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This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based...
Models were developed to classify six different behaviours for a group of seven steers fitted with an accelerometer and pressure sensor. As part of the process, a greedy feature selection method was used to identify the most discriminatory inputs from a diverse set of statistical, spectral and information theory based features. The study showed the second order statistic features (standard deviation...
This paper deals with online detection and accommodation of outliers in transient time series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine technique together with a sliding window-based learning algorithm. A modification to this method is proposed so as to extend its application to transient raw data collected from transmitters attached...
A new algorithm for the estimation of stride interval time series from foot gait signals is proposed. The algorithm is based on the detection of beginning of heel strikes in the signal by using the support vector machine. Morphological operations are used to enhance the accuracy of detection. By taking backward differences of the detected beginning of heel strikes, stride interval time series is estimated...
Recent years, a variety of infrastructure-mediated sensing methods have been proposed to recognize activities of daily living (ADLs). However, due to the inconvenience such as high-cost, difficult-to-install, intrusive and applicable to the house with specific architectures alone, existing water-use activity recognition methods cannot be widely used into people's houses. In this paper, a single-point...
An improved least squares support vector machines (LS-SVM) was proposed to improve the sparse and robust performance of LS-SVM in the small samples prediction. The sparse and robust performance could be improved through adding elements of weighted LS-SVM and robust LS-SVM. We introduced a contrast experiment for ATE parameters prediction control through the three methods of neural network, LS-SVM...
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