Attitude consensus using networks of uncalibrated cameras

in The proceedings of the 33rd Chinese Control Conference (2014)

This paper addresses the problem of consensus on SO(3) for networks of uncalibrated cameras. Under the assumption of a pinhole camera model, we prove convergence to the consensus manifold for two types of ... [more ▼]

This paper addresses the problem of consensus on SO(3) for networks of uncalibrated cameras. Under the assumption of a pinhole camera model, we prove convergence to the consensus manifold for two types of kinematic control laws, when only conjugate rotation matrices KRK-1 are available among the agents. In these conjugate rotations, the rotation matrices are distorted by the (unknown) intrinsic parameters of the cameras. For the conjugate rotations, we introduce distorted versions of well known local parameterizations of SO(3) and show consensus by using three types of control laws. The control laws are similar to the standard consensus protocol used for systems of agents with single integrator dynamics, where pairwise differences between the states of neighboring agents are used. By considering the restriction to the planar case (when all the rotations have the same rotational axes), we weaken the assumptions on the cameras in the system and consider networks where the camera matrices differ between agents. [less ▲]

Automated optimisation of stem cell-derived neuronal cell culture in three dimensional microfluidic device

Doctoral thesis (2019)

This dissertation is a compilation of publications and manuscripts that aim 1) to integrate an automated platform optimised for long term in vitro cell culture maintenance for Parkinson’s disease, long ... [more ▼]

This dissertation is a compilation of publications and manuscripts that aim 1) to integrate an automated platform optimised for long term in vitro cell culture maintenance for Parkinson’s disease, long term live cell imaging and the handling of many cell lines, 2) to combine physics principles with imaging techniques to optimise the seeding of Matrigel embedded human neuroepithelial stem cells into a three-dimensional microfluidic device, and 3) to combine engineering principles with cell biology to optimise the design of a three-dimensional microfluidic system based on phaseguide technology. In the first publication manuscript, we investigated Matrigel as a surrogate extracellular matrix in three-dimensional cell culture systems, including microfluidic cell culture. The study aimed at understanding and characterising the properties of Matrigel. Using classical rheological measurements of Matrigel (viscosity versus shear rate) in combination with fluorescence microscopy and fluorescent beads for particle image velocimetry measurements (velocity profiles), the shear rates experienced by cells in a microfluidic device for three-dimensional cell culture was characterised. We discussed how the result of which helped to mechanically optimise the use of Matrigel in microfluidic systems to minimise the shear stress experienced by cells during seeding in a microchannel. The second manuscript proposes a methodology to passively control the flow of media in a three-dimensional microfluidic channel. We used the fluid dynamic concept of similitude to dynamically replicate cerebral blood flow in a rectangular cross-sectional microchannel. This similarity model of a target cell type and a simple fluid flow mathematical prediction model was used to iterate the most optimum dimensions within some manufacturing constraints to adapt the design of the OrganoPlate, a cell culture plate fully compatible with laboratory automation, which allowed its re-dimension to achieve over 24h of flow for the culture of human neuroepithelial stem cells into midbrain specific dopaminergic neurons. In the third publication manuscript, we propose an automated cell culture platform optimised for long-term maintenance and monitoring of different cells in three-dimensional microfluidic cell culture devices. The system uses Standard in Laboratory Automation or SiLA, an open source standardisation which allows rapid software integration of laboratory automation hardware. The automation platform can be flexibly adapted to various experimental protocols and features time-lapse imaging microscopy for quality control and electrophysiology monitoring to assess cellular activity. It was biologically validated by differentiating Parkinson’s disease patient derived human neuroepithelial stem cells into midbrain specific dopaminergic neurons. This system is the first example of an automated Organ-on-a-Chip culture and has the potential to enable a versatile array of in vitro experiments for patient-specific disease modelling. Finally, the fourth manuscript initiates the assessment of the neuronal activity of induced pluripotent stem cell derived neurons from Parkinson’s Disease patients with LRRK2-G2019S mutations and isogenic controls. A novel image analysis pipeline that combined semi-automated neuronal segmentation and quantification of calcium transient properties was developed and used to analyse neuronal firing activity. It was found that LRRK2-G2019S mutants have shortened inter-spike intervals and an increased rate of spontaneous calcium transient induction than control cell lines. [less ▲]

Außenliegende Wandtemperierung. Praktische Umsetzung anhand eines Demonstrationsgebäudes und Ermittlung der Systemkosten.

in Bauphysik (2018), 40(4), 187-202

Availability-Based Dynamic Pricing on a Round-Trip Carsharing Service: an Experimental Design Using Agent-Based Simulation

Scientific Conference (2020, January)

AWJC Nozzle simulation by 6-way coupling of DEM+CFD+FEM using preCICE coupling library

Scientific Conference (2021, June 16)

The objective of this work is to study the particle-laden fluid-structure interaction within an Abrasive Water Jet Cutting Nozzle. Such coupling is needed to study the erosion phenomena caused by the ... [more ▼]

