Most real-life data is high-dimensional tensor format, which traditional machine learning methods based on vector and matrix cannot deal with directly and result in the curse of dimensionality problems. For the sake o...
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ISBN:
(纸本)9781665413015
Most real-life data is high-dimensional tensor format, which traditional machine learning methods based on vector and matrix cannot deal with directly and result in the curse of dimensionality problems. For the sake of addressing those issues, we develop a kernelized support tensor-ring machine (KSTRM) for high-dimensional tensorial data. Specifically, tensor-ring (TR) and kernel methods are utilized to the support vector machine (SVM). And we construct a TR decomposition based kernel function. Experiments are made on real-life tensorial datasets, which confirms the superiority of an STRM over the SVM, STuM and STTM.
This paper studies the static economic optimization problem of a system with a single aggregator and multiple prosumers in a Real-Time Balancing Market (RTBM). The aggregator, as the agent responsible for portfolio ba...
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Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of single-i...
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The paper develops new results on stability analysis and control law design for differential linear repetitive processes. These results are based on new dilated LMI characterizations for stability along the pass where...
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Modern data centers’ providers are gradually moving away from traditional and multi-vendor IT infrastructures to open, standardized and interchangeable solutions that are based on a software defined approach to manag...
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Motivated by the inadequacy of the existing control strategies for power systems affected by time-varying uncontrolled power injections such as loads and the increasingly widespread renewable energy sources, this pape...
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Now localization is one of the major issues in underwater environment work. In terrestrial application, time different of arrival (TDoA) localization algorithm has been widely used. However, most localization systems ...
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In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbo...
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In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.
In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considere...
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