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  • 1.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Linear Systems, Sparse Solutions, and Sudoku2010In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 17, no 1, p. 40-42Article in journal (Refereed)
  • 2.
    Dai, Liang
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    An ellipsoid based, two-stage screening test for BPDN2012In: Proc. 20th European Signal Processing Conference, IEEE , 2012, p. 654-658Conference paper (Refereed)
  • 3.
    Dai, Liang
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    An online algorithm for controlling a monotone Wiener system2012In: Proceedings of the 2012 24th Chinese Control and Decision Conference (CCDC), Piscataway, NJ: IEEE , 2012, p. 1585-1590Conference paper (Refereed)
    Abstract [en]

    This paper proposes and studies an online algorithm ('NORTKNAR') for controlling a monotone Wiener system to a given level. Such systems consist of a FIR model, followed by a monotonically in-or decreasing nonlinear static function. We consider phenomena which obey such system up to stochastic perturbations. We study almost sure convergence under weak regularity assumptions. Theoretical results are complemented by empirical results on the control of a PharmacoKinetics-PharmacoDynamic (PK-PD) system regulating concentrations of levuodopa in the bloodstream. Finally, we indicate how those ideas find application to regulating the rate of events in pulsatile systems.

  • 4.
    Dai, Liang
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    On the nuclear norm heuristic for a Hankel matrix completion problem2015In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 51, p. 268-272Article in journal (Refereed)
    Abstract [en]

    This note addresses the question if and why the nuclear norm heuristic can recover an impulse response generated by a stable single-real-pole system, if elements of the upper-triangle of the associated Hankel matrix are given. Since the setting is deterministic, theories based on stochastic assumptions for low-rank matrix recovery do not apply in the considered situation. A 'certificate' which guarantees the success of the matrix completion task is constructed by exploring the structural information of the hidden matrix. Experimental results and discussions regarding the nuclear norm heuristic applied to a more general setting are also given.

  • 5.
    Dai, Liang
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Sparse estimation from noisy observations of an overdetermined linear system2014In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 11, p. 2845-2851Article in journal (Refereed)
  • 6.
    Dai, Liang
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Bai, Er-Wei
    Identifiability and convergence analysis of the MINLIP estimator2015In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 51, p. 104-110Article in journal (Refereed)
  • 7.
    Dai, Liang
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Soltanalian, Mojtaba
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    On the randomized Kaczmarz algorithm2014In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 21, no 3, p. 330-333Article in journal (Refereed)
  • 8. De Brabanter, Kris
    et al.
    Karsmakers, Peter
    De Brabanter, Jos
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Suykens, Johan A. K.
    De Moor, Bart
    On Robustness in Kernel Based Regression2010Conference paper (Other academic)
  • 9. De Brabanter, Kris
    et al.
    Karsmakers, Peter
    Ojeda, Fabian
    Alzate, Carlos
    De Brabanter, Jos
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    De Moor, Bart
    Vandewalle, Joos
    Suykens, Johan A. K.
    LS-SVMlab Toolbox User's Guide: version 1.72010Report (Other academic)
  • 10. Falck, Tillmann
    et al.
    Dreesen, Philippe
    De Brabanter, Kris
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    De Moor, Bart
    Suykens, Johan A. K.
    Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems2012In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 20, no 11, p. 1165-1174Article in journal (Refereed)
    Abstract [en]

    This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.

  • 11. Goethals, Ivan
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Falck, Tillmann
    Suykens, Johan A. K.
    De Moor, Bart
    NARX identification of Hammerstein systems using least-squares support vector machines2010In: Block-oriented Nonlinear System Identification, Berlin: Springer-Verlag , 2010, p. 241-258Chapter in book (Refereed)
  • 12. Huang, Xiaolin
    et al.
    Shi, Lei
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Suykens, Johan A. K.
    Asymmetric nu-tube support vector regression2014In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 77, p. 371-382Article in journal (Refereed)
    Abstract [en]

    Finding a tube of small width that covers a certain percentage of the training data samples is a robust way to estimate a location: the values of the data samples falling outside the tube have no direct influence on the estimate. The well-known nu-tube Support Vector Regression (nu-SVR) is an effective method for implementing this idea in the context of covariates. However, the nu-SVR considers only one possible location of this tube: it imposes that the amount of data samples above and below the tube are equal. The method is generalized such that those outliers can be divided asymmetrically over both regions. This extension gives an effective way to deal with skewed noise in regression problems. Numerical experiments illustrate the computational efficacy of this extension to the nu-SVR.

