Smart Building Management Application: Unsupervised First and Second-Order Moments Domain Adaptation for Occupant Behaviour Prediction Using IoT Sensor Data
Bibliographic record
Abstract
Occupancy estimation (OE) and activity recognition (AR), predicted using Internet of Things (IoT) sensor data, are among the most important outcomes of artificial intelligence (AI) applied to buildings. The unavailability of enough labeled data is a serious problem for building researchers which is why they came up with domain adaptation (DA). Unsupervised domain adaptation (UDA) solves the problem of the unavailability of labeled data in target domains. In this research, we present a novel application for OE and AR that considers UDA methods. We have adapted 3 UDA methods called Joint Adaptation Networks (JAN), Deep Adaptation Networks (DAN), and Deep Correlation Alignment (D-CORAL). Our research application offers a practical solution for smart building management, enabling accurate OE and AR using adapted UDA techniques.
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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".