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See detailDistributed Automatic Vehicle Location (AVL) System Based on Connected Vehicle Technology
Smietanka, Piotr; Szczypiorski, Krzysztof; Viti, Francesco UL et al

in Proceedings of IEEE-ITS Conference (2015, September)

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See detailDistributed high-gain attitude synchronization using rotation vectors
Song, W.; Thunberg, Johan UL; Hu, X. et al

in Journal of Systems Science and Complexity (2015), 28(2), 289-304

This paper addresses attitude synchronization problems for systems of multiple rigid-body agents with directed interconnection topologies. Two scenarios which differ in available information are ... [more ▼]

This paper addresses attitude synchronization problems for systems of multiple rigid-body agents with directed interconnection topologies. Two scenarios which differ in available information are considered. In the first scenario the agents can obtain their rotations and angular velocities relative to an inertial reference frame and transmit these information to their neighbors, while in the second scenario the agents can only obtain their own angular velocities and measure the relative rotations and relative angular velocities of their neighbors. By using rotation vectors and the high gain control, the authors provide torque control laws asymptotically synchronizing the rotations of the system almost globally for the first scenario and with initial rotations of the agents contained in a convex ball of SO(3) for the second scenario. An illustrative example is provided to show the synchronization results for both scenarios. [less ▲]

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See detailDistributed identification of the Cell Transmission traffic model: A case study
Rinaldi, Marco UL; Capisani, L.; Ferrara, A. et al

in Procedings of the 2012 American Control Conference, ACC 2012 (2012)

The problem of the distributed identification of a macroscopic first-order traffic model, viz. the Cell Transmission Model (CTM), is considered in the paper. The parameters to be identified characterize ... [more ▼]

The problem of the distributed identification of a macroscopic first-order traffic model, viz. the Cell Transmission Model (CTM), is considered in the paper. The parameters to be identified characterize the dynamics of the density in different sections of the freeway (cells). We explore different distributed identification schemes. The purposes of the approach are mainly to obtain good prediction models through the minimization of the one-step ahead prediction error of the densities of the cells, and to reduce the computational time and the effort required to perform the identification. The methodology is validated relying on real-life data measured on a portion of the A12 freeway in The Netherlands. An evaluation of the performance of the identified model used as a set of virtual sensors in different scenarios is presented. © 2012 AACC American Automatic Control Council). [less ▲]

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See detailDistributed Kalman Filter with minimum-time covariance computation
Thia, Jerry; Yuan, Ye; Shi, Ling et al

in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013)

This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local ... [more ▼]

This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local observations of sensors. It has been shown that the state estimates obtained in [8] and [9] approaches those of the Central Kalman Filter (CKF) asymptotically. However, the convergence to the CKF can sometimes be too slow. This paper proposes an algorithm that enables every node in a sensor network to compute the global average consensus matrix of measurement noise covariance in minimum time without accessing global information. Compared with the algorithm in [8], our theoretical analysis and simulation results show that the new algorithm can offer improved performance in terms of time taken for the state estimates to converge to that of the CKF. [less ▲]

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See detailDistributed reconstruction of nonlinear networks: An ADMM approach
Pan, Wei UL; Sootla, Aivar; Stan, Guy-Bart

in Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC 2014) (2014)

In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks. In particular, we focus on the identification from time-series data of the nonlinear functional ... [more ▼]

In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks. In particular, we focus on the identification from time-series data of the nonlinear functional forms and associated parameters of large-scale nonlinear networks. In (Pan et al. (2013)), a nonlinear network reconstruction problem was formulated as a nonconvex optimisation problem based on the combination of a marginal likelihood maximisation procedure with sparsity inducing priors. Using a convex-concave procedure (CCCP), an iterative reweighted lasso algorithm was derived to solve the initial nonconvex optimisation problem. By exploiting the structure of the objective function of this reweighted lasso algorithm, a distributed algorithm can be designed. To this end, we apply the alternating direction method of multipliers (ADMM) to decompose the original problem into several subproblems. To illustrate the effectiveness of the proposed methods, we use our approach to identify a network of interconnected Kuramoto oscillators with different network sizes (500∼100,000 nodes). [less ▲]

