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See detailNASA-FDL Artificial Intelligence in Planetary Science; Lunar Resource Mission
Backes, Dietmar UL

Scientific Conference (2017, December 15)

Detailed reference viewed: 111 (19 UL)
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See detailNatural frequencies of cracked isotropic & specially orthotropic plates using the extended finite element method
Natarajan, S; Baiz, P; Mahapatra, D Roy et al

Scientific Conference (2011, April)

In this paper, the linear free flexural vibration of cracked isotropic and specially orthotropic plates is studied using the extended finite element method. The mixed interpolation technique of the well ... [more ▼]

In this paper, the linear free flexural vibration of cracked isotropic and specially orthotropic plates is studied using the extended finite element method. The mixed interpolation technique of the well- established MITC4 [1] quadrilateral finite element with 12 standard degrees of freedom per element is used for this study. The natural frequencies of simply supported square plates are computed as a function of crack length and crack location. [less ▲]

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See detailNecessary and sufficient conditions for dynamical structure reconstruction of LTI networks
Goncalves, Jorge UL; Warnick, S.

in IEEE Transactions on Automatic Control (2008), 53(7), 1670-1674

This paper formulates and solves the network reconstruction problem for linear time-invariant systems. The problem is motivated from a variety of disciplines, but it has recently received considerable ... [more ▼]

This paper formulates and solves the network reconstruction problem for linear time-invariant systems. The problem is motivated from a variety of disciplines, but it has recently received considerable attention from the systems biology community in the study of chemical reaction networks. Here, we demonstrate that even when a transfer function can be identified perfectly from input–output data, not even Boolean reconstruction is possible, in general, without more information about the system.We then completely characterize this additional information that is essential for dynamical reconstruction without appeal to ad-hoc assumptions about the network, such as sparsity or minimality. [less ▲]

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See detailNecessary conditions for robust stability of a class of nonlinear systems
Goncalves, Jorge UL; Dahleh, M. A.

in Automatica (1998), 34(6), 705-714

Input-output stability results for feedback systems are developed. Robust stability conditions are presented for nonlinear systems with nonlinear uncertainty defined by some function (with argument equal ... [more ▼]

Input-output stability results for feedback systems are developed. Robust stability conditions are presented for nonlinear systems with nonlinear uncertainty defined by some function (with argument equal to the norm of the input) that bounds its output norm. A sufficient small gain theorem for a class of these systems is known. Here, necessary conditions are presented for the vector space (L- infinity ll . ll infinity). These results capture the conservatism of the small gain theorem as it is applied to systems that do not have linear gain. The theory is also developed for the case of L2 signal norms, indicating some difficulties which make this case less natural than L-infinity. [less ▲]

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See detailThe need of standardised metadata to encode causal relationships: Towards safer data-driven machine learning biological solutions
Garcia Santa Cruz, Beatriz UL; Vega Moreno, Carlos Gonzalo UL; Hertel, Frank UL

Scientific Conference (2021, November 16)

In this paper, we discuss the importance of considering causal relations in the development of machine learning solutions to prevent factors hampering the robustness and generalisation capacity of the ... [more ▼]

In this paper, we discuss the importance of considering causal relations in the development of machine learning solutions to prevent factors hampering the robustness and generalisation capacity of the models, such as induced biases. This issue often arises when the algorithm decision is affected by confounding factors. In this work, we argue that the integration of causal relationships can identify potential confounders. We call for standardised meta-information practices as a crucial step for proper machine learning solutions development, validation, and data sharing. Such practices include detailing the dataset generation process, aiming for automatic integration of causal relationships. [less ▲]

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See detailNetwork Identifiability from Intrinsic Noise
Goncalves, Jorge UL; Hayden, David; Yuan, Ye

in IEEE Transactions on Automatic Control (in press)

Detailed reference viewed: 441 (32 UL)
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See detailNetwork observability information maximization through ad-hoc route enumeration approaches
Rinaldi, Marco UL; Corman, Francesco; Viti, Francesco UL

Scientific Conference (2016, June)

Detailed reference viewed: 65 (2 UL)
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See detailNetwork Reconstruction from Intrinsic Noise
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the American Control Conference (2014)

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest ... [more ▼]

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest variables only from observations of these variables. We consider linear, time-invariant systems of minimal order and with one noise source per measured state. If the transfer matrix from the inputs to manifest states is known to be minimum phase, this problem is shown to have a unique solution irrespective of the network topology. This is equivalent to there being only one spectral factor (up to a choice of signs of the inputs) of the output spectral density that satisfies these assumptions. Hence for this significant class of systems, the network reconstruction problem is well posed. [less ▲]

