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Railway track circuit fault diagnosis using recurrent neural networks

de Bruin, T.D. (author), Verbert, K.A.J. (author), Babuska, R. (author)

Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from...

journal article 2017

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Biometric palmprint verification: A dynamical system approach

Palma, David (author), Montessoro, Pier Luca (author), Giordano, G. (author), Blanchini, Franco (author)

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is...

journal article 2017

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Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks

Reale, C. (author), Gavin, Kenneth (author), Librić, Lovorka (author), Jurić-Kaćunić, Danijela (author)

Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the...

journal article 2018

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Pricing Options and Computing Implied Volatilities using Neural Networks

Liu, S. (author), Oosterlee, C.W. (author), Bohte, Sander M. (author)

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a...

journal article 2019

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Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks

Xie, S. (author), Garofano, V. (author), Chu, Xiumin (author), Negenborn, R.R. (author)

Real-time collision avoidance with full consideration of ship maneuverability, collision risks and International Regulations for Preventing Collisions at Sea (COLREGs) is difficult in multi-ship encounters. To deal with this problem, a novel method is proposed based on model predictive control (MPC), an improved Q-learning beetle swarm...

journal article 2019

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Adaptive control for autonomous ships with uncertain model and unknown propeller dynamics

Haseltalab, A. (author), Negenborn, R.R. (author)

Motion control is one of the most critical aspects in the design of autonomous ships. During maneuvering, the dynamics of propellers as well as the craft hydrodynamical specifications experience severe uncertainties. In this paper, an adaptive control approach is proposed to control the motion and trajectory tracking of an autonomous vessel...

journal article 2019

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Fine-tuning deep RL with gradient-free optimization

de Bruin, T.D. (author), Kober, J. (author), Tuyls, Karl (author), Babuska, R. (author)

Deep reinforcement learning makes it possible to train control policies that map high-dimensional observations to actions. These methods typically use gradient-based optimization techniques to enable relatively efficient learning, but are notoriously sensitive to hyperparameter choices and do not have good convergence properties. Gradient...

journal article 2020

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A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids

Sapountzoglou, Nikolaos (author), Lago, Jesus (author), De Schutter, B.H.K. (author), Raison, Bertrand (author)

Power outages in electrical grids can have very negative economic and societal impacts rendering fault diagnosis paramount to their secure and reliable operation. In this paper, deep neural networks are proposed for fault detection and location in low-voltage smart distribution grids. Due to its key properties, the proposed method solves some...

journal article 2020

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Predictive and generative machine learning models for photonic crystals

Christensen, Thomas (author), Loh, Charlotte (author), Picek, S. (author), Jakobović, Domagoj (author), Jing, Li (author), Fisher, Sophie (author), Ceperic, Vladimir (author), Joannopoulos, John D. (author), Soljačić, Marin (author)

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods...

journal article 2020

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Table tennis tutor: Forehand strokes classification based on multimodal data and neural networks

Sanusi, Khaleel Asyraaf Mat (author), Di Mitri, Daniele (author), Limbu, B.H. (author), Klemke, Roland (author)

Beginner table-tennis players require constant real-time feedback while learning the funda-mental techniques. However, due to various constraints such as the mentor’s inability to be around all the time, expensive sensors and equipment for sports training, beginners are unable to get the immediate real-time feedback they need during training....

journal article 2021

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Research On ADCN Method For Damage Detection Of Mining Conveyor Belt

Qu, Dingran (author), Qiao, Tiezhu (author), Pang, Y. (author), Yang, Yi (author), Zhang, Haitao (author)

Belt conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of conveyor belt damage detection based on ADCN (Adaptive Deep...

journal article 2021

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Wavelet scattering and neural networks for railhead defect identification

Jin, J. (author)

Accurate and automatic railhead inspection is crucial for the operational safety of railway systems. Deep learning on visual images is effective in the automatic detection of railhead defects, but either intensive data requirements or ignoring defect sizes reduce its applicability. This paper developed a machine learning framework based on...

journal article 2021

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Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning

Castellini, Jacopo (author), Oliehoek, F.A. (author), Savani, Rahul (author), Whiteson, Shimon (author)

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which...

journal article 2021

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Feasibility of a neural network‐based virtual sensor for vehicle unsprung mass relative velocity estimation

Šabanovič, Eldar (author), Kojis, Paulius (author), Šukevičius, Šarūnas (author), Shyrokau, B. (author), Ivanov, Valentin (author), Dhaens, Miguel (author), Skrickij, Viktor (author)

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this...

journal article 2021

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The quadrature method: A novel dipole localisation algorithm for artificial lateral lines compared to state of the art

Bot, Daniël M. (author), Wolf, B.J. (author), van Netten, Sietse M. (author)

The lateral line organ of fish has inspired engineers to develop flow sensor arrays— dubbed artificial lateral lines (ALLs)—capable of detecting near-field hydrodynamic events for obstacle avoidance and object detection. In this paper, we present a comprehensive review and comparison of ten localisation algorithms for ALLs. Differences in the...

journal article 2021

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Charting the low-loss region in electron energy loss spectroscopy with machine learning

Roest, Laurien I. (author), van Heijst, S.E. (author), Maduro, L.A. (author), Rojo, Juan (author), Conesa Boj, S. (author)

Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model...

journal article 2021

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Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning

Stölzle, Maximilian (author), Miki, Takahiro (author), Gerdes, Levin (author), Azkarate, Martin (author), Hutter, Marco (author)

Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion planning or the missing...

journal article 2022

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Monte Carlo simulation of SDEs using GANs

van Rhijn, J. (author), Oosterlee, C.W. (author), Grzelak, L.A. (author), Liu, S. (author)

Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can also be used to approximate one-dimensional Ito ^ stochastic differential equations (SDEs). We propose a scheme that approximates the path-wise conditional...

journal article 2022

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Adaptive neural sliding mode control for heterogeneous ship formation keeping considering uncertain dynamics and disturbances

You, Xu (author), Yan, Xinping (author), Liu, Jialun (author), Li, Shijie (author), Negenborn, R.R. (author)

This paper investigates the formation keeping problem of heterogeneous ships with underactuated inputs, uncertain dynamics, and environmental disturbances. The control objective is to make the heterogeneous followers keep the desired formation while tracking a leader. To solve the problem effectively, a novel virtual leader–follower formation...

journal article 2022

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A PDE-free, neural network-based eddy viscosity model coupled with RANS equations

Xu, R. (author), Zhou, Xu Hui (author), Han, Jiequn (author), Dwight, R.P. (author), Xiao, Heng (author)

In fluid dynamics, constitutive models are often used to describe the unresolved turbulence and to close the Reynolds averaged Navier–Stokes (RANS) equations. Traditional PDE-based constitutive models are usually too rigid to calibrate with a large set of high-fidelity data. Moreover, commonly used turbulence models are based on the weak...

journal article 2022