étection et identification des activités quotidiennes dans les maisons intelligentes
Bibliographic record
Abstract
ans le cadre de ce projet de fin d’études, nous avons proposé d’utiliser deux méthodes de classification pour détecter les activités quotidiennes au sein des maisons intelligentes. Ceci dans le but est de connaitre la quantité d’électricité nécessaire pour répondre aux besoins des occupants de ces maisons intelligentes et éviter ainsi une production d’électricité qui dépasse leurs besoins c’est à dire créer une adéquation entre l’offre et la demande en terme de consommation d’électricité. Il s’agit de deux méthodes qui font parties des méthodes d’apprentissage automatique non supervisé : K-Means et DBSCAN. Les résultats obtenus sur un ensemble de données ont montré que DBSCAN fournit de bonnes performances comparée à K-Means sur une variété de distributions différentes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".