论文标题
基于模糊 - 邻骨分析的QR分解，改善家庭设备识别
Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition
论文作者
论文摘要
本文提出了一种新的设备识别方案，它通过引入一种新的方法来提取高度歧视性特征集，可以大大区分各种设备足迹。在这种情况下，根据基于模糊的邻域分析分析（FNPA-QR），一种精确而强大的特征投影技术应用于提取的能源消耗时域特征。 FNPA-QR旨在减少类特征之间的距离，并增加不同类别特征之间的差距。随后，一个新颖的行李决策树（BDT）分类器还旨在进一步提高分类精度。然后，在三个设备能量消耗数据集上验证了所提出的技术，这些数据集以低频和高频收集。获得的实际结果指出了基于时间域的FNPA-QR和BDT的未偿分类率。
This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT.
