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Micromechanics-based deep-learning for composites: Challenges and future perspectives

Mirkhalaf, Mohsen (author), Rocha, I.B.C.M. (author)

During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites. One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using...

journal article 2024

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Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification

Mateo-Barcos, S. (author), Ribo-Perez, D.G. (author), Rodríguez-García, J. (author), Alcázar-Ortega, M. (author)

This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and...

journal article 2024

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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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Promises of Deep Kernel Learning for Control Synthesis

Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)

Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...

journal article 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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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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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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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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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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Transient Stable Corrective Control Using Neural Lyapunov Learning

Bellizio, Federica (author), Cremer, Jochen (author), Strbac, Goran (author)

This paper proposes a method to compute corrective control actions for dynamic security in real-time and quantifies the economic value of corrective control. Lowered inertia requires fast control methods in real-time to correct system operation and maintain system security when equipment fails. However, using corrective control beyond such...

journal article 2022

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An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Cuperman, Rafael (author), Jansen, K.M.B. (author), Ciszewski, M.G. (author)

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...

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

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Wind turbine gearbox fault prognosis using high-frequency monitoring SCADA data

Verma, Ayush (author)

With growing wind energy capacity, especially offshore, reliability of wind turbines (WT) becomes a relevant concern. Poor reliability directly affects their cost effectiveness due to increased operation and maintenance ...

master thesis 2021

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Comparing the performance of machine learning methods in a surrogate approach to optimize low-thrust interplanetary trajectories

Brederveld, Jan (author)

Various machine learning algorithms have been applied to find optimal lowthrust<br/>satellite trajectories, however, no fair comparison of their accuracy has been made yet. In this paper, two common and promising supervised machine learning algorithms are compared for their regression capacities:<br/>the artificial neural network and the...

master thesis 2021

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Estimation of lignocellulosic biomass pyrolysis product yields using artificial neural networks

Tsekos, C. (author), Tandurella, S. (author), de Jong, W. (author)

As the push towards more sustainable ways to produce energy and chemicals intensifies, efforts are needed to refine and optimize the systems that can give an answer to these needs. In the present work, the use of neural networks as modelling tools for lignocellulosic biomass pyrolysis main products yields estimation was evaluated. In order to...

journal article 2021

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MPC Based Centralized Voltage and Reactive Power Control for Active Distribution Networks

Nguyen, Hoa Minh (author), Rueda, José L. (author), Lekić, A. (author), Pham, Hoan Van (author)

The paper presents an approach for online centralized control in active distribution networks. It combines a proportional integral (PI) control unit with a corrective control unit (CCU), based on the principle of Model Predictive Control (MPC). The proposed controller is designed to accommodate the increasing penetration of distributed...

journal article 2021

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Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Antonopoulos, Ioannis (author), Robu, Valentin (author), Couraud, Benoit (author), Flynn, David (author)

Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the...

journal article 2021

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Deterministic and Statistical Strategies to Protect ANNs against Fault Injection Attacks

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

Artificial neural networks are currently used for many tasks, including safety critical ones such as automated driving. Hence, it is very important to protect them against faults and fault attacks. In this work, we propose two fault injection attack detection mechanisms: one based on using output labels for a reference input, and the other on...

conference paper 2021

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Environmental impacts cost assessment model of residential building using an artificial neural network

Hamida, A.B. (author), Alsudairi, Abdulsalam (author), Alshaibani, Khalid (author), Alshamrani, Othman (author)

Purpose: Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore,...

journal article 2020

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Production of ethanol fuel via syngas fermentation: Optimization of economic performance and energy efficiency

Magalhaes de Medeiros, E. (author), Noorman, H.J. (author), Maciel Filho, Rubens (author), Posada Duque, J.A. (author)

In this work, a model was developed to predict the performance of a bubble column reactor for syngas fermentation and the subsequent recovery of anhydrous ethanol. The model was embedded in an optimization framework which employs surrogate models (artificial neural networks) and multi-objective genetic algorithm to optimize different process...

journal article 2020