  • 13. Karsmakers, Peter
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    De Brabanter, Kris
    Van hamme, Hugo
    Suykens, Johan A. K.
    Sparse conjugate directions pursuit with application to fixed-size kernel models2011In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 85, no 1-2, p. 109-148Article in journal (Refereed)
  • 14.
    Kotrschal, Alexander
    et al.
    Department of Zoology, Stockholm Univ.
    Zeng, Hong-Li
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    van der Bijl, Wouter
    Department of Zoology, Stockholm University.
    Öhman-Mägi, Caroline
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
    Kotrschal, Kurt
    Department of Behavioural Biology, University of Vienna.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Kolm, Niclas
    Department of Zoology, Stockholm University.
    Evolution of brain region volumes during artificial selection for relative brain size2017In: Evolution, ISSN 0014-3820, E-ISSN 1558-5646, Vol. 71, no 12, p. 2942-2951Article in journal (Refereed)
    Abstract [en]

    The vertebrate brain shows an extremely conserved layout across taxa. Still, the relative sizes of separate brain regions vary markedly between species. One interesting pattern is that larger brains seem associated with increased relative sizes only of certain brain regions, for instance telencephalon and cerebellum. Till now, the evolutionary association between separate brain regions and overall brain size is based on comparative evidence and remains experimentally untested. Here, we test the evolutionary response of brain regions to directional selection on brain size in guppies (Poecilia reticulata) selected for large and small relative brain size. In these animals, artificial selection led to a fast response in relative brain size, while body size remained unchanged. We use microcomputer tomography to investigate how the volumes of 11 main brain regions respond to selection for larger versus smaller brains. We found no differences in relative brain region volumes between large- and small-brained animals and only minor sex-specific variation. Also, selection did not change allometric scaling between brain and brain region sizes. Our results suggest that brain regions respond similarly to strong directional selection on relative brain size, which indicates that brain anatomy variation in contemporary species most likely stem from direct selection on key regions.

  • 15.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A closed loop stability condition of switched systems applied to NCSs with packet loss2015In: Proc. 5th IFAC Workshop on Distributed Estimation and Control in Networked Systems, International Federation of Automatic Control , 2015Conference paper (Refereed)
  • 16.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A cooperative decentralized PI control strategy: discrete-time analysis and nonlinear feedback2012In: Proc. 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2012, p. 103-108Conference paper (Refereed)
    Abstract [en]

    This paper discusses an extension of a PI control strategy towards the control of a large m×m-MIMO system. This strategy is fully decentralized, it requires only the tuning of m different controllers, while we only allow for neighboring controllers to exchange error signals. This makes it a strong candidate for an implementation on a decentralized, low-power and high performance Wireless Sensor Network (WSN). The main idea is to feed locally observed control errors (’feedback’) not only into the local control law, but also in a fixed proportionate way into neighboring controllers. The analysis concerns convergence to a set point. The analysis is essentially based on a conversion of the PI control law into a discrete-time gradient descent scheme. As an interesting byproduct, this analysis indicates how to deal with quantization functions and nonlinear effects in the feedback signals.

