Publications related to Sampling error

Quantitative measurement and comparison of breakthroughs inside the gas diffusion layer using lattice Boltzmann method and computed tomography scan

Jan Van Herle, Emad Oveisi, Hossein Pourrahmani, Hamza Moussaoui

In Proton Exchange Membrane Fuel Cells (PEMFCs), the presence of residual water within the Gas Diffusion Layer (GDL) poses challenges during cold starts and accelerates degradation. A computational model based on the Lattice Boltzmann Method (LBM) was deve ...

Nature Portfolio2024

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann, Zhiye Wang

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

Pre-annealing for improved LPCVD deposited boron-doped poly-Si hole-selective contacts

Franz-Josef Haug, Luca Massimiliano Antognini, Josua Andreas Stückelberger, Xinyu Zhang, Zhao Wang, Jie Yang

We demonstrate the beneficial effect of a pre-annealing step prior to the boron diffusion on passivation and contact resistivity of industrially LPCVD deposited poly-Si/SiOX hole-selective contacts. We investigate the influence of the pre-annealing tempera ...

ELSEVIER2023

Epigenome-wide DNA methylation in externalizing behaviours: A review and combined analysis

Maria del Carmen Sandi Perez, Mandy Meijer

DNA methylation (DNAm) is one of the most frequently studied epigenetic mechanisms facilitating the interplay of genomic and environmental factors, which can contribute to externalizing behaviours and related psychiatric disorders. Previous epigenome-wide ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Origin of the enhanced pseudo-elasticity of additively manufactured Fe-17Mn-5Si-4Ni-10Cr-(V, C) shape memory alloy fabricated by laser powder bed fusion

Christian Leinenbach, Irene Ferretto

This study investigated the origin of the enhanced pseudo-elasticity of additively manufactured vanadium carbide-containing Fe-based shape memory alloys. The aged samples with two different starting microstructures, as-built and solution-treated, showed co ...

Lausanne2023

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

Dusan Licina, Shen Yang, Marouane Merizak, Akila Muthalagu

Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...

PERGAMON-ELSEVIER SCIENCE LTD2023

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Jürg Alexander Schiffmann, Cyril Picard, Faez Ahmed

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Amer Soc Mechanical Engineers2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

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Sampling error

Influence of Si3N4 fillers and pyrolysis profile on the microstructure of additively manufactured silicon carbonitride ceramics derived from polyvinylsilazane

Quantitative measurement and comparison of breakthroughs inside the gas diffusion layer using lattice Boltzmann method and computed tomography scan

Center-aware Adversarial Augmentation for Single Domain Generalization

Pre-annealing for improved LPCVD deposited boron-doped poly-Si hole-selective contacts

Epigenome-wide DNA methylation in externalizing behaviours: A review and combined analysis

Origin of the enhanced pseudo-elasticity of additively manufactured Fe-17Mn-5Si-4Ni-10Cr-(V, C) shape memory alloy fabricated by laser powder bed fusion

Stochastic adaptive resampling for the estimation of discrete choice models

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

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Chat with Graph Search

Quantitative measurement and comparison of breakthroughs inside the gas diffusion layer using lattice Boltzmann method and computed tomography scan

Epigenome-wide DNA methylation in externalizing behaviours: A review and combined analysis

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

Influence of Si3N4 fillers and pyrolysis profile on the microstructure of additively manufactured silicon carbonitride ceramics derived from polyvinylsilazane

Center-aware Adversarial Augmentation for Single Domain Generalization

Origin of the enhanced pseudo-elasticity of additively manufactured Fe-17Mn-5Si-4Ni-10Cr-(V, C) shape memory alloy fabricated by laser powder bed fusion

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Stochastic adaptive resampling for the estimation of discrete choice models

Pre-annealing for improved LPCVD deposited boron-doped poly-Si hole-selective contacts

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins