Analysis of a new method of measurement and visualization of indoor conditions by infrared thermography
Bibliographic record
Abstract
We present a new tool of measurement and visualization of the indoor ambient parameters (air temperature, air speed, and mean radiant temperature) by infrared thermography. The theoretical fundamentals are discussed. They include a design stage, the mathematical modeling, the measurement procedure, and the performance evaluation through error analysis. The measuring system consists of a set of auxiliary devices (targets) arranged on a measurement grid. An infrared camera measures their temperature histories. From each temperature chronology, the three ambient parameters are locally deduced together by solving an inverse heat transfer problem. The results are then mapped to provide a 2D or 3D visualization. The question of identifiability is addressed leading to a robust parameter estimation algorithm. The robustness of the algorithm is tested for a wide range of noisy data during a numerical experiment. The numerical data are built by varying the air speed from 0 ms(-1) to 2 ms(-1) with a step of 0.2 ms(-1), the air temperature from 15 °C to 30 °C with a step of 3 °C and the mean radiant temperature from 15 °C to 30 °C with a step of 3 °C. It appears that stability and repeatability are guaranteed by the presented method for the range of ambient parameters and accuracy usually found and needed in indoor conditions. Brief illustrative experimental results are given as an initial validation of the method. Since the spatial distributions of these ambient parameters are obtained qualitatively and quantitatively, the method is suitable for indoor microclimate mapping, visualization of air patterns, building inspection, thermal comfort assessment, etc.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".