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See detailData science meets computational mechanics
Dehghani, Hamidreza UL; Zilian, Andreas UL

Report (2021)

Detailed reference viewed: 77 (2 UL)
See detailData Science, Learning by Latent Structures, and Knowledge Discovery
Lausen, Berthold; Krolak-Schwerdt, Sabine UL; Böhmer, Matthias UL

Book published by Springer (2015)

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See detailData-driven modelling and simulation: fracture and medical simulations
Bordas, Stéphane UL

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 ▲]

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See detailData-driven preventive maintenance for a heterogeneous machine portfolio
Deprez, Laurens; Antonio, Katrien; Arts, Joachim UL et al

in Operations Research Letters (2023)

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See detailDBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
Baniasadi, Mehri UL; Petersen, Mikkel V.; Goncalves, Jorge UL et al

in Human Brain Mapping (2022)

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by ... [more ▼]

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage. [less ▲]

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See detailDeadzone-Quadratic Penalty Function for Predictive Extended Cruise Control with Experimental Validation
Sajadi Alamdari, Seyed Amin UL; Voos, Holger UL; Darouach, Mohamed

in ROBOT 2017: Third Iberian Robotics Conference, Sevilla, Spain 22-24 November 2017 (2017, November)

Battery Electric Vehicles have high potentials for the modern transportations, however, they are facing limited cruising range. To address this limitation, we present a semi-autonomous ecological driver ... [more ▼]

Battery Electric Vehicles have high potentials for the modern transportations, however, they are facing limited cruising range. To address this limitation, we present a semi-autonomous ecological driver assistance system to regulate the velocity with energy-efficient techniques. The main contribution of this paper is the design of a real-time nonlinear receding horizon optimal controller to plan the online cost-effective cruising velocity. Instead of conventional L2-norms, a deadzone-quadratic penalty function for the nonlinear model predictive controller is proposed. Obtained field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of real-time energy-efficient velocity regulation and constraints satisfaction. [less ▲]

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See detailDealing with interfaces in partitioned model order reduction for application to nonlinear problems
Goury, Olivier; Kerfriden, Pierre; Bordas, Stéphane UL

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 ▲]

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See detailDealing with requirements: Influences on Idea Generation in the Early Stages of Product Development
Gericke, Kilian UL; Schmidt-Kretschmer, Michael; Blessing, Lucienne UL

in Proceedings of DTRS7: Design Meeting Protocols (2007)

This paper aims at identifying factors which influence the number of ideas generated during a brainstorming meeting as part of an industrial mechanical engineering design project. A framework for ... [more ▼]

This paper aims at identifying factors which influence the number of ideas generated during a brainstorming meeting as part of an industrial mechanical engineering design project. A framework for describing groups of influencing factors and their relationships is used. As a result of an explorative, comparative protocol analysis of two design meetings the influence of some factors is described e.g. the formulation of the design task description and the sequence of the process steps. [less ▲]

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See detailDecentralised minimal-time consensus
Yuan, Y.; Stan, G.-B.; Barahona, M. et al

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 ▲]

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See detailDecentralised minimal-time dynamic consensus
Yuan, Y.; Liu, J.; Murray, R. M. et al

in The proceedings of the 2012 American Control Conference (ACC) (2012)

This paper considers a group of agents that aim to reach an agreement on individually measured time-varying signals by local communication. In contrast to static network averaging problem, the consensus ... [more ▼]

This paper considers a group of agents that aim to reach an agreement on individually measured time-varying signals by local communication. In contrast to static network averaging problem, the consensus we mean in this paper is reached in a dynamic sense. A discrete-time dynamic average consensus protocol can be designed to allow all the agents tracking the average of their reference inputs asymptotically. We propose a minimal-time dynamic consensus algorithm, which only utilises minimal number of local observations of randomly picked node in a network to compute the final consensus signal. Our results illustrate that with memory and computational ability, the running time of distributed averaging algorithms can be indeed improved dramatically using local information as suggested by Olshevsky and Tsitsiklis. [less ▲]

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See detailDecentralised minimum-time consensus
Yuan, Ye; Stan, Guy-Bart; Shi, Ling et al

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 ▲]

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See detailDecentralized final value theorem for discrete-time LTI systems with application to minimal time distributed consensus
Yuan, Y.; Stan, G. B. V.; Shi, L. et al

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

In this study, we consider an unknown discrete-time, linear time-invariant, autonomous system and characterise, the minimal number of discrete-time steps necessary to compute the asymptotic final value of ... [more ▼]