  • 17.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A direct proof of the discrete-time multivariate circle and Tsypkin criteria2016In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 61, no 2, p. 544-549Article in journal (Refereed)
    Abstract [en]

    This technical note presents a new proof of the circle criterion for multivariate, discrete-time systems with time-varying feedback nonlinearities. A new proof for the multivariate Tsypkin criterion for time-invariant monotonic feedback nonlinearities is derived as well. Both integrator- and non-integrator systems are considered. The proofs are direct in the sense that they do not resort to any existing result in systems theory, such as Lyapunov theory, passivity theory or the small-gain theorem. Instead, the proofs refer to the asymptotic properties of block-Toeplitz matrices. One major advantage of the new proof is that it elegantly handles integrator systems without resorting to loop transformation/pole shifting techniques. Additionally, less conservative stability bounds are derived by making stronger assumptions on the sector bound conditions on the feedback nonlinearities. In particular, it is exemplified how this technique relaxes stability conditions of (i) a model predictive control (MPC) rule and (ii) an integrator system.

  • 18.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Conditions for input-output stability of discrete-time Luré systems with time-varying delays2015In: Proc. 54th Conference on Decision and Control, Piscataway, NJ: IEEE , 2015, p. 7707-7714Conference paper (Refereed)
    Abstract [en]

    This paper derives a stability condition for a type of Lur'e systems with time-varying delays and a feedback nonlinearity. The case of discrete-time systems is considered, consisting of a LTI, fed back though a time-varying static nonlinearity. There is an additional delay before or after this nonlinearity, which delays the signals by a positive, time-varying number of steps. Either the time-delay needs to be bounded by a constant (if the LTI system contains a single integrator) or its rate need to be bounded (in case the LTI system is stable). It turns out that, if the LTI has a single integrator with non-decreasing impulse response, the derived stability criterion coincides exactly with the circle criterion for the corresponding constant delay system. The technical proofs rely on direct manipulation of the involved signals, and do not make use of traditional tools as the small gain theorem, Lyapunov functions or passivity results.

  • 19.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    On the stability and optimality of an output feedback control law2015In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523Article in journal (Other academic)
  • 20.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stability analysis of an adaptively sampled controller for SISO systems with nonlinear feedback2015In: Proc. American Control Conference: ACC 2015, American Automatic Control Council , 2015, p. 5353-5358Conference paper (Refereed)
  • 21.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Carlsson, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Approximate adjoint-based iterative learning control2014In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 87, no 5, p. 1028-1046Article in journal (Refereed)
  • 22.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Carlsson, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Iterative Learning Control and Recursive Identification2010Conference paper (Other academic)
    Abstract [en]

    This abstract discusses our investigations relating Iterative Learning Control (ILC) for periodic systems on the one hand, and the class of Recursive Identification (RI), Gradient Descent (GD), Stochastic Approximation (SA) and adaptive filtering algorithms on the other. The benefit of such is the straightforward transfer of results in the latter context which is useful to study different design decisions made for the former. We discuss briefly the possible relevance of this observation for (i) design and analysis of suitable gain factors, (ii) working with constrained control signals, (iii) designing a model-free control strategy. For a survey of design, analysis and applications of ILC, see e.g. (1; 2). For an overview of practical and theoretical studies of RI and SA see (3), GD (4; 5), and adaptive filtering (6; 7). In this note we articulate the basic idea, and discuss further work which may be expected from this.

  • 23.
    Nygren, Johannes
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Delay-independent stability criteria for networked control systems2015Report (Other academic)
  • 24.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    An adaptive compression algorithm in a deterministic world2013In: Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence, Springer Berlin/Heidelberg, 2013, p. 299-305Conference paper (Refereed)
    Abstract [en]

    Assume that we live in a deterministic world, we ask ourselves which place the device of randomness still may have, even in case that there is no philosophical incentive for it. This note argues that improved accuracy may be achieved when modeling the (deterministic) residuals of the best model of a certain complexity as 'random'. In order to make this statement precise, the setting of adaptive compression is considered: (1) accuracy is understood in terms of codelength, and (2) the 'random device' relates to Solomonoff's Algorithmic Probability (ALP) via arithmetic coding. The contribution of this letter is threefold: (a) the proposed adaptive coding scheme possesses interesting behavior in terms of its regret bound, and (b) a mathematical characterization of a deterministic world assumption is given. (c) The previous issues then facilitate the derivation of the Randomness-Complexity (RC) frontier of the given algorithm.

