2,700 research outputs found
A generic holonic control architecture for heterogeneous multi-scale and multi-objective smart microgrids
Designing the control infrastructure of future “smart” power grids is a challenging task. Future grids will integrate a wide variety of heterogeneous producers and consumers that are unpredictable and operate at various scales. Information and Communication Technology (ICT) solutions will have to control these in order to attain global objectives at the macrolevel, while also considering private interests at the microlevel. This article proposes a generic holonic architecture to help the development of ICT control systems that meet these requirements. We show how this architecture can integrate heterogeneous control designs, including state-of-the-art smart grid solutions. To illustrate the applicability and utility of this generic architecture, we exemplify its use via a concrete proof-of-concept implementation for a holonic controller, which integrates two types of control solutions and manages a multiscale, multiobjective grid simulator in several scenarios. We believe that the proposed contribution is essential for helping to understand, to reason about, and to develop the “smart” side of future power grids
Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
Cortical plasticity is one of the main features that enable our ability to
learn and adapt in our environment. Indeed, the cerebral cortex self-organizes
itself through structural and synaptic plasticity mechanisms that are very
likely at the basis of an extremely interesting characteristic of the human
brain development: the multimodal association. In spite of the diversity of the
sensory modalities, like sight, sound and touch, the brain arrives at the same
concepts (convergence). Moreover, biological observations show that one
modality can activate the internal representation of another modality when both
are correlated (divergence). In this work, we propose the Reentrant
Self-Organizing Map (ReSOM), a brain-inspired neural system based on the
reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose
and compare different computational methods for unsupervised learning and
inference, then quantify the gain of the ReSOM in a multimodal classification
task. The divergence mechanism is used to label one modality based on the
other, while the convergence mechanism is used to improve the overall accuracy
of the system. We perform our experiments on a constructed written/spoken
digits database and a DVS/EMG hand gestures database. The proposed model is
implemented on a cellular neuromorphic architecture that enables distributed
computing with local connectivity. We show the gain of the so-called hardware
plasticity induced by the ReSOM, where the system's topology is not fixed by
the user but learned along the system's experience through self-organization.Comment: Preprin
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