Data Centric Engineering and Data-Driven Modelling - Computational Engineering Lab Report 2019

Report (2019)

https://www.cambridge.org/core/journals/data-centric-engineering

DATA DRIVEN SURGICAL SIMULATIONS

Scientific Conference (2020, July)

The DAta Protection REgulation COmpliance Model

in IEEE Security and Privacy (2019), 17(6), 37-45

Understanding whether certain technical measures comply with the General Data Protection Regulation’s (GDPR’s) principles is complex legal work. This article describes a model of the GDPR that allows for ... [more ▼]

Understanding whether certain technical measures comply with the General Data Protection Regulation’s (GDPR’s) principles is complex legal work. This article describes a model of the GDPR that allows for semiautomatic processing of legal text and the leveraging of state-of-the-art legal informatics approaches, which are useful for legal reasoning, software design, information retrieval, or compliance checking. [less ▲]

Data Science in Europe

in International Journal of Data Science and Analytics (2018)

Data-driven modelling and simulation: fracture and medical simulations

Presentation (2018, February 08)

Predicting failure in aircraft structures – simulating fracture across scales and times You could fly every day of your life in a commercial aircraft for twenty thousand years without suffering a fatal ... [more ▼]

Predicting failure in aircraft structures – simulating fracture across scales and times You could fly every day of your life in a commercial aircraft for twenty thousand years without suffering a fatal accident. This extraordinary level of safety is the product of decades of engineering and materials science research. Simultaneously, engineers have strived to produce lighter and stronger aircraft, with increased range and metals have thus been gradually replaced by lighter advanced composite materials which take up more than half of the structural weight of today's most advanced aircraft. Such progress has been largely enabled by modeling and simulation of materials and structures, which have revolutionized design by enabling engineers to investigate virtually various design strategies. This presentation will focus on the challenges which have been posed, are posed, and will be posed to such modeling and simulation tools in the strive to predict the durability of lighter, stronger, longer-ranging and more reliable aircraft. [less ▲]

Dealing with interfaces in partitioned model order reduction for application to nonlinear problems

Scientific Conference (2013)

We propose a reduced order modelling technique based on a partitioning of the domain of study in the context of para- metric nonlinear problems. A formulation of the reduction of the displacement and of ... [more ▼]

We propose a reduced order modelling technique based on a partitioning of the domain of study in the context of para- metric nonlinear problems. A formulation of the reduction of the displacement and of the interface tractions linking subdomains to each others will be performed in a FETI context. [less ▲]

Decentralised minimal-time consensus

in The proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) (2011)

This study considers the discrete-time dynamics of a network of agents that exchange information according to the nearest-neighbour protocol under which all agents are guaranteed to reach consensus ... [more ▼]

This study considers the discrete-time dynamics of a network of agents that exchange information according to the nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the consensus value of the whole network in finite time using only the minimal number of successive values of its own history. We show that this minimal number of steps is related to a Jordan block decomposition of the network dynamics and present an algorithm to obtain the minimal number of steps in question by checking a rank condition on a Hankel matrix of the local observations. Furthermore, we prove that the minimal number of steps is related to other algebraic and graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the underlying graph topology. [less ▲]

Decentralised minimum-time consensus

in Automatica (2013), 49(5), 1227-1235

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically ... [more ▼]

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the final consensus value of the whole network in finite time using the minimum number of successive values of its own state history. We show that the minimum number of steps is related to a Jordan block decomposition of the network dynamics, and present an algorithm to compute the final consensus value in the minimum number of steps by checking a rank condition of a Hankel matrix of local observations. Furthermore, we prove that the minimum number of steps is related to graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the minimum external equitable partition. [less ▲]

Decompositions of the optimal dispatching problem of electric and electric-hybrid buses with energy constraints for Luxembourg City

Scientific Conference (2018, September)

We are a team of engineers working on a concrete project of Mobility in Luxembourg. We want to solve the problem of optimally determining the sequence of electric and hybrid electric buses, considering ... [more ▼]

We are a team of engineers working on a concrete project of Mobility in Luxembourg. We want to solve the problem of optimally determining the sequence of electric and hybrid electric buses, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities) and at the same time ensure a high level of quality of service for the user satisfaction. The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by twenty sets of constraints, ranging from scheduling adherence to discharge-recharge dynamics. Individual operational costs at the bus level (cost of running an electric / non-electric bus per km, cost of recharging) and at the trip level (penalty due to failed schedule adherence) are fully parametrised, allowing for extensive sensitivity analysis. We investigate a real-life case study based in the city of Luxembourg, where the objective is to reach the all-electric mode for principal urban buses network. Through the model we investigate: the minimum amount of electric buses necessary to perform a day’s schedule for two currently partially electrified lines, without resorting to conventional internal combustion alternatives; the impact of electrifying two additional lines, specifically considering the trade-offs related to either adding new buses or new charging stations at the bus terminal. Finally, we studied how to best decompose the overall problem in several smaller problems, to be able to solve also realistic scenarios and using large real data sets from the Mobility Data owner of Luxembourg. We analysed and compared two kinds of decomposition: a bus line-based decomposition, and a time-based decomposition. [less ▲]

Deep neural network with high-order neuron for the prediction of foamed concrete strength

in Computer-Aided Civil and Infrastructure Engineering (2018)

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the ... [more ▼]

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross-entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C-ANN) and second-order artificial neural network (SO-ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high-performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C-ANN and SO-ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water-to-cement and sand-to-cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete. [less ▲]