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Tuning of PI compensators for integrating systems with large parametric uncertainty

Tuning of PI compensators for integrating systems with large...

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Pedro Mercader Alfonso Baños Dpt. Computer and Systems Engineering University of Murcia Murcia Spain

A simple PI tuning rule for integrating plus dead time (IPDT) systems, with large parametric uncertainty, is developed. In order to deal with parametric uncertainty a family of plants is considered. The design specifications are upper bounds on the sensitivity and the complementary sensitivity functions, that must be satisfied for any element belonging to the plants set. When applied to a system without uncertainty (or with no significant uncertainty) the well-known SIMC tuning rule is recovered. To conclude, several examples are analyzed to illustrate the proposed tuning rule.

关键词： Tuning Sensitivity Uncertainty Optimization Robustness Control design Transfer functions

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Bayesian classification and active learning using lp-priors. Application to image segmentation 22

Bayesian classification and active learning using lp-priors....

引用

22nd European Signal Processing Conference, EUSIPCO 2014

作者： Ruiz, Pablo De La Blanca, Nicolás Pérez Molina, Rafael Katsaggelos, Aggelos K. Dept. Ciencias de la Computación e I.A. Universidad de Granada Spain Dpt. of Electrical Engineering and Computer Science Northwestern University United States

ISBN: (纸本)9780992862619

In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p p-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between the prior model used and the independent Gaussian prior model is provided. The posterior probabilities are used to classify new samples and to define two Active Learning methods to improve classifier performance: Minimum Probability and Maximum Entropy. In the experimental section the proposed Bayesian framework is applied to Image Segmentation problems on both synthetic and real datasets, showing higher accuracy than state-of-the-art approaches. © 2014 EURASIP.

关键词： Image segmentation

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Learning filters in Gaussian process classification problems

Learning filters in Gaussian process classification problems

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作者： Ruiz, Pablo Mateos, Javier Molina, Rafael Katsaggelos, Aggelos K. Dpto. de Ciencia de la Computación e I.A. Universidad de Granada Spain Dpt. of Electrical Engineering and Computer Science Northwestern University United States

ISBN: (纸本)9781479957514

Many real classification tasks are oriented to sequence (neighbor) labeling, that is, assigning a label to every sample of a signal while taking into account the sequentiality (or neighborhood) of the samples. This is normally approached by first filtering the data and then performing classification. In consequence, both processes are optimized separately, with no guarantee of global optimality. In this work we utilize Bayesian modeling and inference to jointly learn a classifier and estimate an optimal filterbank. Variational Bayesian inference is used to approximate the posterior distributions of all unknowns, resulting in an iterative procedure to estimate the classifier parameters and the filterbank coefficients. In the experimental section we show, using synthetic and real data, that the proposed method compares favorably with other classification/filtering approaches, without the need of parameter tuning. © 2014 IEEE.

关键词： Classification (of information)

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Forecast Uncertainty in Procurement Decisions for Cloud Storage

Forecast Uncertainty in Procurement Decisions for Cloud Stor...

引用

International Conference on computer Modeling and Simulation, UKSIM

作者： Maurizio Naldi Dpt. of Computer Science and Civil Engineering Università di Roma Tor Vergata Roma Italy

In public vs. private solutions (i.e. Cloud vs. In-house, or leased vs. Owned) for storage, both alternatives have their pros and cons. Cloud storage can easily adapt to the company needs, but exhibits a higher unit cost than in-house solutions. On the other hand, if the company relies on its own storage equipment, it must periodically purchase it on the basis of forecasts, which may prove imprecise and lead to idle equipment. In this paper, we propose a comparative evaluation tool for the two procurement approaches, where the cloud can play the role of either exclusive storage medium or supplement to in-house equipment (in the case of underestimation of storage needs). The tool considers the impact of equipment acquisition intervals and forecast accuracy over a long time horizon, adopting a Geometric Brownian Motion model for the evolution of storage capacity needs, it can be employed as a decision support tool for procurement decisions.

关键词： Cloud computing Companies Procurement Market research Stochastic processes Mathematical model Computational modeling

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Interval Estimation of the Herfindahl-Hirschman Index under Incomplete Market Information

Interval Estimation of the Herfindahl-Hirschman Index under ...

