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Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning

Dong, Y. (author), Lu, Xingmin (author), Li, Ruohan (author), Song, Wei (author), van Arem, B. (author), Farah, H. (author)

The burgeoning navigation services using digital maps provide great convenience to drivers. However, there are sometimes anomalies in the lane rendering map images, which might mislead human drivers and result in unsafe driving. To accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into...

poster 2024

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Enhancing Historical Dutch OCR Accuracy with Post-Correction & Synthetic Data

Eckhardt, Thomas (author)

This paper presents a novel approach to synthetic data generation for OCR post-correction, utilizing specific background and font variations tailored to specific timeperiods. The goal is to use synthetic data to enhance text accuracy in digitized historical documents. The proposed three-step process involves generating synthetic images that...

master thesis 2023

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Modelling X-Ray photon transport through a transformer-based neural network in computed tomography forward projection

Hoendermis, Dora (author)

Radiotherapy is one of the main treatments for cancer and relies heavily on CT images to calculate radiation dose. With research on radiotherapy moving to adaptive treatments aiming to calculate these doses at real-time speeds while maintaining high precision, a need for accurate CT imaging at comparable real-time speeds has emerged. Currently,...

master thesis 2023

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Assessing the applicability of Transformer-based architectures as rainfall-runoff models

Mao, Kangmin (author)

Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...

master thesis 2023

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Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification

Liu, Sijie (author), Xin, Jingmin (author), Wu, Jiayi (author), Deng, Yangyang (author), Su, Ruisheng (author), Niessen, W.J. (author), Zheng, Nanning (author), van Walsum, T. (author)

Identification and detection of thin-cap fibroatheroma (TCFA) from intravascular optical coherence tomography (IVOCT) images is critical for treatment of coronary heart diseases. Recently, deep learning methods have shown promising successes in TCFA identification. However, most methods usually do not effectively utilize multi-view...

journal article 2023

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High-frequency transformer winding model with adequate protection

Nasirpour, F. (author), Heidary, Amir (author), Ghaffarian Niasar, M. (author), Lekić, A. (author), Popov, M. (author)

High local electric field intensity in transformer windings originating from transient signals is one of the reasons for transformer failures. Due to the integration of renewable energy sources into the power grids and the increased number of transients, the likelihood of transformer catastrophic failure increases accordingly. Therefore, to...

journal article 2023

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Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG

Peh, Wei Yan (author), Thangavel, Prasanth (author), Yao, Yuanyuan (author), Thomas, John (author), Tan, Yee Leng (author), Dauwels, J.H.G. (author)

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent...

journal article 2023

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Attention-based deep learning for DNA repair outcome prediction: Learning how the cell repairs DNA breaks using local sequence context

de Boer, Jurrian (author)

Recent advancements in quantification of repair outcomes of CRISPR-Cas9 mediated double-stranded DNA breaks (DSBs) have allowed for the use of machine learning for predicting the frequencies of these repair outcomes. Local DNA sequence context influences the frequencies of mutations that arise when DNA gets repaired after it is targeted by...

master thesis 2022

document

Analyzing Components of a Transformer under Different Data Scales in 3D Prostate CT Segmentation

Tan, Yicong (author)

Literature on medical imaging segmentation claims that self-attention-based Transformer blocks perform better than convolution in UNet-based architectures. This recently touted success of Transformers warrants an investigation into which of its components contribute to its performance. Moreover, previous work has a limitation of analysis only at...

master thesis 2022

document

Development of Resonance-Based Test System to Measure Lifetime Curves of Dielectric Materials

Patil, Ashutosh (author)

With the increasing growth and penetration of power electronic devices in the power system, it is essential to understand the behaviour of insulation materials and systems under the varied signals that are produced with power electronic systems. These signals could be square pulses, sinusoidal or even superimposed. The high-frequency sinusoidal...

master thesis 2022

document

Utilizing Lingual Structures to Enhance Transformer Performance in Source Code Completions

Katzy, Jonathan (author)

We explored the effect of augmenting a standard language model’s architecture (BERT) with a structural component based on the Abstract Syntax Trees (ASTs) of the source code. We created a universal abstract syntax tree structure that can be applied to multiple languages to enable the model to work in a multilingual setting. We adapted the...