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See detailNetwork Reconstruction from Intrinsic Noise: Non-Minimum-Phase Systems
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the The 19th World Congress of The International Federation of Automatic Control (2014)

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest ... [more ▼]

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest variables only from observations of these variables. We consider linear, time-invariant systems of minimal order and with one noise source per manifest state. It is known that if the transfer matrix from the inputs to manifest states is minimum phase, then this problem has a unique solution, irrespective of the network topology. Here we consider the general case where the transfer matrix may be non-minimum phase and show that solutions are characterized by an Algebraic Riccati Equation (ARE). Each solution to the ARE corresponds to at most one spectral factor of the output spectral density that satisfies the assumptions made. Hence in general the problem may not have a unique solution, but all solutions can be computed by solving an ARE and their number may be finite. [less ▲]

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See detailNetwork reconstruction using knock-out and over-expression data
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the 2013 European Control Conference (ECC) (2013)

This paper outlines necessary and sufficient conditions for network reconstruction of linear, time-invariant systems using data from either knock-out or over-expression experiments. These structural ... [more ▼]

This paper outlines necessary and sufficient conditions for network reconstruction of linear, time-invariant systems using data from either knock-out or over-expression experiments. These structural system perturbations, which are common in biological experiments, can be formulated as unknown system inputs, allowing the network topology and dynamics to be found. We assume that only partial state measurements are available and propose an algorithm that can reconstruct the network at the level of the measured states using either time-series or steady-state data. A simulated example illustrates how the algorithm successfully reconstructs a network from data. [less ▲]

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See detailNetwork structure preserving model reduction with weak a priori structural information
Yeung, E.; Goncalves, Jorge UL; Sandberg, H. et al

in The proceedings of the Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (2009)

This paper extends a state projection method for structure preserving model reduction to situations where only a weaker notion of system structure is available. This weaker notion of structure ... [more ▼]

This paper extends a state projection method for structure preserving model reduction to situations where only a weaker notion of system structure is available. This weaker notion of structure, identifying the causal relationship between manifest variables of the system, is especially relevant is settings such as systems biology, where a clear partition of state variables into distinct subsystems may be unknown, or not even exist. The resulting technique, like similar approaches, does not provide theoretical performance guarantees, so an extensive computational study is conducted, and it is observed to work fairly well in practice. Moreover, conditions characterizing structurally minimal realizations and sufficient conditions characterizing edge loss resulting from the reduction process, are presented. [less ▲]

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See detailNetwork Structure Preserving Model Reduction: Results of a Simulation Study
Yeung, E.; Goncalves, Jorge UL; Sandberg, H. et al

in The proceedings of the Third International Conference on Foundations of Systems Biology in Engineering (FOSBE 2009) (2009)

Reconstructed models of biochemical networks often reflect the high level of complexity inherent in the biological system being modeled. The difficulties of predicting gene expression and analyzing the ... [more ▼]

Reconstructed models of biochemical networks often reflect the high level of complexity inherent in the biological system being modeled. The difficulties of predicting gene expression and analyzing the effects of individual perturbations at a system-wide resolution are exacerbated by model complexity. This paper extends a state projection method for structure preserving model reduction to a particular model class of reconstructed networks known as dynamical structure functions. In contrast to traditional approaches where a priori knowledge of partitions on unmeasured species is required, dynamical structure functions require a weaker notion of system structure, specifying only the causal relationship between measured chemical species of the system. The resulting technique, like similar approaches, does not provide theoretical performance guarantees, so an extensive computational study is conducted, and it is observed to work fairly well in practice. Moreover, sufficient conditions, characterizing edge loss resulting from the reduction process, are presented. [less ▲]

Detailed reference viewed: 78 (2 UL)
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See detailA network-wide assessment of local signal control policies’ performance in practical implementations
Cantelmo, Guido UL; Viti, Francesco UL; Rinaldi, Marco UL

in Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on (2016, November)

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See detailNeuro-Inspired Reward-Based Tracking Control for Robotic Manipulators with Unknown Dynamics
Klecker, Sophie UL; Hichri, Bassem UL; Plapper, Peter UL

in Proceedings of the 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE) (2017, December)

Tracking control for robotic manipulators is required for numerous automation tasks in manufacturing engineering. For this purpose, model-free PD-controllers are largely implemented by default in ... [more ▼]

