Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method
Abstract Considering that existing device-based occupant detection methods cannot count those who do not carry a device, in this paper, for buildings where the behaviour of the occupants tends to be regular, taking the WiFi-based occupant detection method as a basis, we propose a p-persistent frequent itemsets with 1-right-hand-side (RHS)-based occupant detection algorithm to improve the occupant detection performance in terms of accuracy. Association analysis using apriori algorithm is utilized to predict the occupancy of buildings through mining the relationships among occupants. We mathematically prove the reasonability of frequent itemsets with 1-RHS chosen in our algorithm and show the experimental results of applying this approach with different p. The results show that our proposed method can improve the accuracy performance in that it can see the occupant in buildings that the WiFi-based occupant detection method cannot see.
A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method
Abstract Considering that existing device-based occupant detection methods cannot count those who do not carry a device, in this paper, for buildings where the behaviour of the occupants tends to be regular, taking the WiFi-based occupant detection method as a basis, we propose a p-persistent frequent itemsets with 1-right-hand-side (RHS)-based occupant detection algorithm to improve the occupant detection performance in terms of accuracy. Association analysis using apriori algorithm is utilized to predict the occupancy of buildings through mining the relationships among occupants. We mathematically prove the reasonability of frequent itemsets with 1-RHS chosen in our algorithm and show the experimental results of applying this approach with different p. The results show that our proposed method can improve the accuracy performance in that it can see the occupant in buildings that the WiFi-based occupant detection method cannot see.
A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method
Wang, Ping (Autor:in) / Cao, Huaqian (Autor:in) / Chen, Si (Autor:in) / Li, Jiake (Autor:in) / Tu, Chang (Autor:in) / Zhang, Zhenya (Autor:in)
01.01.2019
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm
| British Library Conference Proceedings | 2012
A Classification-Based Occupant Detection Method for Smart Home Using Multiple-WiFi Sniffers
| Springer Verlag | 2019
| British Library Conference Proceedings | 2011
Building the Data Warehouse of Frequent Itemsets in the DWFIST Approach
| British Library Conference Proceedings | 2005