Enhanced Localization for Indoor Construction
Why this work is in the frame
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Bibliographic record
Abstract
Considerable research work had been conducted in recent years embracing the utilization of wireless technologies in construction with a focus on identification of locations of material, equipment and personnel. A fundamental key for reliable and accurate use of these technologies is path loss models, which are used to estimate distances based on received signal strength (RSSI). This paper introduces a newly developed path loss model accounting for signal de-noising using a Kalman filter. The developed model is tested using four wireless technologies (WLAN, Bluetooth, Zigbee and Synapse SNAP), 20 experiments were carried out in laboratory environment and 1500 data sets were analyzed to investigate the accuracy of distance estimation. The results show an average of 50% enhancement in the distance estimation accuracy, which considered a potential for enhanced localization on indoor construction jobsites.
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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 it