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Related publications (29)

Model-informed machine learning for multi-component T2 relaxometry

Jean-Philippe Thiran, Meritxell Bach Cuadra, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Elda Fischi Gomez, Gian Franco Piredda, Thomas Yu, Cristina Granziera, Muhamed Barakovic, Marco Pizzolato

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to comb ...

2020

Flexible Support for Gas-Lubricated Bearing Bushings

Jürg Alexander Schiffmann

Herringbone-grooved journal bearings (HGJBs) for high-speed turbo-machinery are often supported on O-rings due to their favorable stiffness and damping characteristics for stability and alignment reasons. They yield limited lifetime at high temperatures, i ...

2020

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Ian Smith, Sai Ganesh Sarvotham Pai, Masoud Sanayei

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and sys ...

2021

The bias of dark matter tracers: assessing the accuracy of mapping techniques

Cheng Zhao

We present a comparison between approximated methods for the construction of mock catalogues based on the halo-bias mapping technique. To this end, we use as reference a high-resolution N-body simulation of 38403 dark matter particles on a 400 h(-1) Mpc cu ...

OXFORD UNIV PRESS2020

Improve the Model Stability of Dam's Displacement Prediction Using a Numerical-Statistical Combined Model

Zhenzhu Meng, Yating Hu, Chenfei Shao

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to im ...

2020

Camera Calibration Models and Methods for Corridor Mapping with UAVs

Jan Skaloud, Davide Antonio Cucci, Emmanuel Pierre Quentin Clédat

Camera calibration refers to the modeling of the relationship between the coordinates of object points and their projections on the image plane. This is usually done by parametric models that describe the physical properties of the lens systems and camera ...

2020

Better Patch Stitching for Parametric Surface Reconstruction

Pascal Fua, Mathieu Salzmann, Jan Bednarík, Zhantao Deng

Recently, parametric mappings have emerged as highly effective surface representations, yielding low reconstruction error. In particular, the latest works represent the target shape as an atlas of multiple mappings, which can closely encode object parts. A ...

IEEE2020

Parameter Estimation of Three-Phase Untransposed Short Transmission Lines from Synchrophasor Measurements

Jean-Yves Le Boudec, Mario Paolone, Arpan Mukhopadhyay

We present a new approach for estimating the parameters of three-phase untransposed electrically short transmission lines using voltage/current synchrophasor measurements obtained from phasor measurement units. The parameters to be estimated are the entrie ...

2020

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

Alireza Karimi, Seyed Sohail Madani

In this paper, a data-driven method for controller design with constraints on the positive-realness of closed-loop transfer functions over an arbitrary set of frequencies is proposed. The positive-realness of a closed-loop transfer function is represented ...

IEEE2020

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