Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine

The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. For non-intrusive occupancy analysis, multiple types of sensors can be installed to collect data based on which the consumer occupancy can be learned. However, the overall system cost will be increased as a result. Therefore, this research proposes a cheap and lightweight machine learning approach to predict the energy consumer occupancy based solely on their electricity consumption data. The proposed approach employs a support vector machine (SVM), in which different kernels are used and compared, including positive semi-definite and conditionally positive definite kernels. Efficiency of the proposed approach is depicted by different performance indexes calculated on simulation results with a realistic, publicly available dataset. Among SVM models with different kernels, those with Gaussian (rbf) and sigmoid kernels have the highest performance indexes, hence they may be most suitable to be used for residential energy consumer occupancy prediction.

Learning DOM Trees of Web Pages by Subpath Kernel and Detecting Fake e-Commerce Sites

The subpath kernel is a class of positive definite kernels defined over trees, which has the following advantages for the purposes of classification, regression and clustering: it can be incorporated into a variety of powerful kernel machines including SVM; It is invariant whether input trees are ordered or unordered; It can be computed by significantly fast linear-time algorithms; And, finally, its excellent learning performance has been proven through intensive experiments in the literature. In this paper, we leverage recent advances in tree kernels to solve real problems. As an example, we apply our method to the problem of detecting fake e-commerce sites. Although the problem is similar to phishing site detection, the fact that mimicking existing authentic sites is harmful for fake e-commerce sites marks a clear difference between these two problems. We focus on fake e-commerce site detection for three reasons: e-commerce fraud is a real problem that companies and law enforcement have been cooperating to solve; Inefficiency hampers existing approaches because datasets tend to be large, while subpath kernel learning overcomes these performance challenges; And we offer increased resiliency against attempts to subvert existing detection methods through incorporating robust features that adversaries cannot change: the DOM-trees of web-sites. Our real-world results are remarkable: our method has exhibited accuracy as high as 0.998 when training SVM with 1000 instances and evaluating accuracy for almost 7000 independent instances. Its generalization efficiency is also excellent: with only 100 training instances, the accuracy score reached 0.996.

Harmonic analysis of network systems via kernels and their boundary realizations

<p style='text-indent:20px;'>With view to applications to harmonic and stochastic analysis of infinite network/graph models, we introduce new tools for realizations and transforms of positive definite kernels (p.d.) <inline-formula><tex-math id="M1">\begin{document}$ K $\end{document}</tex-math></inline-formula> and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of factorizations: (i) Probabilistic: Starting with a positive definite kernel <inline-formula><tex-math id="M2">\begin{document}$ K $\end{document}</tex-math></inline-formula> we analyze associated Gaussian processes <inline-formula><tex-math id="M3">\begin{document}$ V $\end{document}</tex-math></inline-formula>. Properties of the Gaussian processes will be derived from certain factorizations of <inline-formula><tex-math id="M4">\begin{document}$ K $\end{document}</tex-math></inline-formula>, arising as a covariance kernel of <inline-formula><tex-math id="M5">\begin{document}$ V $\end{document}</tex-math></inline-formula>. (ii) Geometric analysis: We discuss families of measure spaces arising as boundaries for <inline-formula><tex-math id="M6">\begin{document}$ K $\end{document}</tex-math></inline-formula>. Our results entail an analysis of a partial order on families of p.d. kernels, a duality for operators and frames, optimization, Karhunen–Loève expansions, and factorizations. Applications include a new boundary analysis for the Drury-Arveson kernel, and for certain fractals arising as iterated function systems; and an identification of optimal feature spaces in machine learning models.</p>

Channel Identification of Non-linear Systems with Binary-Valued Output Observations Based on Positive Definite Kernels

Nowadays, the kernel methods are increasingly developed, they are a significant source of advances, not only in terms of computational cost but also in terms of the obtained efficiencies in solving complex tasks, they are founded on the theory of reproducing kernel Hilbert spaces (RKHS). In this paper, we propose an algorithm for recursive identification of finite impulse response (FIR) nonlinear systems, whose outputs are detected by binary value sensors. This algorithm is based on a nonlinear transformation of the data using a kernel function. This transformation performs a basic change that allows the data to be projected into a new space where the relationships between the variables are linear. To test the accuracy of the proposed algorithm, we have compared it with another algorithm proposed in the literature, for that, we employ the practical frequency selective fading channel, called Broadband Radio Access Network (BRAN). Monte Carlo simulation results, in noisy environment and for various data length, demonstrate that the proposed algorithm can give better precision.