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Augmenting Aircraft Engine Flight Data with Generative Adversarial Networks for Fault Detection

Cisneros Acevedo, Daniel (author)

Recent advancements in deep learning for aircraft engine fault detection have been predominantly focused on research using simulated datasets. Despite significant progress, the gap between simulated and real-world data underscores a pressing need for models that are more applicable and adaptable to the aerospace industry. This discrepancy stems...

master thesis 2024

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Comparative assessment of generative models for transformer- and consumer-level load profiles generation

Xia, W. (author), Huang, Hanyue (author), Duque, Edgar Mauricio Salazar (author), Shengren, H. (author), Palensky, P. (author), Vergara Barrios, P.P. (author)

Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating...

journal article 2024

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Probabilistic Regression of wind turbine loads using Conditional Generative Adversarial Networks

Yap, Li-Toong (author)

Site analysis to determine the loads experienced by wind turbines based on site-specific environmental conditions is typically done using either coupled aero-servo-elastic simulations for onshore wind turbines or coupled aero-servo-hydroelastic simulations in the case of offshore wind turbines. These simulations become computationally expensive...

master thesis 2023

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Augment it Maybe?: Improving Deep Vision Models with Adversarial Scene Text Augmentation

Sharma, Anirvin (author)

Image data augmentation has been regarded as a reliable and effective way to increase the data available for training. With the advent and rise of Generative AI, generative data augmentation has been shown to realize even better gains in performance for downstream tasks. However, these performance gains are often the cause of "extra information"...

master thesis 2023

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Deep Learning for Geotechnical Engineering: The Effectiveness of Generative Adversarial Networks in Subsoil Schematization

Campos Montero, Fabian (author)

This thesis introduces a novel Generative Adversarial Network application called SchemaGAN, which has been adapted from the Pix2Pix architecture to take Cone Penetration Test (CPT) data as a conditional input and generate subsoil schematizations. For training, validation and testing, a database of 24,000 synthetic schematizations of size 32x512...

master thesis 2023

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A LSTM-based Generative Adversarial Network for End-use Water Modelling

Xie, yukun (author)

Research pertaining to end-use water analysis plays a pivotal role in enabling local communities to enhance their management of pipelines, water resources, and associated policies. Nowadays, various end-use models have been developed based on diverse databases and measurements. Nonetheless, a predominant drawback prevalent in most of these...

master thesis 2023

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3D Generative Adversarial Networks to Autonomously Generate Building Geometry

Müller, Lisa-Marie (author)

Across the world, countries are facing housing shortages and the Netherlands is no different. The increasing demand for new housing exceeds the growth rate of the architecture, engineering, and construction industry. Current solutions remain small in scale and therefore unsustainable. Multi-family housing is the optimal typology to address the...

master thesis 2023

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Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks

do Lago, Cesar A.F. (author), Giacomoni, Marcio H. (author), Bentivoglio, Roberto (author), Taormina, R. (author), Gomes, Marcus N. (author), Mendiondo, Eduardo M. (author)

Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for...

journal article 2023

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Generative Adversarial Networks for Shadow Removal to Improve Semantic Segmentation for Autonomous Driving

Bruggink, Daan (author)

Traversability estimation is a key component in autonomous driving tasks. In many applications, semantic segmentation is used to pixel-wise classify a visual scene. The pixel-wise segmented map is used to estimate the traversability of different environments. The semantic segmentation accuracy can drop if environmental conditions change. The...

master thesis 2023

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Physics-informed Data Augmentation for Human Radar Signatures

Focante, Edoardo (author)

In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to...

master thesis 2023

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Training Generative Adversarial Networks via Stochastic Nash Games

Franci, B. (author), Grammatico, S. (author)

Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a stochastic Nash equilibrium problem. Since the associated training process is challenging,...

journal article 2023

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Local Implicit Neural Representations for Multi-Sequence MRI Translation

Chen, Yunjie (author), Staring, M. (author), Wolterink, Jelmer M. (author), Tao, Q. (author)

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contraindications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical...

conference paper 2023

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A Pre-Silicon Power Leakage Assessment Based on Generative Adversarial Networks

Aljuffri, A.A.M. (author), Saxena, Mudit (author), Reinbrecht, Cezar (author), Hamdioui, S. (author), Taouil, M. (author)

Security is one of the most important features that a system must provide. Depending on the application of the target device, different threats should be considered at design time. However, the attack space is vast. Hence, it is difficult to decide what components to protect, what level of protection they require and how efficient they are in...

conference paper 2023

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Enriching Diversity of Synthetic Images for Person Detection

POLYA RAMESH, CHINMAY (author)

Camera-based patient monitoring is undergoing rapid adoption in the healthcare sector with the recent COVID-19 pandemic acting as a catalyst. It offers round-the-clock monitoring of patients in clinical units (e.g. ICUs, ORs), or at their homes through installed cameras, enabling timely, pre-emptive care. These are powered by Computer Vision...

master thesis 2022

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Permutation-Invariant Tabular Data Synthesis

Zhu, Yujin (author)

Tabular data synthesis is a promising approach to circumvent strict regulations on data privacy. Although the state-of-the-art tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, they are sensitive to column permutations of input data. In this work, we conduct an impact and...

master thesis 2022

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Extreme-value Neural Networks for Weather Forecasting

Wang, Zhiyi (author)

Deep-learning models are commonly used in short-term precipitation forecasting. However, most deep-learning models are likely to produce blurry output problems. In order to get realistic and accurate results, AENN, a variant of Generative Adversarial Networks (GANs), has been developed. The AENN implements an additional temporal discriminator to...

master thesis 2022

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How might human experience inform AI systems

Jin, Shuyue (author)

The problem we want to address<br/>AI(Artificial intelligence) is diving into people’s lives as its algorithm continues to iterate. However, the algorithmic and quantitative systems do not seem to access people’s experiences, which always include emotional and qualitative factors. How can an AI system understand people’s feelings? How can an AI...

master thesis 2022

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Self-supervised Audio-reactive Music Video Synthesis: Measuring and optimizing audiovisual correlation

Brouwer, Hans (author)

Synthesizing audio-reactive videos to accompany music is challenging multi-domain task that requires both a visual synthesis skill-set and an understanding of musical information extraction. In recent years a new flexible class of visual synthesis methods has gained popularity: generative adversarial networks. These deep neural networks can be...

master thesis 2022

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Building massing generation using GAN trained on Dutch 3D city models

Veselý, Ondrej (author)

Despite being relatively novel, generative adversarial networks (GAN) have already been appropriated for application to several problems within the field of architectural and urban generative design. However, the preceding GAN based models for building massing generation make use of only simplified and two dimensional representation of the built...

master thesis 2022

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Empirical study of GANITE’s robustness to hidden confounders

van Oudenhoven, Vincent (author)

An empirical study is performed exploring the sensitivity to hidden confounders of GANITE, a method for Individualized Treatment Effect (ITE) estimation. Most real world datasets do not measure all confounders and thus it is important to know how crucial this is in order to obtain comparable predictions. This is explored through the removal of...

bachelor thesis 2022