In this study, we consider an unknown discrete-time, linear time-invariant, autonomous system and characterise, the minimal number of discrete-time steps necessary to compute the asymptotic final value of a state. The results presented in this paper have a direct link with the celebrated final value theorem. We apply these results to the design of an algorithm for minimal-time distributed consensus and illustrate the results on an example. [less ▲]

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See detailDecompositions of the optimal dispatching problem of electric and electric-hybrid buses with energy constraints for Luxembourg City
Picarelli, Erika; Rinaldi, Marco UL; Viti, Francesco UL et al

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 ▲]

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See detailA Decoupling Approach to Design Observers for Polytopic Takagi-Sugeno Models Subject to Unknown Inputs
Bezzaoucha, Souad UL; Voos, Holger UL; Davila, Jorge et al

in Proceedings of the 2018 American Control Conference (2018, June 27)

A decoupling approach for state estimation of nonlinear systems represented in the polytopic Takagi-Sugeno with unmeasurable premise variables subject to unknown inputs is proposed in this paper. The idea ... [more ▼]

A decoupling approach for state estimation of nonlinear systems represented in the polytopic Takagi-Sugeno with unmeasurable premise variables subject to unknown inputs is proposed in this paper. The idea consists in defining a state and unknown input transformations in order to divide the state vector into two parts, a measurable part and an observable one (decoupled from the unknown input). A classical Luenberger observer to estimate the unmeasurable part is then designed and given in terms of Linear Matrix Inequalities (LMIs) conditions. A numerical example is also presented in order to illustrate the proposed approach. [less ▲]

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See detailDeep Learning for Ground and Non-ground Surface Separation: A Feature-based Semantic Segmentation Algorithm for Point Cloud Classification
Nurunnabi, Abdul Awal Md UL; Lindenbergh, Roderik; Teferle, Felix Norman UL

E-print/Working paper (2022)

Precise ground surface topography is crucial for 3D city analysis, digital terrain modeling, natural disaster monitoring, high-density map generation, and autonomous navigation to name a few. Deep ... [more ▼]

Precise ground surface topography is crucial for 3D city analysis, digital terrain modeling, natural disaster monitoring, high-density map generation, and autonomous navigation to name a few. Deep learning (DL; LeCun, et al., 2015), a division of machine learning (ML), has been achieving unparalleled success in image processing, and recently demonstrated a huge potential for point cloud analysis. This article presents a feature-based DL algorithm that classifies ground and non-ground points in aerial laser scanning point clouds. Recent advancements of remote sensing technologies make it possible digitizing the real world in a near automated fashion. LiDAR (Light Detection and Ranging) based point clouds that are a type of remotely sensed georeferenced data, providing detailed 3D information on objects and environment have been recognized as one of the most powerful means of digitization. Unlike imagery, point clouds are unstructured, sparse and of irregular data format which creates many challenges, but also provides huge opportunities for capturing geometric details of scanned surfaces with millimeter accuracy. Classifying and separating non-ground points from ground points largely reduce data volumes for consecutive analyses of either ground or non-ground surfaces, which consequently saves cost and labor, and simplifies further analysis. [less ▲]

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See detailDeep neural network with high-order neuron for the prediction of foamed concrete strength
Nguyen, Tuan; Kashani, Alireza; Ngo, Tuan et al

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 ▲]

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See detailDeFi, Not So Decentralized: The Measured Distribution of Voting Rights
Barbereau, Tom Josua UL; Smethurst, Reilly UL; Papageorgiou, Orestis UL et al

in Proceedings of the Hawaii International Conference on System Sciences 2022 (2022, January)

Bitcoin and Ethereum are frequently promoted as decentralized, but developers and academics question their actual decentralization. This motivates further experiments with public permissionless ... [more ▼]

Bitcoin and Ethereum are frequently promoted as decentralized, but developers and academics question their actual decentralization. This motivates further experiments with public permissionless blockchains to achieve decentralization along technical, economic, and political lines. The distribution of tokenized voting rights aims for political decentralization. Tokenized voting rights achieved notoriety within the nascent field of decentralized finance (DeFi) in 2020. As an alternative to centralized crypto-asset exchanges and lending platforms (owned by companies like Coinbase and Celsius), DeFi developers typically create non-custodial projects that are not majority-owned or managed by legal entities. Holders of tokenized voting rights can instead govern DeFi projects. To scrutinize DeFi’s distributed governance strategies, we conducted a multiple-case study of non-custodial, Ethereum-based DeFi projects: Uniswap, Maker, SushiSwap, Yearn Finance, and UMA. Our findings are novel and surprising: quantitative evaluations of DeFi’s distributed governance strategies reveal a failure to achieve political decentralization. [less ▲]

Detailed reference viewed: 809 (59 UL)