The objective of this work is to study the particle-laden fluid-structure interaction within an Abrasive Water Jet Cutting Nozzle. Such coupling is needed to study the erosion phenomena caused by the abrasive particles inside the nozzle. So far, the erosion in the nozzle was predicted only through the number of collisions, using only a simple DEM+CFD[1] coupling. To improve these predictions, we extend our model to a 6-way Eulerian-Lagrangian momentum coupling with DEM+CFD+FEM to account for deformations and vibrations in the nozzle. Our prototype uses the preCICE coupling library[2] to couple 3 numerical solvers: XDEM[3] (for the particle motion), OpenFOAM[4] (for the water jet), and CalculiX[5] (for the nozzle deformation). XDEM handles all the particle motions based on the fluid properties and flow conditions, and it calculates drag terms. In the fluid solver, particles are modeled as drag and are injected in the momentum equation as a source term. CalculiX uses the forces coming from the fluid solver and XDEM as boundary conditions to solve for the displacements. It is also used for computing the vibrations induced by particle impacts. . The preliminary 6-way DEM+CFD+FEM coupled simulation is able to capture the complex particle-laden multiphase fluid-structure interaction inside AWJC Nozzle. The erosion concentration zones are identified and are compared to DEM+CFD coupling[1]. The results obtained are planned to be used for predicting erosion intensity in addition to the concentration zones. In the future, we aim to compare the erosions predictions to experimental data in order to evaluate the suitability of our approach. The FEM module of the coupled simulation captures the vibration frequency induced by particles and compares it with the natural frequency of the nozzle. Thus opening up opportunities for further investigation and improvement of the Nozzle design. [less ▲]

Balancing Shareability and Positive Interdependence to Support Collaborative Problem-Solving on Interactive Tabletops

in Advances in Human-Computer Interaction (2021)

To support collaboration, researchers from different fields have proposed the design principles of shareability (engaging users in shared interactions around the same content) and positive interdependence ... [more ▼]

To support collaboration, researchers from different fields have proposed the design principles of shareability (engaging users in shared interactions around the same content) and positive interdependence (distributing roles and information to make users dependent on each other). While, on its own, each principle was shown to successfully support collaboration in different contexts, these principles are also partially conflicting, and their combination creates several design challenges. This paper describes how shareability and positive interdependency were jointly implemented in an interactive tabletop-mediated environment called Orbitia, with the aim of inducing collaboration between three adult participants. We present the design details and rationale behind the proposed application. Furthermore, we describe the results of an empirical evaluation focusing on joint problem-solving efficiency, collaboration styles, participation equity, and perceived collaboration effectiveness. [less ▲]

A Bayesian approach to activity-specific demand estimation

Scientific Conference (2021, May)

Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights

E-print/Working paper (n.d.)

We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material ... [more ▼]

We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single spring is considered, for which the stress-strain curves are artificially created. Besides offering a didactic introduction to BI, this paper proposes an approach to incorporate statistical errors both in the measured stresses, and in the measured strains. It is assumed that the uncertainty is only due to measurement errors and the material is homogeneous. Furthermore, a number of possible misconceptions on BI are highlighted based on the purely elastic case. [less ▲]

A Bayesian inversion approach to recovering material parameters in hyperelastic solids using dolfin-adjoint

Presentation (2015, July 01)

In the first part of the talk I will describe in general terms the link between classical optimisation techniques and the Bayesian approach to statistical inversion as outlined in the seminal book of ... [more ▼]

In the first part of the talk I will describe in general terms the link between classical optimisation techniques and the Bayesian approach to statistical inversion as outlined in the seminal book of [Kaipio and Somersalo, 2005]. Under the assumption of an additive Gaussian noise model, a Gaussian prior distribution and a linear parameter-to-observable map, it is possible to uniquely characterise the Bayesian posterior as Gaussian with the maximum aposteriori (MAP) point equal to the minimum of a classic regularised minimisation problem and covariance matrix equal to the inverse of the Hessian of the functional evaluated at the MAP point. I will also discuss techniques that can be used when these assumptions break down. In the second part of the talk I will describe a method implemented within dolfin-adjoint [Funke and Farrell, arXiv 2013] to quantify the uncertainty in the recovered material parameters of a hyperelastic solid from partial and noisy observations of the displacement field in the domain. The finite element discretisation of the adjoint and higher-order adjoint (Hessian) equations are derived automatically from the high-level UFL representation of the problem. The resulting equations are solved using PETSc. I will concentrate on finding the eigenvalue decomposition of the posterior covariance matrix (Hessian). The eigenvectors associated with the lowest eigenvalues of the Hessian correspond with the directions in parameter space least constrained by the observations [Flath et al. 2011]. This eigenvalue problem is tricky to solve efficiently because the Hessian is very large (on the order of the number of parameters) and dense (meaning that only its action on a vector can be calculated, each involving considerable expense). Finally, I will show some illustrative examples including the uncertainty associated with deriving the material properties of a 3D hyperelastic block with a stiff inclusion with knowledge only of the displacements on the boundary of the domain. J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, vol. 160. New York: Springer-Verlag, 2005. S. W. Funke and P. E. Farrell, “A framework for automated PDE-constrained optimisation,” arXiv:1302.3894 [cs], Feb. 2013. H. P. Flath, L. C. Wilcox, V. Akçelik, J. Hill, B. van Bloemen Waanders, and O. Ghattas, “Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations,” SIAM J. Sci. Comput., vol. 33, no. 1, pp. 407–432, Feb. 2011. [less ▲]

Bearing capacity of steel fiber reinforced concrete flat slabs

Doctoral thesis (2009)