Optimization of home automation systems based on human motion and behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the reduction in cost and power supply of wireless systems along with the increasing demand for conserving energy when controlling consumer electronics and home appliances, smart home automation systems are more popular than ever before. A home automation system designed for reducing electricity consumption typically uses different sensors located in different areas of the house that communicate with a process unit to control the lights, HVAC system, consumer electronics, etc., so that the process unit turns these systems on only when needed. Additionally, other automated tasks may include setting the HVAC to an energy saving setting while the house is unoccupied, and restoring the normal setting when an occupant is about to return. To optimize current home automation systems, it is proposed that by considering the behavior of the residents inside a house, the power consumed on a daily basis will be significantly reduced. Such a power reduction could be achieved by both the sensors that monitor the motions of the residents inside a house and the adaptive control system that promptly adjusts itself to the most efficient level to further reduce electricity consumption based on different actions, habits and lifestyle of the residents.
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.
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 it