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See detailDistributedFBA.jl: High-level, high-performance flux balance analysis in Julia.
Heirendt, Laurent UL; Thiele, Ines UL; Fleming, Ronan MT UL

in Bioinformatics (2017)

MOTIVATION: Flux balance analysis, and its variants, are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with ... [more ▼]

MOTIVATION: Flux balance analysis, and its variants, are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations. RESULTS: DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. AVAILABILITY: The code is freely available on github.com/opencobra/COBRA.jl. The documentation can be found at opencobra.github.io/COBRA.jl. [less ▲]

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See detailDrawing ER diagrams with TikZ
Fiandrino, Claudio UL

in Ars TeXnica (2013), 15

The paper will illustrate some techniques to represent Entity-Relationship (ER) diagrams with TikZ. In particular, it will focus on the standard internal library \library{er}, on the external package TikZ ... [more ▼]

The paper will illustrate some techniques to represent Entity-Relationship (ER) diagrams with TikZ. In particular, it will focus on the standard internal library \library{er}, on the external package TikZ-er2, on the external tool Graphviz and on the object-oriented approach provided by the er-oo library. [less ▲]

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See detailA dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity
Huang, Wei; Viti, Francesco UL; Tampere, Chris

in Journal of Advanced Transportation (2016)

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See detailA Dual-Grid Multiscale Approach to CFD-DEM Couplings for Multiphase Flow
Pozzetti, Gabriele UL

Doctoral thesis (2018)

This thesis focuses on a novel dual-grid multiscale approach to CFD- DEM1 couplings, proposes its advantages in terms of numerical proper- ties and performance, and provides examples of engineering ... [more ▼]

This thesis focuses on a novel dual-grid multiscale approach to CFD- DEM1 couplings, proposes its advantages in terms of numerical proper- ties and performance, and provides examples of engineering applications that can benefit from it. In recent years, CFD-DEM couplings are be- coming a more and more adopted solution for the numerical simulation of particle-laden flows. In particular, couplings based on the volume av- eraging technique have become a standard for numerical simulations in chemical and process engineering. Furthermore, they are rapidly spread- ing to civil, geotechnical and mechanical applications due to their ability in dealing with arbitrarily complex mixtures of continuum and granular media. Despite the several advantages that these Eulerian-Lagrangian cou- plings provide, their rigorous application to complex scenarios is currently limited by two main factors. First, the computational traceability of the solutions can become problematic due to the lack of a general theory on the subject. In particular, grid-convergence studies for the solution of the continuous phases are often not feasible due to the averaging procedure that imposes limitations on the grid structure and refinement. Second, the parallel implementation of these numerical schemes holds important disadvantages in terms of memory consumption and inter-physics com- munication load. These disadvantages are significantly limiting the ex- tension of these approaches to large-scale scenarios. This thesis collects some of the most significant works published in the last years on a novel approach that allows solving the two above- mentioned problems, and, therefore, tackling more complex and expen- sive scenarios. I refer to this approach as dual-grid multiscale approach for CFD-DEM couplings. It consists in using two different computational grids, one for the coupling between continuum and discrete entities and one for the solution of the so-obtained continuum equations. The two grids, i.e. the two problems, are in this way resolved on two different scales. The first scale or “bulk” scale is chosen to optimize the averag- ing operation. At this length-scale, the discrete entities are considered as zero-dimensional, and interact with the fluid with local exchanges of momentum, mass, and energy. The second scale or “fluid-fine” scale is identified as the one at which a unique solution for the averaged equa- tions can be provided. In practice, this is chosen as the one at which the solution of the fluid equations becomes grid-independent. An inter-scale communication is adopted by interpolating fields from the fluid-fine scale to the bulk one and vice-versa. The theoretical description of the method is first provided with par- ticular reference to the DEM-VOF coupling. Even in its simplest version, the multiscale approach is shown to generate grid-convergent solutions and significantly higher accuracy than a standard CFD-DEM coupling. This shows how the new approach is able to overcome the first main limitation described above. Then, an optimized parallel implementation of the method is pro- posed to show how this multiscale approach can provide significant ben- efits also for what concerns the execution time. Technically, this is made possible by moving the communication cost of the coupling from the inter-physics communication that characterized the standard CFD-DEM couplings to an optimized inter-scale communication routine. This en- ables the method to overcome a major bottleneck of the parallel execution of CFD-DEM couplings and therefore the second main limitation of those schemes. Finally, the dual-grid multiscale method is applied to approach in- dustrially relevant problems that were till now out-of-reach for standard CFD-DEM couplings, proving how this technique can have direct real- case application and produce immediate benefits for practitioners willing to adopt it. [less ▲]

