Bibliographic record
Abstract
We propose TEA-bot (Thermography Enabled Autonomous Robot) to address the problem of labor-intensive manual inspections of thermal leaks in Heating Ventilation and Air Conditioning (HVAC) systems while avoiding installation of sensor networks, which can be challenging and expensive for existing buildings. TEA-bot is an Unmanned Ground Vehicle (UGV) designed to navigate in ceilings using visual-based Simultaneously Localization And Mapping (SLAM) while detecting thermal leaks from HVAC systems using Convolutional Neural Networks (CNN). TEA-bot uses inexpensive 3D printed parts as the bones, a single-board computer (SBC) as the brain, an RGB-D camera as the eyes, and a thermal camera as the leak detector. We build an in-lab ceiling environment as a true-to-size testing site with five types of common leaks in HVAC systems (e.g., improper connection) to test the feasibility and effectiveness of TEA-bot. Results show that TEA-bot can 1) generate high-resolution ceiling maps in the format of point clouds for existing buildings without Building Information Models (BIMs), with an average error of 1.56 inches in duct length measurements; 2) identify leak types with a precision of 86.60% using the thermal camera images; and 3) register leaks onto the point cloud maps, providing a holistic and intuitive view of leak locations in an unknown ceiling environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".