引用

International Conference on computer Modeling and Simulation, UKSIM

作者： Maurizio Naldi Marta Flamini Dpt. of Computer Science and Civil Engineering Università di Roma Tor Vergata Roma Italy Faculty of Engineering Università Telematica Internazionale UNINETTUNO Roma Italy

An interval estimate is provided for the Herfindahl-Hirschman Index (HHI) when the knowledge about the market is incomplete, and we know just the largest market shares. Two rigorous bounds are provided for the HHI, without any further assumptions. Though the interval gets wider as the sum of the known market shares gets smaller, the estimate proves to be quite tight even when the fraction of the market that we do not know in detail is as high as 30%. This robustness is shown through three examples, considering respectively a set of real data and two sets of synthetic data, with the company sizes (a proxy for market shares) following respectively a Zipf law and a Pareto distribution.

关键词： Companies Upper bound Indexes Context Computational modeling Mobile communication

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BAYESIAN CLASSIFICATION AND ACTIVE LEARNING USING l_p-PRIORS. APPLICATION TO IMAGE SEGMENTATION

BAYESIAN CLASSIFICATION AND ACTIVE LEARNING USING l_p-PRIORS...

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European Signal Processing Conference

作者： Pablo Ruiz Nicolas Perez de la Blanca Rafael Molina Aggelos K. Katsaggelos Dept. Ciencias de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781479946037

In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p < 1, l_p-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between the prior model used and the independent Gaussian prior model is provided. The posterior probabilities are used to classify new samples and to define two Active Learning methods to improve classifier performance: Minimum Probability and Maximum Entropy. In the experimental section the proposed Bayesian framework is applied to Image Segmentation problems on both synthetic and real datasets, showing higher accuracy than state-of-the-art approaches.

关键词： Bayes methods Training Support vector machines Adaptation models Image segmentation Vectors Entropy

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General concept inclusion absorptions for fuzzy description logics: A first step

General concept inclusion absorptions for fuzzy description ...

引用

26th International Workshop on Description Logics, DL 2013

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialised inference rules, resulting in an important performance gain. In this work, we develop a first absorption algorithm for fuzzy DLs, and evaluate it over some ontologies.

关键词： Data description

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Finite fuzzy description logics and crisp representations

Finite fuzzy description logics and crisp representations

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6th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2010, Held as Part of the 9th International Semantic Web Conferences, ISWC 2010 and 1st International Workshop on Uncertainty in Description Logics, UniDL2010

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

ISBN: (纸本)9783642359743

Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge that is imprecise or vague by nature. In fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful, both for theoretical and practical reasons. In this paper, we propose finite fuzzy DLs as a generalization of existing approaches. We assume a finite totally ordered set of linguistic terms or labels, which is very useful in practice since expert knowledge is usually expressed using linguistic terms. Then, we consider fuzzy DLs based on any smooth t-norm defined over this set. Initially we focus on the finite fuzzy DL, studying some logical properties, and showing the decidability of the logic by presenting a reasoning preserving reduction to the classical case. Finally, we extend our logic in two directions: by considering non-smooth t-norms and by considering additional DL constructors. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Data description

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LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS

LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS

引用

IEEE International Conference on Image Processing

作者： Pablo Ruiz Javier Mateos Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencia de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science. Northwestern University

ISBN: (纸本)9781479957521

关键词： Gaussian Process classification Filter estimation Analysis representation

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Learning System Based on Contextual Awareness for Clinical Practice in Nursing Courses

Learning System Based on Contextual Awareness for Clinical P...

引用

International Conference on Advanced Learning Technologies (ICALT)

作者： Jorge Gómez Gómez J.F. Huete Velssy Hernandez Riaño Department of Systems Engineering - Research Group GNOCIX Universidad del Sinu-Monteria Colombia Dpt. Computer Science and Artificial Intelligence University of Granada-CITIC_UGR Spain Research Group SOCRATES Universidad de Cordoba-Monteria Colombia

The ubiquitous learning is used as a means of supporting teaching processes by means of the use of mobile and wireless communication technologies, sensors and tracking mechanisms / tracking, working together to integrate students with their environment. The process of clinical learning for nursing students may be the main cause of increased stress on teachers and students, given the problematic nature of the patients. This paper presents an ubiquitous learning system based on active learning methodology that provides context awareness support for nursing courses. An experiment with control and experimental groups with nursing students demonstrates that for theoretical concepts to be successfully transferred into practice, the context of practice needs to be considered.

关键词： Medical services Context Education Mobile communication Learning systems Mobile handsets Personal digital assistants

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