Extracting the spatio-temporal variations in the gravity field recovered from GRACE spatial mission: methods and geophysical applications

Doctoral thesis (2019)

Measurements of the spatio-temporal variations of Earth’s gravity field recovered from the Gravity Recovery and Climate Experiment (GRACE) mission have led to unprecedented insights into large spatial ... [more ▼]

Measurements of the spatio-temporal variations of Earth’s gravity field recovered from the Gravity Recovery and Climate Experiment (GRACE) mission have led to unprecedented insights into large spatial mass redistribution at secular, seasonal, and sub-seasonal time scales. GRACE solutions from various processing centers, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial and temporal patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, several methods have been proposed. Among these, methods based on filtering techniques require a priori assumptions regarding the spatio-temporal structure of the errors. Despite the large effort to improve the quality of GRACE data for always finer geophysical applications, removing noise remains a problematic question as discussed in Chapter 1. In this thesis, we explore an alternative approach, using a spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA) described in Chapter 2. M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA simultaneously on 13 years of GRACE spherical harmonics solutions from five different processing centers. We show that the method allows for the extraction of common modes of variability between solutions, and removal of the solution-specific spatio-temporal errors arising from each processing strategies. In particular, the method filters out efficiently the spurious North-South stripes, most likely caused by aliasing of the imperfect geophysical correction models of known phenomena. In Chapter 3, we compare our GRACE solution to other spherical harmonics solutions and to mass concentration (mascon) solutions which use a priori information on the spatio-temporal pattern of geophysical signals. We also compare performance of our M-SSA GRACE solution with respect to others by predicting surface displacements induced by GRACE-derived mass loading and comparing results with independent displacement data from stations of the Global Navigation Satellite System (GNSS). Finally, in Chapter 4 we discuss the possible application of a refined GRACE solution to answer debated post-glacial rebound questions. More precisely, we focus on separating the post-glacial rebound signal related to past ice melting and the present ice melting in the region of South Georgia. [less ▲]

Fast Stochastic Non-linear Model Predictive Control for Electric Vehicle Advanced Driver Assistance Systems

in 13th IEEE International Conference on Vehicular Electronics and Safety, Vienna, Austria 27-28 June 2017 (2017, June 27)

Semi-autonomous driving assistance systems have a high potential to improve the safety and efficiency of the battery electric vehicles that are enduring limited cruising range. This paper presents an ... [more ▼]

Semi-autonomous driving assistance systems have a high potential to improve the safety and efficiency of the battery electric vehicles that are enduring limited cruising range. This paper presents an ecologically advanced driver assistance system to extend the functionality of the adaptive cruise control system. A real-time stochastic non-linear model predictive controller with probabilistic constraints is presented to compute on-line the safe and energy-efficient cruising velocity profile. The individual chance-constraint is reformulated into a convex second-order cone constraint which is robust for a general class of probability distributions. Finally, the performance of proposed approach in terms of states regulation, constraints fulfilment, and energy efficiency is evaluated on a battery electric vehicle. [less ▲]

A fast, certified and "tuning-free" two-field reduced basis method for the metamodelling of parametrised elasticity problems

in Computer Methods in Applied Mechanics and Engineering (2015)

This paper proposes a new reduced basis algorithm for the metamodelling of parametrised elliptic problems. The developments rely on the Constitutive Relation Error (CRE), and the construction of separate ... [more ▼]

This paper proposes a new reduced basis algorithm for the metamodelling of parametrised elliptic problems. The developments rely on the Constitutive Relation Error (CRE), and the construction of separate reduced order models for the primal variable (displacement) and flux (stress) fields. A two-field Greedy sampling strategy is proposed to construct these two fields simultaneously and efficient manner: at each iteration, one of the two fields is enriched by increasing the dimension of its reduced space in such a way that the CRE is minimised. This sampling strategy is then used as a basis to construct goal-oriented reduced order modelling. The resulting algorithm is certified and "tuning-free": the only requirement from the engineer is the level of accuracy that is desired for each of the outputs of the surrogate. It is also one order of magnitude more efficient in terms of computational expenses than competing methodologies. [less ▲]

Federal Blockchain Infrastructure Asylum (FLORA) - Piloting and evaluation of the FLORA support system in the context of the AnkER facility Dresden