  • 25.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    MINLIP for the identification of monotone Wiener systems2011In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 47, no 10, p. 2298-2305Article in journal (Refereed)
  • 26.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    On the identification of monotone Wiener systems2010In: Proc. 49th Conference on Decision and Control, Piscataway, NJ: IEEE , 2010, p. 7208-7213Conference paper (Refereed)
  • 27.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Randomized gossip algorithms for achieving consensus on the majority vote2013In: Proc. 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013Conference paper (Refereed)
    Abstract [en]

    This paper studies a decentralized, randomized gossip algorithm for computing a majority vote amongst the binary decisions associated to n nodes organized in a fixed, ad-hoc network. It is indicated how this problem can be reduced to computing the global average using a standard, randomized gossip algorithm. Then, we illustrate how the majority vote problem allows one to formulate individual stopping rules deciding when an individual node makes its final verdict. Finally, we will provide an illustration of how well the algorithm and associated stopping rule behaves.

  • 28.
    Pelckmans, Kristiaan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Dai, Liang
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A simple recursive algorithm for learning a monotone Wiener system2011In: Proc. 50th Conference on Decision and Control, Piscataway, NJ: IEEE , 2011, p. 3622-3627Conference paper (Refereed)
  • 29.
    Pelckmans, Kristiaan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Dai, Liang
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Bai, Er-Wei
    On the Convergence Analysis of the MINLIP Estimator2012In: Proceedings of the 16th IFAC Symposium on System Identification / [ed] Michel Kinnaert, 2012, p. 482-487Conference paper (Refereed)
  • 30.
    Pelckmans, Kristiaan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    De Brabanter, Jos
    Suykens, Johan A. K.
    De Moor, Bart
    Least conservative support and tolerance tubes2009In: IEEE Transactions on Information Theory, ISSN 0018-9448, E-ISSN 1557-9654, Vol. 55, no 8, p. 3799-3806Article in journal (Refereed)
  • 31.
    Pelckmans, Kristiaan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    van Waterschoot, Toon
    Suykens, Johan A. K.
    Efficient adaptive filtering for smooth linear FIR models2010In: Proc. 18th European Signal Processing Conference, European Association for Signal Processing , 2010, p. 2136-2140Conference paper (Refereed)
  • 32. Signoretto, Marco
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    De Lathauwer, Lieven
    Suykens, Johan A. K.
    Improved non-parametric sparse recovery with data matched penalties2010In: Proc. 2nd International Workshop on Cognitive Information Processing, Piscataway, NJ: IEEE , 2010, p. 46-51Conference paper (Refereed)
  • 33.
    Spiegelberg, Jakob
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
    Muto, Shunsuke
    Nagoya Univ, Inst Mat & Syst Sustainabil, Adv Measurement Technol Ctr, Chikusa Ku, Nagoya, Aichi 4648603, Japan..
    Ohtsuka, Masahiro
    Nagoya Univ, Grad Sch Engn, Chikusa Ku, Nagoya, Aichi 4648603, Japan..
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Rusz, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
    Unmixing hyperspectral data by using signal subspace sampling2017In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 182, p. 205-211Article in journal (Refereed)
    Abstract [en]

    This paper demonstrates how Signal Subspace Sampling (SSS) is an effective pre-processing step for Non-negative Matrix Factorization (NMF) or Vertex Component Analysis (VCA). The approach allows to uniquely extract non-negative source signals which are orthogonal in at least one observation channel, respectively. It is thus well suited for processing hyperspectral images from X-ray microscopy, or other emission spectroscopies, into its non-negative source components. The key idea is to resample the given data so as to satisfy better the necessity and sufficiency conditions for the subsequent NMF or VCA. Results obtained both on an artificial simulation study as well as based on experimental data from electronmicroscopy are reported. 

