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Self-Supervised Neuromorphic Perception for Autonomous Flying Robots

Paredes-Vallés, Federico (author)

In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...

doctoral thesis 2023

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Unsupervised Wafer Map Failure Pattern Recognition with Contrastive Learning

Liu, Kevin (author)

This master’s thesis explores the application of Self-Supervised Contrastive Learning (SSCL), specifically the SimCLR algorithm, to enhance feature representation learning from Wafer Bin Maps (WBM) in the semiconductor manufacturing process. The motivation stems from the industry’s growing need for automated defect detection and root-cause...

master thesis 2023

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Unsupervised Deep Learning-based Side-channel Analysis

Savu, Ioana (author)

Side-channel attacks (SCA) play a crucial role in assessing the security of the implementation of cryp- tographic algorithms. Still, traditional profiled attacks require a nearly identical reference device to the target, limiting their practicality. This thesis focuses on non-profiled SCA, which provides a re- alistic alternative when the...

master thesis 2023

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A Methodology for Damage Detection Using Unsupervised Learning in the Field of Structural Health Monitoring: Based on Gaussian Mixture Modeling

Vilhjálmsson, Thor (author)

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...

master thesis 2023

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Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge

de Boer, Wouter (author)

Autonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system...

master thesis 2023

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Public Transport Delay Pattern Analysis By Unsupervised Learning Approach

Cheng, Yuxing (author)

To analyze latent multiple specific patterns in the line-based public transport daily delay occurrence, a data-driven explorative analysis of public transport daily delay spatial-temporal distribution pattern is performed based on the k-means clustering algorithm. Firstly, we used aggregated daily delay profile to visualize how the delay is...

student report 2023

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G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network

Gold, Andrew (author)

Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing...

master thesis 2023

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What Attention Regulation Behaviors Tell Us About Learners in E-Reading?: Adaptive Data-Driven Persona Development and Application Based on Unsupervised Learning

Lee, Y. (author), Migut, M.A. (author), Specht, M.M. (author)

Different individual features of the learner data often work as essential indicators of learning and intervention needs. This work exploits the personas in the design thinking process as the theoretical basis to analyze and cluster learners’ learning behavior patterns as groups. To adapt to the learning practice, we develop data-driven personas...

journal article 2023

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Constructing Health Indicators for Systems with Few Failure Instances Using Unsupervised Learning

de Pater, I.I. (author), Mitici, M.A. (author)

Health indicators are crucial to assess the health of complex systems. In recent years, several studies have developed data-driven health indicators using supervised learning methods. However, due to preventive maintenance, there are often not enough failure instances to train a supervised learning model, i.e., the data is unlabelled with an...

conference paper 2023

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Automated identification of linear viscoelastic constitutive laws with EUCLID

Marino, Enzo (author), Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection by adopting a generalized Maxwell model expressed by a Prony series, and deploy EUCLID for identification. The methodology is based on four ingredients: i. full...

journal article 2023

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Machine learning approaches exploring the optimal number of driver profiles based on naturalistic driving data

Tselentis, D. (author), Papadimitriou, E. (author)

Driver behavior analytics is an important concept that plays a significant role in the understanding of road crashes. This paper investigates the optimal number of driver profiles to understand the most important characteristics that differentiate drivers and extract useful insights on the value of using different clustering approaches in...

journal article 2023

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Time-series clustering for pattern recognition of speed and heart rate while driving: A magnifying lens on the seconds around harsh events

Tselentis, D. (author), Papadimitriou, E. (author)

Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective of this research is to reveal existing driving patterns around harsh...

journal article 2023

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Automated discovery of generalized standard material models with EUCLID

Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We extend the scope of our recently developed approach for unsupervised automated discovery of material laws (denoted as EUCLID) to the general case of a material belonging to an unknown class of constitutive behavior. To this end, we leverage the theory of generalized standard materials, which encompasses a plethora of important constitutive...

journal article 2023

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Algebraic multiscale grid coarsening using unsupervised machine learning for subsurface flow simulation

Ramesh Kumar, K. (author), Tene, Matei (author)

Subsurface flow simulation is vital for many geoscience applications, including geoenergy extraction and gas (energy) storage. Reservoirs are often highly heterogeneous and naturally fractured. Therefore, scalable simulation strategies are crucial to enable efficient and reliable operational strategies. One of these scalable methods, which...

journal article 2023

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Detecting Dish Types in Picnic Deliveries

Noorthoek, Sterre (author)

In addition to delivering groceries at customers’ doorsteps, online supermarket Picnic goes the extra mile by aiming to improve customer satisfaction. For instance, by providing cooking inspiration to customers through a recently launched recipe page in the app. This feature presents new recipes weekly and allows customers to easily add the...

master thesis 2022

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NN-EUCLID: Deep-learning hyperelasticity without stress data

Thakolkaran, P. (author), Joshi, A. (author), Zheng, Y. (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress–strain pairs, the approach only uses realistically measurable full-field displacement and global reaction force data, thus it lies...

journal article 2022

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Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties

Joshi, A. (author), Thakolkaran, P. (author), Zheng, Y. (author), Escande, Maxime (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but...

journal article 2022

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A critical review of discontinuity plane extraction from 3D point cloud data of rock mass surfaces

Daghigh, Hamid (author), Tannant, Dwayne D. (author), Daghigh, Vahid (author), Lichti, Derek D. (author), Lindenbergh, R.C. (author)

Field investigations of geometric discontinuity properties in rock masses are increasingly using three-dimensional point cloud data. These point clouds sample the rock mass surface and are typically acquired by photogrammetry or LiDAR. The automatic segmentation and extraction of planar surfaces from point cloud data have attracted...

review 2022

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Feasibility study of "Invariant Information Clustering for Unsupervised Image Segmentation"

Dimitrov, Yordan (author)

In this paper we analyze the performance of a novel clustering objective that optimizes a neural network to predict segmentation. We challenge the reported results by replicating the original experiments and conducting additional tests to gain an insight into the algorithm. We analyzed the efficiency of the clustering objective on a different...

master thesis 2021

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Privacy-preserving Clustering of Single-cell RNA Sequencing Data on Intel SGX

Cao, Qingyuan (author)

This project is dedicated to implementing an unsupervised learning clustering method system for processing big data applied in Intel SGX. Intel SGX is a technology developed to meet the needs of the trusted computing industry similarly to ARM TrustZone, but this time for desktop and server platforms. It is a set of safety-related instruction...

master thesis 2021