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See detailDynamic modeling of VISSIM critical gap parameter at unsignalized intersections
Viti, Francesco UL; Wolput, Bart; Tampere, Chris M.J. et al

Poster (2013)

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See detailDynamic modeling of VISSIM's critical gap parameter at unsignalized intersections
Viti, Francesco UL; Wolput, Bart; Tampere, Chris M.J. et al

in Transportation Research Record: Journal of the Transportation Research Board (2014), 2395

Detailed reference viewed: 113 (7 UL)
See detailDynamic modelling of ground antennas
Breyer, Laurent UL

Doctoral thesis (2011)

Detailed reference viewed: 79 (3 UL)
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See detailDynamic neural network approach for atmospheric pollutant prediction: A pulp mill case study
Sainlez, Matthieu UL

Scientific Conference (2011, May 27)

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See detailDynamic OD estimation in congested networks: theoretical findings and implications in practice
Frederix, Rodric; Viti, Francesco UL; Tampere, Chris M.J.

in Transportmetrica (2013), 9(6), 494-513

In this study we analyse the impact of congestion in dynamic origin–destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the ... [more ▼]

In this study we analyse the impact of congestion in dynamic origin–destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the relationship between OD flows and link flows is linearised. In this article the effect of using two types of linear relationship on the estimation process is analysed. It is shown that one type of linearisation implicitly assumes separability of the link flows, which can lead to biased results when dealing with congested networks. Advantages and disadvantages of adopting non-separable relationships are discussed. Another important source of error attributable to congestion dynamics is the presence of local minima in the objective function. It is illustrated that these local minima are the result of an incorrect interpretation of the information from the detectors. The theoretical findings are cast into a new methodology, which is successfully tested in a proof of concept. [less ▲]

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See detailDynamic Origin-Destination Matrix Estimation on Large-Scale Congested Networks Using A Hierarchical Decomposition Scheme
Frederix, Rodric; Viti, Francesco UL; Tampere, Chris M.J.

in Journal of Intelligent Transportation Systems (2014), 18(1), 51-66

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires ... [more ▼]

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires the calculation of the sensitivity of the link flows to all Origin Destination flows, in order to incorporate the effects of congestion spillback. This is however computationally infeasible for large-scale networks. To overcome this issue, we propose a hierarchical approach for off-line application that decomposes the dynamic OD estimation procedure in space. The main idea is to perform a more accurate dynamic OD estimation only on subareas where there is congestion spillback. The output of this estimation is then used as input for the OD estimation on the whole network. This hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods. The main advantage is that different OD estimation method can be used for different parts of the network as necessary. This allows applying more advanced and accurate, but more time consuming methods only where necessary. The hierarchical approach is tested on a study network and on a real network. In both cases the proposed methodology performs better than traditional OD estimation approaches, indicating its merit. [less ▲]

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See detailA dynamic route swapping and control algorithm that maximises network capacity accounting for node constraints and blocking back
Viti, Francesco UL; Huang, Wei; Smith, Mike

Scientific Conference (2016, July)

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See detailDynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator
Mombaerts, Laurent UL; Carignano, Alberto; Robertson, Fiona et al

in PLoS Computational Biology (2019)

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The ... [more ▼]

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems. [less ▲]

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See detailDynamical Modeling Techniques for Biological Time Series Data
Mombaerts, Laurent UL

Doctoral thesis (2019)

The present thesis is articulated over two main topics which have in common the modeling of the dynamical properties of complex biological systems from large-scale time-series data. On one hand, this ... [more ▼]