  • 34.
    Spiegelberg, Jakob
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
    Rusz, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Tensor decompositions for the analysis of atomic resolution electron energy loss spectra2017In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 175, p. 36-45Article in journal (Refereed)
  • 35.
    Spiegelberg, Jakob
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
    Rusz, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
    Thersleff, Thomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Analysis of electron energy loss spectroscopy data using geometric extraction methods2017In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 174, p. 14-26Article in journal (Refereed)
  • 36.
    Stoica, Anca-Juliana
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Rowe, William
    System components of a general theory of software engineering2015In: Science of Computer Programming, ISSN 0167-6423, E-ISSN 1872-7964, Vol. 101, p. 42-65Article in journal (Refereed)
    Abstract [en]

    The contribution of this paper to a general theory of software engineering is twofold: it presents the model system concept, and it integrates the software engineering design process into a decision making theory and a value-based decision-under-risk process. The model system concept is defined as a collection of interconnected and consistent components that work together for defining, developing, and delivering a software system. This model system concept is used to represent the multiple facets of a software engineering project such as stakeholders and models related to domain/environment, success, decision, product, process, and property. The model system concept is derived from software development practices in the industry and academia. The theoretical decision framework acts as a central governance component for a given software engineering project. Applying this decision framework allows for effectively managing risks and uncertainties related to success in the project building stage. Especially, this puts the design process in an economic perspective, where concepts such as value-of-waiting, value-of-information and possible outcomes can be coped with explicitly. In practice, the decision framework allows for the optimal control of modern adaptive software development. In particular, one can use dynamic programming to find the optimal sequence of decisions to be made considering a defined time horizon. In this way we can relate our contribution to a theory of software engineering to the well-studied areas of automatic control, optimization, decision theory and Bayesian analysis.

  • 37. Suykens, Johan A. K.
    et al.
    Alzate, Carlos
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Primal and dual model representations in kernel-based learning2010In: Statistics Surveys, ISSN 1935-7516, Vol. 4, p. 148-183Article in journal (Refereed)
  • 38.
    Szorkovszky, Alex
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Kotrschal, Alexander
    Stockholm Univ, Zool Dept, Stockholm, Sweden.
    Herbert Read, James E.
    Stockholm Univ, Zool Dept, Stockholm, Sweden.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
    Kolm, Niclas
    Stockholm Univ, Zool Dept, Stockholm, Sweden.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    An efficient method for sorting and quantifying individual social traits based on group-level behaviour2017In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210X, Vol. 8, no 12, p. 1735-1744Article in journal (Refereed)
  • 39. Van Belle, Vanya
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Suykens, Johan A. K.
    Van Huffel, Sabine
    Additive survival least-squares support vector machines2010In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 29, no 2, p. 296-308Article in journal (Refereed)
  • 40. Van Belle, Vanya
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Suykens, Johan A. K.
    Van Huffel, Sabine
    Learning transformation models for ranking and survival analysis2011In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 12, p. 819-862Article in journal (Refereed)
  • 41. Van Belle, Vanya
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Suykens, Johan A. K.
    Van Huffel, Sabine
    On the use of a clinical kernel in survival analysis2010In: Proc. 18th European Symposium on Artificial Neural Networks, Evere, Belgium: d-side publications , 2010, p. 451-456Conference paper (Refereed)
  • 42. Van Belle, Vanya
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Van Huffel, Sabine
    Suykens, Johan A. K.
    Improved performance on high-dimensional survival data by application of Survival-SVM2011In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 27, no 1, p. 87-94Article in journal (Refereed)
  • 43. Van Belle, Vanya
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Van Huffel, Sabine
    Suykens, Johan A. K.
    Support vector methods for survival analysis: a comparison between ranking and regression approaches2011In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 53, no 2, p. 107-118Article in journal (Refereed)
  • 44.
    Yasini, Sholeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    High-dimensional online adaptive filtering2017Conference paper (Refereed)
1 - 44 of 44
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