324 research outputs found
Model Predictive Control meets robust Kalman filtering
Model Predictive Control (MPC) is the principal control technique used in
industrial applications. Although it offers distinguishable qualities that make
it ideal for industrial applications, it can be questioned its robustness
regarding model uncertainties and external noises. In this paper we propose a
robust MPC controller that merges the simplicity in the design of MPC with
added robustness. In particular, our control system stems from the idea of
adding robustness in the prediction phase of the algorithm through a specific
robust Kalman filter recently introduced. Notably, the overall result is an
algorithm very similar to classic MPC but that also provides the user with the
possibility to tune the robustness of the control. To test the ability of the
controller to deal with errors in modeling, we consider a servomechanism system
characterized by nonlinear dynamics
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
What Sequences obey Benford's Law ?
We propose a new necessary and sufficient condition to test whether a sequence is Benford (base-b) or not and apply this characterization to some kinds of sequences (re)obtaining some well known results, as the fact that the sequence of powers of 2 is Benford (base-10).Benford's law, equidistributed sequences, ergodic endomorphisms
The role of architectural and learning constraints in neural network models: A case study on visual space coding
The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems
Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance
With the advent of millimeter wave (mmWave) communications, the combination
of a detailed 5G network simulator with an accurate antenna radiation model is
required to analyze the realistic performance of complex cellular scenarios.
However, due to the complexity of both electromagnetic and network models, the
design and optimization of antenna arrays is generally infeasible due to the
required computational resources and simulation time. In this paper, we propose
a Machine Learning framework that enables a simulation-based optimization of
the antenna design. We show how learning methods are able to emulate a complex
simulator with a modest dataset obtained from it, enabling a global numerical
optimization over a vast multi-dimensional parameter space in a reasonable
amount of time. Overall, our results show that the proposed methodology can be
successfully applied to the optimization of thinned antenna arrays.Comment: 5 pages, 7 figures. This paper has been presented at EuCAP 2020.
Copyright IEEE 2020. Please cite it as: M. Lecci, P. Testolina, M. Rebato, A.
Testolin, and M. Zorzi, "Machine Learning-aided Design of Thinned Antenna
Arrays for Optimized Network Level Performance," 14th European Conference on
Antennas and Propagation (EuCAP 2020), Copenhagen, Mar. 202
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control
Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs)
in monitoring and remote area surveillance applications has become widespread
thanks to the price reduction and the increased capabilities of drones. The
drones in the swarm need to cooperatively explore an unknown area, in order to
identify and monitor interesting targets, while minimizing their movements. In
this work, we propose a distributed Reinforcement Learning (RL) approach that
scales to larger swarms without modifications. The proposed framework relies on
the possibility for the UAVs to exchange some information through a
communication channel, in order to achieve context-awareness and implicitly
coordinate the swarm's actions. Our experiments show that the proposed method
can yield effective strategies, which are robust to communication channel
impairments, and that can easily deal with non-uniform distributions of targets
and obstacles. Moreover, when agents are trained in a specific scenario, they
can adapt to a new one with minimal additional training. We also show that our
approach achieves better performance compared to a computationally intensive
look-ahead heuristic.Comment: Preprint of the paper published in IEEE Transactions on Cognitive
Communications and Networking ( Early Access
Smell and taste alterations in Covid-19: a cross-sectional analysis of different cohorts
Olfactory (OD) and gustatory (GD) dysfunction have been proven to be a typical symptom of SARS-CoV-2 infection. However, their prevalence in different patient populations still needs to be clarified
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