master thesis 2022

document

Generalization by Visual Attention

Collé, Baptiste (author)

Most deep learning models fail to generalize in production. Indeed, sometimes data used during training does not completely reflect the deployed environment. The test data is then considered out-of-distribution compared to the training data. In this paper, we focus on out-of-distribution performance for image classification. In fact,...

bachelor thesis 2022

document

Using Transformers to Generate Wellbeing Questions

Trasberg, Markus (author)

With the surge of mental health issues during COVID-19, more emphasis has turned towards assessing wellbeing. At the same time, recent advances in AI have shown huge potential in a variety of fields. However, few solutions are available at the intersection of those two fields. This research explores how the use of transformer models like GPT-3...

bachelor thesis 2022

document

Long-term traffic flow predictions in a transformer-based framework: Capturing temporal and external features, to obtain a traffic flow prediction for the next 24 hours

Petsch, Carmen (author)

Traffic flow predictions are an important component in the rising demand for solutions to cope with the increasing pressure on transportation networks. Especially on a long prediction horizon, traffic flow predictions remain challenging due to the complex, nonlinear nature of traffic flow and the influence of both temporal and external features....

master thesis 2022

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VideoMuscle: Estimating human musculoskeletal model using neural networks from monocular video

Yang, Wei-Tse (author)

We present the first deep learning approach to estimate the human skeletal system of the musculoskeletal model from monocular video. The current practice of musculoskeletal modeling relies on a motion capture system and OpenSim. The data is recorded in a restricted environment, and OpenSim workflow for musculoskeletal modeling is costly. Our...

master thesis 2021

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One Pose Fits All: A novel kinematic approach to 3D human pose estimation

Wu, Yen-Lin (author)

3D human pose estimation is a widely researched computer vision task that could be applied in scenarios such as virtual reality and human-robot interaction. With the lack of depth information, 3D estimation from monocular images is an inherently ambiguous problem. On top of that, unrealistic human poses have been overlooked in the majority of...

master thesis 2021

document

Proton Therapy Dose Calculations Using A Transformer Deep Learning Algorithm

Wielinga, Kevin (author)

Proton therapy is a great way to treat cancer, since protons can concentrate energy on one single spot, which minimises the irradiated healthy tissue. Because of this property protons are sensitive to uncertainties. Since the tumour is not stationary throughout the treatment, multiple scans are essential. With the current dose calculation...

bachelor thesis 2021

document

A Compact Transformer-Based Fractional-N ADPLL in 10-nm FinFET CMOS

Li, Chao Chieh (author), Yuan, Min Shueh (author), Liao, Chia Chun (author), Chang, Chih Hsien (author), Lin, Yu Tso (author), Tsai, Tsung Hsien (author), Huang, Tien Chien (author), Liao, Hsien Yuan (author), Staszewski, R.B. (author)

In this article, we introduce a fractional-N all-digital phase-locked loop (ADPLL) architecture based on a single LC-tank, featuring an ultra-wide tuning range (TR) and optimized for ultra-low area in 10-nm FinFET CMOS. Underpinned by excellent switches in the FinFET technology, a high turn-on/off capacitance ratio of LC-tank switched...

journal article 2021

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Reinforcement Learning for the Knapsack Problem

Pierotti, J. (author), Kronmueller, Maximilian (author), Alonso-Mora, J. (author), van Essen, J.T. (author), Böhmer, J.W. (author)

Combinatorial optimization (CO) problems are at the heart of both practical and theoretical research. Due to their complexity, many problems cannot be solved via exact methods in reasonable time; hence, we resort to heuristic solution methods. In recent years, machine learning (ML) has brought immense benefits in many research areas,...

book chapter 2021

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Modelling of Relaxation Phenomena in Transformer Oil-Paper-Pressboard Insulation to determine the Dielectric Response Behaviour under DC and Impulse voltages

Poduval, Devika (author)

The factory acceptance tests are extremely important for the deployment and service life of Extra-high voltage transformers (EHV) and reactors. An essential part of such tests is Lightning Impulse (LI) tests which are intended to ensure that the transformer insulation withstands the transient lightning overvoltages which may occur while in...

master thesis 2020