Report (2022)

Federated Learning for Credit Risk Assessment

in Proceedings of the 56th Hawaii International Conference on System Sciences (2023, January 03)

Credit risk assessment is a standard procedure for financial institutions (FIs) when estimating their credit risk exposure. It involves the gathering and processing quantitative and qualitative datasets ... [more ▼]

Credit risk assessment is a standard procedure for financial institutions (FIs) when estimating their credit risk exposure. It involves the gathering and processing quantitative and qualitative datasets to estimate whether an individual or entity will be able to make future required payments. To ensure effective processing of this data, FIs increasingly use machine learning methods. Large FIs often have more powerful models as they can access larger datasets. In this paper, we present a Federated Learning prototype that allows smaller FIs to compete by training in a cooperative fashion a machine learning model which combines key data derived from several smaller datasets. We test our prototype on an historical mortgage dataset and empirically demonstrate the benefits of Federated Learning for smaller FIs. We conclude that smaller FIs can expect a significant performance increase in their credit risk assessment models by using collaborative machine learning. [less ▲]

Finding surface Lyapunov functions through sum-of-squares Programming

in Proceedings of the American Control Conference 2004 (2004)

This work presents a sum-of-squares method to construct polynomial surface Lyapunov functions (SuLF) of arbitrary order for the impact maps of limit cycles in piecewise linear systems (PLS). This work ... [more ▼]

This work presents a sum-of-squares method to construct polynomial surface Lyapunov functions (SuLF) of arbitrary order for the impact maps of limit cycles in piecewise linear systems (PLS). This work extends previous results on stability analysis of such limit cycles, which utilized quadratic SuLFs. This paper also discusses an initial study of hierarchical jump linear systems where the switching is driven by feedback of low-level dynamical system states and a Markovian process. [less ▲]

Finite element analysis on implicitly defined domains: An accurate representation based on arbitrary parametric surfaces

in Computer Methods in Applied Mechanics and Engineering (2011), 200(5-8), 774-796

In this paper, we present some novel results and ideas for robust and accurate implicit representation of geometric surfaces in finite element analysis. The novel contributions of this paper are threefold ... [more ▼]

In this paper, we present some novel results and ideas for robust and accurate implicit representation of geometric surfaces in finite element analysis. The novel contributions of this paper are threefold: (1) describe and validate a method to represent arbitrary parametric surfaces implicitly; (2) represent arbitrary solids implicitly, including sharp features using level sets and boolean operations; (3) impose arbitrary Dirichlet and Neumann boundary conditions on the resulting implicitly defined boundaries. The methods proposed do not require local refinement of the finite element mesh in regions of high curvature, ensure the independence of the domain's volume on the mesh, do not rely on boundary regularization, and are well suited to methods based on fixed grids such as the extended finite element method (XFEM). Numerical examples are presented to demonstrate the robustness and effectiveness of the proposed approach and show that it is possible to achieve optimal convergence rates using a fully implicit representation of object boundaries. This approach is one step in the desired direction of tying numerical simulations to computer aided design (CAD), similarly to the isogeometric analysis paradigm. © 2010 Elsevier B.V. [less ▲]

Finite-time road grade computation for a vehicle platoon

in IEEE (2014)

Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the ... [more ▼]

Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the road grade in a finite number of time-steps by using only its own successive velocity measurements. Simulations then illustrate the theoretical results. These new results can be applied to real-world vehicle platooning problems to reduce fuel consumption and carbon dioxide emissions. [less ▲]

FLEXCAP – Cost competitive flexible process for highly aesthetic closures manufacturing

in Kakogiannos, Ioannis; Hichri, Bassem; Plapper, Peter (Eds.) Robotix-Academy Conference for Industrial Robotics (RACIR) 2018 (2018, November)

Flood Detection On Low Cost Orbital Hardware

Scientific Conference (2019, October)

Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the ... [more ▼]

Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability to automatically identify flooded regions in images. We describe a prototypical flood segmentation system, identifying cloud, water and land, that could be deployed on a constellation of small satellites, performing processing on board to reduce downlink bandwidth by 2 orders of magnitude. We target PhiSat-1, part of the FSSCAT mission, which is planned to be launched by the European Space Agency (ESA) near the start of 2020 as a proof of concept for this new technology. [less ▲]