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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

document

Hybrid human-centric haptic shared control using artificial neural network and model predictive control

Harmankaya, Hüseyin (author)

Commercially available Lane Keeping Assist systems fail to consider the driver's intentions since they mainly focus on minimising path tracking errors, resulting in conflicts between humans and automation. This often leads to users being unsatisfactory and turning off the assist, as a result diminishing the advantages such as reduced workload...

master thesis 2023

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Microphones as Airspeed Sensors for Micro Air Vehicles

Makaveev, Momchil (author)

This project proposes and evaluates a novel concept for an airspeed instrument aimed at small hybrid unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer formed over the vehicle’s body to its airspeed. The instrument consists of two microphones,...

master thesis 2023

document

Enhancing early-stage energy consumption predictions using dynamic operational voyage data: A grey-box modelling investigation

Odendaal, Kirsten (author), Alkemade, Aaron (author), Kana, A.A. (author)

The adverse human contribution to global climate change has forced the yachting industry to acknowledge the need to reduce its environmental impact due to the client's increasing pressure and potential future regulations to limit the ecological effects. Unfortunately, current real-world data presents a significant disparity between predicted...

journal article 2023

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Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate

Alves Maia, M. (author), Rocha, I.B.C.M. (author), Kerfriden, P. (author), van der Meer, F.P. (author)

Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application is especially advantageous in concurrent multiscale finite element analysis (FE²) due to the exceedingly high computational costs often associated with it...

journal article 2023

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Using artificial neural networks to accelerate flowsheet optimization for downstream process development

Keulen, D. (author), van der Hagen, Erik (author), Geldhof, Geoffroy (author), Le Bussy, Olivier (author), Pabst, Martin (author), Ottens, M. (author)

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or...

journal article 2023

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Efficient Embedded Element Pattern Prediction via Machine Learning: A Case Study with Planar Non-Uniform Sub-Arrays

Onat, N.B. (author), Roldan Montero, I. (author), Fioranelli, F. (author), Yarovoy, Alexander (author), Aslan, Y. (author)

Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in...

conference paper 2023

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Rapid extraction of pavement aggregate gradation based on point clouds using deep learning networks

Chen, Siyu (author), Chen, Can (author), Ma, Tao (author), Han, Chengjia (author), Luo, Haoyuan (author), Wang, Siqi (author), Gao, Y. (author), Yang, Yaowen (author)

Usage of asphalt mixture with poor gradation will most likely lead to pavement deficiency. There is a growing need for rapid and non-destructive methods to extract pavement aggregate gradation. In this study, a deep learning-based method that utilizes point clouds data for gradation extraction was proposed. Firstly, a data enhancement...

journal article 2023

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How does “listening” help fracture understanding?

de Araujo Alves Lima, Rosemere (author), Tao, R. (author), Bernasconi, A. (author), Carboni, Michele (author), Carrere, Nicolas (author), Teixeira De Freitas, S. (author)

Understanding the relationship between the sensors’ outputs and the damage evolution within the joints is becoming increasingly crucial to improving structural health monitoring systems and collecting data to improve the joint’s design. Therefore, a study of the acoustic emission method associated with visual fracture evaluation was proposed to...

poster 2023

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Quantitative Damage Monitoring of Filament Wound Composites by Using Machine Learning-Based Techniques

Bani Mohammad Ali, Amir (author), Valizadeh Sotubadi, Saleh (author), Alimirzaei, Sajad (author), Ahmadi Najafabadi, Mehdi (author), Pahlavan, Lotfollah (author)

Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring...

journal article 2023

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Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Kamel Targhi, Elahe (author), Emami Niri, Mohammad (author), Zitha, P.L.J. (author)

Cross-linked polymer gel is widely used in the oil and gas industry to block high permeability conduits and reduce water cut. The complex nature of this fluid, especially regarding flow in porous media, makes its numerical simulation very time-consuming. This study presents an approach to designing an Artificial Neural Network (ANN) model...

journal article 2023

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Black-Box Online Aerodynamic Performance Optimization for a Seamless Wing with Distributed Morphing

Ruland, Oscar (author), Mkhoyan, T. (author), De Breuker, R. (author), Wang, Xuerui (author)

journal article 2023

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A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Yalcin Kavus, Bahar (author), Gülüm Taş, P. (author), Taskin, Alev (author)

Non-ergonomic working conditions are the leading causes of musculoskeletal disorders that seriously affect human health. REBA is widely used tool due to its convenience and consideration of all body parts. However, it heavily relies on the subjective judgments of the assessor, leading to inconsistencies in results, and lacks sensitivity in...

journal article 2023

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Crashworthiness Analysis of An Aircraft Subfloor Structure using Artificial Neural Networks

Waaijer, Joost (author)

The goal of this thesis is to build an artificial neural network(ANN) surrogate model, that predicts the crashworthiness performance of a structure. The structure used in this research is a thermoplastic fibre-reinforced composite aircraft subfloor section which is part of the SmarT multifUNctioNal and INteGrated thermoplastic fuselage (STUNNING...

master thesis 2022

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Trajectory Pruning with Neural Networks for Efficient Monte-Carlo based Quadrotor Flight Envelope Prediction

Kaneko, Kei (author)

Flight envelope prediction is a challenging task where one of the difficulties is that widely used methods, like the level set methods, are impractical for systems with more than four coupled state dimensions due to the “curse of dimensionality”. Monte-Carlo simulation based approach suffers less from this, however a large number of simulations...

master thesis 2022

document

An Artificial Neural Network Approach to Within-Game Predictions in Football using Spatiotemporal Data

Römer, Stijn (author)

Recent developments in \ac{ML} have paved the way for unprecedented possibilities in the field of data analytics in numerous team sports, such as American football, baseball, and basketball. In more recent years, \ac{ML} techniques have been applied to football as professional teams got inspired to collect enormous quantities of data to evaluate...

master thesis 2022

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Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system

Sun, B. (author), Wang, Xuerui (author), van Kampen, E. (author)

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic...

journal article 2022

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Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model

Janssens, M. (author), Hulshoff, S.J. (author)

Data-driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational-Multiscale (VMS) Artificial Neural...

journal article 2022

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Investigation of the efficacy of the UV/Chlorine process for the removal of trimethoprim: Effects of operational parameters and artificial neural networks modelling

Teo, Ying Shen (author), Jafari, Iman (author), Liang, Fei (author), Jung, Youmi (author), van der Hoek, J.P. (author), Ong, Say Leong (author), Hu, Jiangyong (author)

The UV/Cl2 process (also known as chlorine photolysis, which is the combination of chlorine and simultaneous irradiation of UV light) is conventionally applied at acidic mediums for drinking water treatment and further treatment of wastewater effluents for secondary reuse. This is because the quantum yield of HO• from HOCl (ϕHO•, 254 = 1.4) is...

journal article 2022

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Near real-time detection of blockages in the proximity of combined sewer overflows using evolutionary ANNs and statistical process control

Rosin, T. R. (author), Kapelan, Z. (author), Keedwell, E. (author), Romano, M. (author)

Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant cleanup costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology,...

journal article 2022