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Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home

机译:普适智能家居中带有混合SVM / GMM分类器的多设备识别系统

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摘要

Ubiquitous computing provides convenient and fast information distribution service by using sensor nodes and wireless network, and a good household appliance recognition system will allow users to effectively understand the household appliance usage and develop habits of power preservation. At present, smart meters convert the information of traditional electric meters to easily accessible digital information, based on which, the household appliance recognition service can be carried out. However, it is different from video or audio recognition service, when a variety of electrical appliances run, they will all have individual impact on power consumption, thereby resulting in the difficulties in recognition. Presently, the complex current information arising from many household appliances also increases the difficulty in extracting power features. For addressing the challenge, this study proposes a set of multi-appliance recognition system, which designs a single smart meter using a current sensor and a voltage sensor in combination with a microprocessor to meter multi-appliances. After fuzzy processing of the power information are read through the smart meter and extraction of the power features, electric appliances are classified using the hybrid Support Vector Machine/Gaussian Mixture Model (SVM/GMM) classification model. GMM is mainly used describe the wave distribution situation according to the current information, so as to find the power similarity; while SVM is used to classify the power features of different electric appliances, so as to summarize the classification properties of different electric appliances and establish a classification model. Finally, the household appliances that are in use can be recognized with the household power supply terminal, and their information can be reported to users through wired or wireless network to achieve ubiquitous recognition service. This study has developed and implemented this system prototype, and is used to prove its design theory.
机译:普适计算通过使用传感器节点和无线网络提供便捷,快速的信息分发服务,良好的家用电器识别系统将使用户能够有效地了解家用电器的使用情况并养成节电的习惯。当前,智能电表将传统电表的信息转换为易于访问的数字信息,从而可以进行家用电器识别服务。但是,它不同于视频或音频识别服务,当运行各种电器时,它们都会对功耗产生个别影响,从而导致识别困难。当前,由许多家用电器产生的复杂的当前信息也增加了提取功率特征的难度。为了解决这一挑战,本研究提出了一套多设备识别系统,该系统设计了一个使用电流传感器和电压传感器并结合微处理器以计量多设备的智能电表。在通过智能电表读取了电力信息的模糊处理并提取了电力特征之后,使用混合支持向量机/高斯混合模型(SVM / GMM)分类模型对电器进行分类。 GMM主要用于根据当前信息描述波的分布情况,以找到功率相似度;支持向量机用于对不同电器的功率特性进行分类,以总结不同电器的分类特性,建立分类模型。最后,可以通过家用电源终端识别正在使用的家用电器,并可以通过有线或无线网络将其信息报告给用户,以实现普遍存在的识别服务。本研究开发并实现了该系统原型,并用于证明其设计理论。

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