Name
Abstract | ||
|---|---|---|
Presence detection is used in occupancy control to dynamically adjust energy-related appliances in smart building applications. Yet, practical applications typically suffer from high sensor unreliability. We propose a computationally efficient approach, based on Hidden Markov Models, to fuse sensor observations from multiple sensors to better estimate user state (presence/absence). Our model considers a realistic scenario, where sensor communication may be limited or unreliable, thus some sensor observations data may be missing for some intervals. Compared to state of art classifiers (Logistic Regression, Naïve Bayes, SVM), our approach achieves improved results while maintaining low computational and memory requirements or even relaxing these. Judging from our experiments, the algorithm appears to work well also in real-world test set-up where user presence and sensors error may not exactly follow our idealized model assumptions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CIT.2017.31 | 2017 IEEE International Conference on Computer and Information Technology (CIT) |
Keywords | Field | DocType |
Hidden Markov Model (HMM),multiple observations,erasure channel,missing observations | Naive Bayes classifier,Computer science,Support vector machine,Binary erasure channel,Real-time computing,Artificial intelligence,Building automation,Control system,Statistical classification,Hidden Markov model,Fuse (electrical),Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-0959-0 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charikleia Papatsimpa | 1 | 0 | 0.34 |
| jeanpaul m g linnartz | 2 | 152 | 49.53 |