Supervised Analysis Dictionary Learning: Application in Consumer Electronics Appliance Classification

被引:1
作者
Bhattacharjee, P. [1 ]
Banerjee, S. [2 ]
Gulati, M. [1 ]
Majumdar, A. [1 ]
Ram, S. S. [1 ]
机构
[1] IIIT Delhi, New Delhi, India
[2] Xerox Res Ctr, Bengaluru, Karnataka, India
来源
PROCEEDINGS OF THE FOURTH ACM IKDD CONFERENCES ON DATA SCIENCES (CODS '17) | 2017年
关键词
Non-intrusive load monitoring; supervised learning; dictionary learning; LINEAR INVERSE PROBLEMS; LOAD DISAGGREGATION; ENERGY-CONSUMPTION; SPARSE; ALGORITHM;
D O I
10.1145/3041823.3041825
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The objective of this paper is to estimate if an electrical appliance is 'ON' based on their common mode electromagnetic (CM EMI) emissions. The assumption being that, a user by knowing the state of the appliance can make an informed decision whether to keep it running or switch it off to save power. Here, state estimation of a single appliance is formulated as a classification problem. A new technique called analysis dictionary learning is proposed to generate features from CM EMI. The proposed method outperforms feature extraction based on deep learning techniques as well as a state-of-the-art information theoretic feature extraction technique based on Conditional Likelihood Maximization.
引用
收藏
页数:10
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