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arXiv 2019年

作者： Danca, Marius-F Fečkan, Michal Kuznetsov, Nikolay Cheng, Guanrong Dept. of Mathematics and Computer Science Avram Iancu University of Cluj- Napoca Romania Romanian Institute of Science and Technology Cluj-Napoca Romania Dept. of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mathematical Institute Slovak Academy of Sciences Dept. of Applied Cybernetics Saint-Petersburg State University Russia Dept. of Mathematical Information Technology University of Jyväskylä Finland Dept. of Electronic Engineering City University of Hong Kong Hong Kong

This paper reveals some new and rich dynamics of a two-dimensional prey-predator system and to anticontrol the extinction of one of the species. For a particular value of the bifurcation parameter, one of the system variable dynamics is going to extinct, while another remains chaotic. To prevent the extinction, a simple anticontrol algorithm is applied so that the system orbits can escape from the vanishing trap. As the bifurcation parameter increases, the system presents quasiperiodic, stable, chaotic and also hyperchaotic orbits. Some of the chaotic attractors are Kaplan-Yorke type, in the sense that the sum of its Lyapunov exponents is positive. Also, atypically for undriven discrete systems, it is numerically found that, for some small parameter ranges, the system seemingly presents strange nonchaotic attractors. It is shown both analytically and by numerical simulations that the original system and the anticontrolled system undergo several Neimark-Sacker bifurcations. Beside the classical numerical tools for analyzing chaotic systems, such as phase portraits, time series and power spectral density, the’0-1’ test is used to differentiate regular attractors from chaotic attractors. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectral density

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Overcoming the disconnect between energy system and climate modelling

arXiv

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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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On the effect of local search in the multi-objective evolutionary discovery of software architectures

On the effect of local search in the multi-objective evoluti...

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2017 IEEE Congress on Evolutionary Computation, CEC 2017

作者： Ramirez, Aurora Romero, Jose Raul Ventura, Sebastian Dept. of Computer Science and Numerical Analysis University of Córdoba Campus of Rabanales Córdoba14071 Spain

ISBN: (纸本)9781509046010

Software architects devote substantial efforts to find the most fitting architectural description for their system, which should not only specify its structure, but is also required to meet multiple, simultaneous quality criteria. Evolutionary computation has recently demonstrated to provide insightful support during the design phase by automatically deciding how to organise internal software components and how they should interact each other. Observed from a multi-objective perspective, particular care has to be taken in order to reach an appropriate trade-off among design metrics, while providing the software engineer with diverse alternatives to choose among. However, multi-objective evolutionary algorithms may find difficulties to control both aspects and, at the same time, to explore the entire search space in dept.. Under these circumstances, local search can be applied to complement the evolution by scrutinising the most promising search directions. This paper proposes two different approaches that take advantage of the benefits of local search within the multi-objective evolutionary discovery of component-based software architectures. A detailed analysis and comparative study provides interesting findings like the importance of assigning a sufficient number of evaluations to the local improvement. The way in which local search explores and compares solutions for acceptance is a relevant aspect to promote diversity during the discovery process as well. © 2017 IEEE.

关键词： Software architecture

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Monotonic classification: An overview on algorithms, performance measures and data sets

arXiv

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arXiv 2018年

作者： Cano, José-Ramón Gutiérrez, Pedro Antonio Krawczyk, Bartosz Woźniak, Michal García, Salvador Dept. of Computer Science University of Jaén EPS of Linares Avenida de la Universidad S/N Linares Jaén23700 Spain Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Computer Science Virginia Commonwealth University RichmondVA23284 United States Department of Computer Science Wroclaw University of Technology Wyb. Wyspiańskiego 27 Wroclaw50-370 Poland Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview about the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining

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On the effect of local search in the multi-objective evolutionary discovery of software architectures

On the effect of local search in the multi-objective evoluti...

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Congress on Evolutionary Computation

作者： Aurora Ramírez José Raúl Romero Sebastián Ventura Universidad de Cordoba Cordoba AndalucÃa ES Dept. of Computer Science and Numerical Analysis University of Córdoba Campus of Rabanales Córdoba Spain

ISBN: (纸本)9781509046027

关键词： Software architecture Search problems Computer architecture Memetics Evolutionary computation Measurement Mathematical model

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Preconditioned smoothers for the full approximation scheme for the RANS equations

arXiv

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arXiv 2017年

作者： Birken, Philipp Bull, Jonathan Jameson, Antony Centre for the Mathematical Sciences Numerical Analysis Lund University Lund Sweden Division of Scientific Computing Dept. of Information Technology Uppsala University Box 337 Uppsala75105 Sweden Stanford University Department of Aeronautics and Astronautics StanfordCA94305 United States

We consider multigrid methods for finite volume discretizations of the Reynolds Averaged Navier-Stokes (RANS) equations for both steady and unsteady flows. We analyze the effect of different smoothers based on pseudo time iterations, such as explicit and additive Runge-Kutta (ARK) methods. Furthermore, we derive the new class of additive W (AW) methods from Rosenbrock smoothers. This gives rise to two classes of preconditioned smoothers, preconditioned ARK and additive W (AW), which are implemented the exact same way, but have different parameters and properties. The new derivation allows to choose some of these based on results for time integration methods. As preconditioners, we consider SGS preconditioners based on flux vector splitting discretizations with a cutoff function for small eigenvalues. We compare these methods based on a discrete Fourier analysis. numerical results on pitching and plunging airfoils identify AW3 as the best smoother regarding overall efficiency. Specifically, for the NACA 64A010 airfoil steady-state convergence rates of as low as 0.85 were achieved, or a reduction of 6 orders of magnitude in approximately 25 pseudo-time iterations. Unsteady convergence rates of as low as 0.77 were achieved, or a reduction of 11 orders of magnitude in approximately 70 pseudo-time iterations. Copyright © 2017, The Authors. All rights reserved.

