CSI-Based Model for Precision Localization in Underground Mines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study demonstrates the application of Artificial Intelligence (AI), particularly through a TensorFlow-based Multi-Layer Perceptron (MLP), for enhanced localization in underground mining environments. An extensive experimental measurement campaign forms the basis of this work, which utilizes Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) metrics. The data, derived from a series of rigorous experimental procedures using ESP32 modules, informs the training of our AI model. This model proficiently deciphers the complex signal patterns found in underground settings. The employment of MLP stands out as an effective strategy to forge a data-driven model that delivers exceptional localization precision. Such accuracy is evident in both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios, signifying the substantial role AI can play in revolutionizing underground navigation and tracking systems. The insights gained from this research pave the way for the advent of advanced AI-powered localization solutions tailored for the complexities of underground environments.
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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