Management (FM) processes. The paper proposes a new approach to use received signals from available reference tags in the building attached to fixed assets to locate movable assets. The approach uses signal pattern matching and clustering algorithms for localization. As a result, a user equipped with an RFID reader is able to estimate the location of target assets, without having access to any Real-Time Location System (RTLS) infrastructure. A case study is performed to demonstrate the feasibility of proposed methods. Keywords: Asset localization, RFID, Location-based services, Pattern matching, Clustering DOI: https://doi.org/10.22260/ISARC2013/0068 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".