关键词： Runge Kutta methods

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Machine learning paradigms for weed mapping via unmanned aerial vehicles

Machine learning paradigms for weed mapping via unmanned aer...

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2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

作者： Perez-Ortiz, M. Gutierrez, P.A. Pena, J.M. Torres-Sanchez, J. Lopez-Granados, F. Hervas-Martinez, C. Dept. of Quantitative Methods Universidad Loyola Andalucía Córdoba14004 Spain Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain Institute for Sustainable Agriculture CSIC Córdoba14080 Spain

ISBN: (纸本)9781509042401

This paper presents a novel strategy for weed monitoring, using images taken with unmanned aerial vehicles (UAVs) and concepts of image analysis and machine learning. Weed control in precision agriculture designs site-specific treatments based on the coverage of weeds, where the key is to provide precise weed maps timely. Most traditional remote platforms, e.g. piloted planes or satellites, are, however, not suitable for early weed monitoring, given their low temporal and spatial resolutions, as opposed to he ultra-high spatial resolution of UAVs. The system here proposed makes use of UAV-imagery and is based on: 1) Divide the image, 2) compute and binarise the vegetation indexes, 3) detect crop rows, 4) optimise the parameters and 4) learn a classification model. Since crops are usually organised in rows, the use of a crop row detection algorithm helps to separate properly weed and crop pixels, which is a common handicap given the spectral similitude of both. Several artificial intelligence paradigms are compared in this paper to identify the most suitable strategy for this topic (i.e. unsupervised, supervised and semi-supervised approaches). Our experiments also study the effect of different parameteres: the flight altitude, the sensor and the use of previously trained models at a different height. Our results show that 1) very promising performance can be obtained, even when using very few labelled data and 2) the classification model can be learnt in a subplot of the experimental field at low altitude and then applied to the whole field at a higher height, which simplifies the whole process. These results motivate the use of this strategy to design weed monitoring strategies for early post-emergence weed control. © 2016 IEEE.

关键词： Weed control

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A STUDY of multigrid smoothers used in compressible CFD based on the convection diffusion equation 7

A STUDY of multigrid smoothers used in compressible CFD base...

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7th European Congress on Computational Methods in Applied sciences and Engineering, ECCOMAS Congress 2016

作者： Birken, Philipp Bull, Jonathan Jameson, Antony Lund University Centre for the Mathematical Sciences Numerical Analysis Box 118 Lund22100 Sweden Uppsala University Division of Scientific Computing Dept. of Information Technology Box 337 Uppsala75105 Sweden Stanford University Department of Aeronautics and Astronautics StanfordCA94305 United States

ISBN: (纸本)9786188284401

We look at multigrid methods for unsteady viscous compressible flows. We specifically target smoothers that can be used in parallel and without computation of a Jacobian, which are particlarly attractive candidates in the context of Discontinuous Galerkin discretizations. In CFD, a plethora of nonlinear smoothers have been suggested which are hard to analyze. Our methodology is to use a linear model problem, here the convection diffusion equation, to be able to classify and compare smoothers better. Specifically, we consider explicit and implicit pseudo time iterations, GMRES as a smoother, SGS and implicit line smoothers. We relate GMRES to explicit Runge-Kutta smoothers, identify implicit line smoothers as Block Jacobi and analyze the potential of implicit pseudo time iterations. Finally, we discuss the relation between methods for steady and unsteady flows. numerical results show that GMRES is a very attractive smoother in this context.

关键词： Unsteady flow

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A note on terminology in multigrid methods

A note on terminology in multigrid methods

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Joint 87th Annual Meeting on the International Association of Applied Mathematics and Mechanics, GAMM 2016 and Deutsche Mathematiker‐Vereinigung, DMV 2016

We compare terminology used in the literature on multigrid methods for compressible computational fluid dynamics to that used in linear multigrid theory. Several popular iterative and direct smoothers are presented side-by-side using the same terminology. We argue for greater analysis of these methods in order to place them into a more rigorous framework and to identify the most promising candidates for future development. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

关键词： Terminology

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Adapting Linear Discriminant analysis to the Paradigm of Learning from Label Proportions

Adapting Linear Discriminant Analysis to the Paradigm of Lea...

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IEEE Symposium Series on Computational Intelligence

作者： M. Pérez-Ortiz P.A. Gutiérrez M. Carbonero-Ruz C. Hervás-Martínez Dept. of Quantitative Methods Universidad Loyola Andalucía 14004 Córdoba Spain Dept. of Computer Science and Numerical Analysis University of Córdoba 14071 Spain

ISBN: (纸本)9781509042418

The recently coined term "learning from label proportions" refers to a new learning paradigm where training data is given by groups (also denoted as "bags"), and the only known information is the label proportion of each bag. The aim is then to construct a classification model to predict the class label of an individual instance, which differentiates this paradigm from the one of multi-instance learning. This learning setting presents very different applications in political science, marketing, healthcare and, in general, all fields in relation with anonymous data. In this paper, two new strategies are proposed to tackle this kind of problems. Both proposals are based on the optimisation of pattern class memberships using the data distribution in each bag and the known label proportions. To do so, linear discriminant analysis has been reformulated to work with non-crisp class memberships. The experimental part of this paper sets different objetives: 1) study the difference in performance, comparing our proposals and the fully supervised setting, 2) analyse the potential benefits of refining class memberships by the proposed approaches, and 3) test the influence of other factors in the performance, such as the number of classes or the bag size. The results of these experiments are promising, but further research should be encouraged for studying more complex data configurations.

关键词： weak supervision linear discriminant analysis learning from label proportions

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