LABORATORY EXPERIMENTS OF PULSED SUBAQEOUS SEDIMENT DENSITY FLOWS: INTERNAL STRATIFICATION AND LAYERING

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Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Roland Schwan, Melanie Nicole Zeilinger, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

Touradj Ebrahimi

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Te ...

IEEE COMPUTER SOC2023

Quantified Effects of Multiple Parameters on Inputs and Potential Sources of Microplastics from a Typical River Flowing into the Sea

Florian Frédéric Vincent Breider, Yang Liu, Wenxin Liu

As an important connection of terrestrial and marine ecosystems, rivers input multitudinous (micro)plastics into the oceans. However, comparisons of field and estimated riverine inputs of (micro)plastics and contributions of multiple parameters are limited ...

2022

Determining crack kinematics from imaged crack patterns

Katrin Beyer, Bryan German Pantoja Rosero, Amir Rezaie, Ketson Roberto Maximiano Dos Santos

Determining the relationship between the cause of damage and the subsequent structural behavior of infrastructure systems requires an accurate characterization of the propagation of cracks, which represents the evolution of the damage state. When no inform ...

2022

Unconfined Plunging of a Hyperpycnal River Plume Over a Sloping Bed and Its Lateral Spreading: Laboratory Experiments and Numerical Modeling

David Andrew Barry, Ulrich Lemmin, Koen Blanckaert, Haoran Shi

Hyperpycnal (negatively buoyant) river inflow into lakes and oceans often develops three-dimensional (3D) plunging flow patterns when laterally unconfined. To determine the 3D flow pattern characteristics, laboratory experiments of laterally unconfined plu ...

American Geophysical Union2022

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars

Giovanni De Cesare, Christian Mörtl

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

International Association for Hydro-Environment Engineering and Research (IAHR)2022

3D printing of elastomeric mechanical sensors designed for human motion monitoring

Ryan Mitchell van Dommelen

In this thesis several advances are made to the emerging field of 3D printed mechanical sensors. Techniques and processes were developed to enable the integration of highly conductive, and capacitive and piezoresistive sensing features embedded within 3D p ...

EPFL2022

Attribute Prediction as Multiple Instance Learning

Devis Tuia, Diego Marcos Gonzalez

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

2022

A life cycle analysis of novel lightweight composite processes: Reducing the environmental footprint of automotive structures

Véronique Michaud, Baris Çaglar, Colin Gomez, Vincent Werlen, Christian Rytka

In this study, three novel thermoplastic impregnation processes were analyzed towards automotive applications. The first process is thermoplastic compression resin transfer molding in which a glass fiber mat is impregnated in through thickness by a thermop ...

ELSEVIER SCI LTD2022

Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer

Marc Conrad Russwurm

Data imputation of incomplete image sequences is an essential prerequisite for analyzing and monitoring all development stages of plants in precision agriculture. For this purpose, we propose a conditional Wasserstein generative adversarial network TransGr ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

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