Zobrazeno 1 - 10 of 27 pro vyhledávání: '"Roberto Rocchetta"'
1
Exploring the link between scenario theory, complexity- and compression-based learning
Autor: F.A. Oliehoek, Mey Alexander, Roberto Rocchetta
This work investigates generalization error bounds that apply to general classes of learning problems and focuses on recently observed parallels between PAC-learning bounds, like compression and complexity-based bounds, and novel error guarantees der
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dafb5f1cad6f656cb3f96507f94bd90
https://doi.org/10.36227/techrxiv.21739397.v1
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2
Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm
Autor: Wenhua Zhao, Lechang Yang, Chao Dang, Roberto Rocchetta, Marcos Valdebenito, David Moens
Publikováno v: Mechanical Systems and Signal Processing, 171:108889. Academic Press Inc.
In practical engineering, experimental data is not fully in line with the true system response due to various uncertain factors, e.g., parameter imprecision, model uncertainty, and measurement errors. In the presence of mixed sources of aleatory and
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39edc876d6e991cd4cb56700c03d4d98
https://research.tue.nl/en/publications/bee04c5a-4122-4207-8bb3-5d5861ca9aa4
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3
Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics
Autor: Roberto Rocchetta
Publikováno v: Renewable and Sustainable Energy Reviews, 159:112185. Elsevier
Controlled islanding can enhance power grid resilience and help mitigate the effect of emerging failure by splitting the grid into islands that can be rapidly and independently recovered and managed. In practice, controlled islanding is challenging a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23d14e3850c81d645f42bcf3bb6bb6bf
https://research.tue.nl/en/publications/182098b7-8364-4ed2-b062-ec2159336582
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4
From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information
Autor: Enrique Miralles-Dolz, M. de Angelis, P.O. Hristov, Dominic Calleja, Ander Gray, Alexander Wimbush, Roberto Rocchetta
Publikováno v: Mechanical Systems and Signal Processing, 165:108210. Academic Press Inc.
Mechanical Systems and Signal Processing
In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient meth
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69ea3358a26a0651453ac3bac1b93c12
https://research.tue.nl/en/publications/12c1fd1e-30a9-4f9b-8683-8ec9018b7e74
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5
Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data
Autor: Qi Gao, Roberto Rocchetta, Milan Petkovic
Publikováno v: Mechanical Systems and Signal Processing, 161:107973. Academic Press Inc.
Interval Predictor Models (IPMs) offer a non-probabilistic, interval-valued, characterization of the uncertainty affecting random data generating processes. IPMs are constructed directly from data, with no assumptions on the distributions of the unce
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c8da883c9204f2fd1a34be99105e79a
https://research.tue.nl/nl/publications/6e3bc78c-f2a1-427d-9ac2-3aa8c72816ca
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6
A reinforcement learning framework for optimal operation and maintenance of power grids
Autor: Luca Bellani, Michele Compare, Roberto Rocchetta, Edoardo Patelli, Enrico Zio
Publikováno v: APPLIED ENERGY
Applied Energy
Applied Energy, Elsevier, 2019, 241, pp.291-301. ⟨10.1016/j.apenergy.2019.03.027⟩
We develop a Reinforcement Learning framework for the optimal management of the operation and maintenance of power grids equipped with prognostics and health management capabilities. Reinforcement learning exploits the information about the health st
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e753fb7ba18ed55573b35b517ada983
https://doi.org/10.1016/j.apenergy.2019.03.027
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9
A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids
Autor: Edoardo Patelli, Roberto Rocchetta
Publikováno v: RELIABILITY ENGINEERING & SYSTEM SAFETY
Reliability Engineering and System Safety, 197:106817. Elsevier
Risk-based power dispatch has been proposed as a viable alternative to Security-Constrained Dispatch to reduce power grid costs and help to better understand of prominent hazards. In contrast to classical approaches, risk-based frameworks assign diff
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4d65ba13dff25cb011899031d8432a1
http://www.scopus.com/inward/record.url?scp=85078656144&partnerID=8YFLogxK
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10
A scenario optimization approach to reliability-based design
Autor: Luis G. Crespo, Roberto Rocchetta, Sean P. Kenny
Publikováno v: Reliability Engineering and System Safety, 196:106755. Elsevier
This article introduces a scenario optimization framework for reliability-based design given a set of observations of uncertain parameters. In contrast to traditional methods, scenario optimization makes direct use of the available data thereby elimi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd1ef6d20f31336ac90a6d9deff9be03
https://doi.org/10.1016/j.ress.2019.106755
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