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

document

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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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

document

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

document

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

document

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

document

Adaptive time-delay estimation and control of optimized Stewart robot

Tajdari, F. (author)

Aiming at a more efficient and accurate performance of parallel manipulators in the existence of complex kinematics and dynamics, a robust generalizable methodology is proposed here for an integrated 6-DOF Stewart platform with rotary time-delayed actuators torque control. The suggested method employs a time-delay linear–quadratic integral...

journal article 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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Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs

Overwater, R.W.J. (author), Babaie, M. (author), Sebastiano, F. (author)

Quantum error correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their...

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

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Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Garzón Díaz, J.A. (author), Kapelan, Z. (author), Langeveld, J.G. (author), Taormina, R. (author)

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML),...

review 2022

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Smart Redundancy Schemes for ANNs against Fault Attacks

Köylü, T.C. (author), Hamdioui, S. (author), Taouil, M. (author)

Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks....

conference paper 2022

document

Learning to run a power network with trust

Marot, Antoine (author), Donnot, Benjamin (author), Chaouache, Karim (author), Kelly, Adrian (author), Huang, Qiuhua (author), Hossain, Ramij Raja (author), Cremer, Jochen (author)

Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon and operators will be in charge of taking action for the foreseeable future. Aiming at designing...

journal article 2022

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Deep Neural Network-Based Digital Pre-distortion for High Baudrate Optical Coherent Transmission

Bajaj, V. (author), Buchali, Fred (author), Chagnon, Mathieu (author), Wahls, S. (author), Aref, Vahid (author)

High-symbol-rate coherent optical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. Recently, we proposed in [20] a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128...

journal article 2022

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Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network

Tao, Qinghua (author), Li, Zhen (author), Xu, Jun (author), Lin, Shu (author), De Schutter, B.H.K. (author), Suykens, Johan A.K. (author)

Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities and is affected by many elements. Recently, neural networks have attracted much attention for TF prediction, but they are commonly black boxes with complex architectures and difficult to be interpreted,...

journal article 2022