Tracking control for robotic manipulators is required for numerous automation tasks in manufacturing engineering. For this purpose, model-free PD-controllers are largely implemented by default in commercially available robot arms and provide satisfactory performance for simple path following applications. Ever more complex automation tasks however ask for novel intelligent and adaptive tracking control strategies. In surface finishing processes, discontinuous freeform paths as well as changing constraints between the robotic end-effector and its surrounding environment affect the tracking control by undermining the stable system performance. The lacking knowledge of industrial robot dynamic parameters presents an additional challenge for the tracking control algorithms. In this paper the control problem of robotic manipulators with unknown dynamics and varying constraints is addressed. A robust sliding mode controller is combined with an RBF (Radial Basis Function) Neural Network-estimator and an intelligent, biomimetic BELBIC (Brain Emotional Learning-Based Intelligent Control) term to approximate the nonlinear robot dynamics function and achieve a robust and adaptive tracking performance. [less ▲]

Detailed reference viewed: 150 (17 UL)
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See detailA New Mathematical Model for the Heat Shock Response
Petre, Ion; Mizera, Andrzej UL; Hyder, Claire L. et al

in Condon, Anne; Harel, David; Kok, Joost N. (Eds.) et al Algorithmic Bioprocesses (2009)

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See detailNew services, new travelers, old models? Directions to pioneer public transport models in the era of big data
Fonzone, Achille; Schmocker, Jan-Dirk; Viti, Francesco UL

in Journal of Intelligent Transportation Systems (2016)

Detailed reference viewed: 132 (8 UL)
See detailThe NIS 2.0 Directive - Lessons learnt or lagging behind? A legal perspective
Schmitz, Sandra UL

Scientific Conference (2022, May 10)

Detailed reference viewed: 52 (0 UL)
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See detailNitsche’s method for two and three dimensional NURBS patch coupling
Nguyen, VP; Kerfriden, Pierre; Brino, Marco et al

in Computational Mechanics (2014), 53(6), 1163-1182

We present a Nitche’s method to couple non-conforming two and three-dimensional NURBS (Non Uniform Rational B-splines) patches in the context of isogeometric analysis (IGA). We present results for linear ... [more ▼]

We present a Nitche’s method to couple non-conforming two and three-dimensional NURBS (Non Uniform Rational B-splines) patches in the context of isogeometric analysis (IGA). We present results for linear elastostatics in two and and three-dimensions. The method can deal with surface-surface or volume-volume coupling, and we show how it can be used to handle heterogeneities such as inclusions. We also present preliminary results on modal analysis. This simple coupling method has the potential to increase the applicability of NURBS-based isogeometric analysis for practical applications. [less ▲]

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See detailNitsche’s method for two and three dimensional NURBS patch coupling
Nguyen, Vinh-Phu; Kerfriden, Pierre; Brino, Marco et al

in Computational Mechanics (in press)

We present a Nitche’s method to couple non-conforming two and three-dimensional NURBS (Non Uniform Rational B-splines) patches in the context of isogeometric analysis (IGA). We present results for linear ... [more ▼]

We present a Nitche’s method to couple non-conforming two and three-dimensional NURBS (Non Uniform Rational B-splines) patches in the context of isogeometric analysis (IGA). We present results for linear elastostatics in two and and three-dimensions. The method can deal with surface-surface or volume-volume coupling, and we show how it can be used to handle heterogeneities such as inclusions. We also present preliminary results on modal analysis. This simple coupling method has the potential to increase the applicability of NURBS-based isogeometric analysis for practical applications. [less ▲]

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See detailNitsche’s method method for mixed dimensional analysis: conforming and non-conforming continuum-beam and continuum-plate coupling
Nguyen, VP; Kerfriden, Pierre; Claus, SPA et al

in Computer Methods in Applied Mechanics and Engineering (2014)

A Nitche’s method is presented to couple different mechanical models. They include coupling of a solid and a beam and of a solid and a plate. Both conforming and non-conforming formulations are presented ... [more ▼]

A Nitche’s method is presented to couple different mechanical models. They include coupling of a solid and a beam and of a solid and a plate. Both conforming and non-conforming formulations are presented. In a non-conforming formulation, the structure domain is overlapped by a refined solid model which is needed to either get more accuracy or to capture highly nonlinear events. Applications can be found in multi-dimensional analyses in which parts of a structure are modeled with solid elements and others are modeled using a coarser model with beam and/or plate elements. Discretisations are performed using both standard Lagrange elements and high order NURBS (Non Uniform Rational Bsplines) based isogeometric elements. We present various examples to demonstrate the performance of the method. [less ▲]

Detailed reference viewed: 305 (18 UL)