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Adaptive Prescribed Performance Asymptotic Tracking for High-Order Odd-Rational-Power Nonlinear Systems

Lv, Maolong (author), De Schutter, B.H.K. (author), Cao, Jinde (author), Baldi, S. (author)

Practical tracking results have been reported in the literature for high-order odd-rational-power nonlinear dynamics (a chain of integrators whose power is the ratio of odd integers). Asymptotic tracking remains an open problem for such dynamics. This note gives a positive answer to this problem in the framework of prescribed performance...

journal article 2023

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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

document

Training Generative Adversarial Networks via Stochastic Nash Games

Franci, B. (author), Grammatico, S. (author)

Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a stochastic Nash equilibrium problem. Since the associated training process is challenging,...

journal article 2023

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DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Li, Wanda (author), Xu, Zhiwei (author), Sun, Yi (author), Gong, Qingyuan (author), Chen, Y. (author), Ding, Aaron Yi (author), Wang, Xin (author), Hui, Pan (author)

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...

journal article 2023

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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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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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Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

Murti, Fahri Wisnu (author), Ali, Samad (author), Iosifidis, G. (author), Latva-aho, Matti (author)

Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central...

journal article 2024

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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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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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Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction

Liu, Chengen (author), Leus, G.J.T. (author), Isufi, E. (author)

The edge flow reconstruction task consists of retreiving edge flow signals from corrupted or incomplete measurements. This is typically solved by a regularized optimization problem on higher-order networks such as simplicial complexes and the corresponding regularizers are chosen based on prior knowledge. Tailoring this prior to the setting...

journal article 2023

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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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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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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

document

Symbolic knowledge extraction for explainable nutritional recommenders

Magnini, Matteo (author), Ciatto, Giovanni (author), Cantürk, Furkan (author), Aydoğan, Reyhan (author), Omicini, Andrea (author)

Background and objective:This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there...

journal article 2023

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Backdoor Pony: Evaluating backdoor attacks and defenses in different domains

Mercier, Arthur (author), Smolin, Nikita (author), Sihlovec, Oliver (author), Koffas, S. (author), Picek, S. (author)

Outsourced training and crowdsourced datasets lead to a new threat for deep learning models: the backdoor attack. In this attack, the adversary inserts a secret functionality in a model, activated through malicious inputs. Backdoor attacks represent an active research area due to diverse settings where they represent a real threat. Still,...

journal article 2023

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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