Computer
Reduced feature-set based parallel CHMM speech recognition systems
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
Localization Site Prediction for Membrane Proteins by Integrating Rule and SVM Classification
IEEE Transactions on Knowledge and Data Engineering
Associated evolution of a support vector machine-based classifier for pedestrian detection
Information Sciences: an International Journal
IEEE Transactions on Information Theory
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof
IEEE Transactions on Consumer Electronics
Design and Implementation of Control Mechanism for Standby Power Reduction
IEEE Transactions on Consumer Electronics
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Multi-sensor data fusion using support vector machine for motor fault detection
Information Sciences: an International Journal
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
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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.