The present thesis is articulated over two main topics which have in common the modeling of the dynamical properties of complex biological systems from large-scale time-series data. On one hand, this thesis analyzes the inverse problem of reconstructing Gene Regulatory Networks (GRN) from gene expression data. This first topic seeks to reverse-engineer the transcriptional regulatory mechanisms involved in few biological systems of interest, vital to understand the specificities of their different responses. In the light of recent mathematical developments, a novel, flexible and interpretable modeling strategy is proposed to reconstruct the dynamical dependencies between genes from short-time series data. In addition, experimental trade-offs and optimal modeling strategies are investigated for given data availability. Consistent literature on these topics was previously surprisingly lacking. The proposed methodology is applied to the study of circadian rhythms, which consists in complex GRN driving most of daily biological activity across many species. On the other hand, this manuscript covers the characterization of dynamically differentiable brain states in Zebrafish in the context of epilepsy and epileptogenesis. Zebrafish larvae represent a valuable animal model for the study of epilepsy due to both their genetic and dynamical resemblance with humans. The fundamental premise of this research is the early apparition of subtle functional changes preceding the clinical symptoms of seizures. More generally, this idea, based on bifurcation theory, can be described by a progressive loss of resilience of the brain and ultimately, its transition from a healthy state to another characterizing the disease. First, the morphological signatures of seizures generated by distinct pathological mechanisms are investigated. For this purpose, a range of mathematical biomarkers that characterizes relevant dynamical aspects of the neurophysiological signals are considered. Such mathematical markers are later used to address the subtle manifestations of early epileptogenic activity. Finally, the feasibility of a probabilistic prediction model that indicates the susceptibility of seizure emergence over time is investigated. The existence of alternative stable system states and their sudden and dramatic changes have notably been observed in a wide range of complex systems such as in ecosystems, climate or financial markets. [less ▲]

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See detailDynamical structure analysis of sparsity and minimality heuristics for reconstruction of biochemical networks
Howes, R.; Eccleston, L. J.; Goncalves, Jorge UL et al

in The proceedings of the 47th IEEE Conference on Decision and Control (2008)

Network reconstruction, i.e. obtaining network structure from input-output information, is a central theme in systems biology. A variety of approaches aim to obtaining structural information from ... [more ▼]

Network reconstruction, i.e. obtaining network structure from input-output information, is a central theme in systems biology. A variety of approaches aim to obtaining structural information from available data. Previous work has introduced dynamical structure functions as a tool for posing and solving the network reconstruction problem. Even for linear time invariant systems, reconstruction requires specific additional information not generated in the typical system identification process. This paper demonstrates that such extra information can be obtained through a limited sequence of system identification experiments on structurally modified systems, analogous to gene silencing and overexpression experiments. In the absence of such extra information, we discuss whether combined assumptions of network sparsity and minimality contribute to the recovery of the network dynamical structure. We provide sufficient conditions for a transfer function to have a completely decoupled minimal realization, and demonstrate that every transfer function is arbitrarily close to one that admits a perfectly decoupled minimal realization. This indicates that the assumptions of sparsity and minimality alone do not lend insight into the network structure. [less ▲]

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See detailDynamical structure function identifiability conditions enabling signal structure reconstruction
Adebayo, J.; Southwick, T.; Chetty, V. et al

in The proceedings of the 51st IEEE Conference on Decision and Control (CDC) (2012, December)

Networks of controlled dynamical systems exhibit a variety of interconnection patterns that could be interpreted as the structure of the system. One such interpretation of system structure is a system's ... [more ▼]

Networks of controlled dynamical systems exhibit a variety of interconnection patterns that could be interpreted as the structure of the system. One such interpretation of system structure is a system's signal structure, characterized as the open-loop causal dependencies among manifest variables and represented by its dynamical structure function. Although this notion of structure is among the weakest available, previous work has shown that if no a priori structural information is known about the system, not even the Boolean structure of the dynamical structure function is identifiable. Consequently, one method previously suggested for obtaining the necessary a priori structural information is to leverage knowledge about target specificity of the controlled inputs. This work extends these results to demonstrate precisely the a priori structural information that is both necessary and sufficient to reconstruct the network from input-output data. This extension is important because it significantly broadens the applicability of the identifiability conditions, enabling the design of network reconstruction experiments that were previously impossible due to practical constraints on the types of actuation mechanisms available to the engineer or scientist. The work is motivated by the proteomics problem of reconstructing the Per-Arnt-Sim Kinase pathway used in the metabolism of sugars. [less ▲]

Detailed reference viewed: 62 (0 UL)