Home appliances classification based on multi-feature using ELM

被引:4
|
作者
Liu, Qi [1 ]
Chen, Fangpeng [2 ]
Chen, Fenghua [3 ]
Wu, Zhengyang [4 ]
Liu, Xiaodong [5 ]
Linge, Nigel [6 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
[3] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Atmospher Environm Monitoring & P, Collaborat Innovat Ctr Atmospher Environm & Equip, Nanjing 210044, Jiangsu, Peoples R China
[4] South China Normal Univ, Coll Comp, Guangzhou, Guangdong, Peoples R China
[5] Edinburgh Napier Univ, Sch Comp, 10 Clinton Rd, Edinburgh EH10 5DT, Midlothian, Scotland
[6] Univ Salford, Sch Comp Sci & Engn, Salford M5 4WT, Greater Manches, England
关键词
feature extraction; smart home; data collection; WSN; wireless sensor network; ELM; extreme learning machine; smart socket; data analysis; EXTREME LEARNING-MACHINE; NEURAL-NETWORKS; SMART HOME; OPTIMIZATION;
D O I
10.1504/IJSNET.2018.10015979
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the development of science and technology, smart home has become a hot topic. And pattern recognition adapting to smart home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of extreme learning machine (ELM). At the same time, the sampling frequency with low rate is mentioned in this pa-per, which can obtain the required data through intelligent hardware directly, as well as reduce the cost of investment. Experiments in this paper show that the proposed method can accurately determine the using electrical appliances. And greatly improve the accuracy of identification, which can further improve the popularity of smart home.
引用
收藏
页码:34 / 42
页数:9
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