A real-time occupancy detection system for unoccupied, normally and abnormally occupied situation discrimination via sensor array and cloud platform in indoor environment
| Autor: | Changhao Feng, Shaohua Yang, Cong Wang, Xu Ran, Bin Chen, Huang Zeqiong |
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| Rok vydání: | 2021 |
| Předmět: |
Occupancy
Computer science business.industry Real-time computing Metals and Alloys Cloud computing Feature selection Condensed Matter Physics Surfaces Coatings and Films Electronic Optical and Magnetic Materials Environmental data Running time Sensor array Electrical and Electronic Engineering business Instrumentation Energy (signal processing) Extreme learning machine |
| Zdroj: | Sensors and Actuators A: Physical. 332:113116 |
| ISSN: | 0924-4247 |
| Popis: | It is significant to detect the occupancy of indoor environment from the view of saving energy. In addition, occupied situation detection can help control the density of people in room to reduce the risk of disease transmission. In preliminary, we trained nine algorithm models on the existing occupancy dataset to select the optimum algorithm. Then, all-subsets regression model is used for feature selection (sensor contribution evaluation) to optimize the size of sensor array. As a result, the voting based weighted extreme learning machine (WV-ELM) model achieved the highest prediction accuracy and the combination of light and CO2 sensors could realize a satisfied classification result. Finally, a real-time occupancy detection system based on the sensor array and cloud platform was proposed. The system used the indoor environmental data collected by the sensor array. WV-ELM model was combined with the proposed system to detect the real-time occupied situation in the indoor environment. To verify their efficiency, the system was implemented in a laboratory to collect occupancy data for one week. According to the actual test results, the proposed system realized a detection accuracy of 97.32% with running time less than 30 s. |
| Databáze: | OpenAIRE |
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