Enhancing Cluster-based RFID Tag Localization using artificial neural networks and virtual reference tags
Bibliographic record
Abstract
Indoor/outdoor localization has gained importance as it has the potential to improve various processes related to the resource management of construction projects and to deliver personalized and location-based services (LBS). Radio Frequency Identification (RFID) based systems, have been widely used in different applications in construction and maintenance. This paper investigates the usage of active RFID technology for the localization of movable objects (e.g. material, equipment, tools, and assets) equipped with RFID tags using handheld readers. The method builds on Cluster-based Movable Tag Localization (CMTL) technique which uses k-Nearest Neighbor (k-NN) algorithm. CMTL uses multidimensional clustering technique that considers signal pattern similarity between target and reference tags together with spatial distribution of reference tags for detecting the region where the target tag is located. This paper proposes applying an irregular bilinear interpolation method to form a grid of virtual reference tags within the selected cluster of real reference tags. Moreover, the proposed method uses artificial neural networks (ANN) for positioning the target tag, as opposed to empirical weighted averaging formulas used in similar k-NN based methods. Comparative analysis is performed to quantify the improvement of the proposed method over similar k-NN-based methods using a simulation environment. A case study is performed to analyze the performance of the proposed method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".