IoT-based Platform for an Air-to-Air Heat Exchanger Evaluation
Bibliographic record
Abstract
Applying to many commercial and residential applications, air-to-air heat/energy exchangers are extensively considered as one of the promising technologies for improving indoor air quality (IAQ), providing thermal comfort, and dwindling energy consumption costs as well. The present paper proposes an IoT-based platform to experimentally assess the performances of a heat recovery ventilator (HRV) system in terms of heat recovery and IAQ enhancement. To gather and log measurements, the developed IoT platform is integrated into the mechanical ventilation system without affecting its operation modes. For more than 12 months, the proposed IoT approach successfully collected and sent every 60 s, real-time measurements related to the indoor and outdoor air quality, with a focus on TVOC and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , and the related temperature, humidity, and pressure used to assess the system’s sensible heat recovery potential. Results from a real environment located in New Brunswick, Canada are presented, and the system performances are evaluated under extreme weather conditions. The developed IoT platform was flexible in terms of deployment and data exchange and proved to be efficient in collecting real-time data.
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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".