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SSRN 2024年

作者： Alcañiz, Alba Agarwal, Shagun Tiwald, Philipp Isabella, Olindo Ziar, Hesan Colomés, Oriol Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Netherlands Faculty of Civil Engineering and Geosciences TU Delft Netherlands Photovoltaic Materials and Devices Group

As climate change becomes more critical and renewable energy sources expand, deploying photovoltaic (PV) systems on land faces limitations due to competition with agricultural and housing sectors. The sea offers a promising location for the installation of offshore floating PV (OFPV) systems. For the truly offshore deployment of these systems, an in-depth understanding of the fluid-structure interaction is required. In this work, we aim to contribute to this aspect by exploring how different parameters affect the structural load endured by the floating platform and the related electrical power losses. We employ a multi-physics framework that integrates the mechanical model of a very large floating structure with the optoelectrical modeling of PV modules located on top of the structure. This framework is used to analyze a hypothetical OFPV platform design, which includes a range of floater configurations, from a single large floater to multiple small floaters connected with free hinges. The results reveal a trade-off in selecting the number of floaters. The power mismatch loss tends to be lower for platforms with fewer, longer floaters. However, the structural loads are influenced by the interplay between elastic and rigid body responses, with high stresses observed in longer floaters due to the dominance of the elastic response. We further explore the influence of structural properties on the power mismatch losses. It is observed that the Young's modulus of the material only has an impact for longer floaters where the elastic-response dominates. Conversely, changes in the cross-section fill ratio affect shorter floaters, where the rigid-body response prevails. The floater-beam thickness has the most significant impact across various floater lengths. © 2024, The Authors. All rights reserved.

关键词： Finite element method

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RL-Based Control of UAS Subject to Significant Disturbance

RL-Based Control of UAS Subject to Significant Disturbance

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kousheek Chakraborty Thijs Hof Ayham Alharbat Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

关键词： Training Attitude control Simulation Force Reinforcement learning Autonomous aerial vehicles Stability analysis Maintenance Dynamical systems Logistics

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Integration of aggregated data in causally interpretable meta-analysis by inverse weighting

arXiv

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arXiv 2025年

作者： Vo, Tat Thang Le, Tran Trong Khoi Afach, Sivem Vansteelandt, Stijn Research Group EPIDERME Faculty of Medicine University Paris Est Creteil France Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we aim to develop novel strategies to integrate aggregated-level data from eligible trials with non-accessible IPD into a causal meta-analysis, by extending moment-based methods frequently used for population-adjusted indirect comparison in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis. © 2025, CC BY.

关键词： Inverse problems

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A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

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24th International Conference on Engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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Segment Anything in Medical Images with nnUNet 1

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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IoT Based Health Monitoring System with AI-Powered Disease Prediction 9

IoT Based Health Monitoring System with AI-Powered Disease P...

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9th International Conference on science, Technology, Engineering and mathematics, ICONSTEM 2024

作者： Radha Mahendran, S. Khan, Mohammad Shahbaz Hari Priya, M.P. Obaid, Ahmed J. Mohsen, Karrar Shareef Shareef, Ashraf Mohammed Department of Bioinformatics Chennai Pallavaram117 India Operations And Regulatory Affairs Children's National Hospital WashingtonDC United States Department of Computer Science Kothanur K.Narayanapura Karnataka Bangalore560077 India University of Kufa Technical Engineering College Al-Ayen University Faculty of Computer Science And Mathematics Najaf Iraq Department of Computer Technical Engineering Thi-Qar Iraq Scientific Research Center Al-Ayen University Information And Communication Technology Research Group Thi-Qar Iraq National University of Science And Technology DhiQar Iraq

ISBN: (纸本)9798350365092

Healthcare monitoring systems have advanced significantly in emergency rooms and many other health settings. Today, many nations are deeply concerned about the rise of small healthcare monitoring systems. In-person advising and telemedicine are two different ways that the Internet of Things (IoT) is further developing healthcare. The Internet of Things makes things and individuals reliably available, and its relationship with the cloud improves our personal satisfaction. Proactive clinical exploration can help change a responsive healthcare approach into a proactive one, as man-made reasoning (AI) and high-level human insight become increasingly more coordinated into the healthcare area. Within the field of machine learning, deep learning holds significant potential in precisely and swiftly analysing vast quantities of data, producing perceptive experiences, and resolving intricate issues. Preventive treatment and early intervention for persons who are at risk depend on accurate and useful illness prediction. The constant inflow of electronic medical information necessitates the need for more accurate prediction models in recurrent neural network versions of deep learning that handle successive time-series data. Devices connected to the Internet of Things provide data to the proposed system, and future analysis will be conducted on cloud-stored electronic clinical records pertaining to patient histories. © 2024 IEEE.

关键词： Forecasting

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Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation

arXiv

引用

arXiv 2023年

作者： He, Yanbin Joseph, Geethu Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty Delft University of Technology Netherlands

We study the sparse recovery problem with an underdetermined linear system characterized by a Kronecker-structured dictionary and a Kronecker-supported sparse vector. We cast this problem into the sparse Bayesian learning (SBL) framework and rely on the expectation-maximization method for a solution. To this end, we model the Kronecker-structured support with a hierarchical Gaussian prior distribution parameterized by a Kronecker-structured hyperparameter, leading to a non-convex optimization problem. The optimization problem is solved using the alternating minimization (AM) method and a singular value decomposition (SVD)-based method, resulting in two algorithms. Further, we analytically guarantee that the AM-based method converges to the stationary point of the SBL cost function. The SVD-based method, though it adopts approximations, is empirically shown to be more efficient and accurate. We then apply our algorithm to estimate the uplink wireless channel in an intelligent reflecting surface-aided MIMO system and extend the AM-based algorithm to address block sparsity in the channel. We also study the SBL cost to show that the minima of the cost function are achieved at sparse solutions and that incorporating the Kronecker structure reduces the number of local minima of the SBL cost function. Our numerical results demonstrate the effectiveness of our algorithms compared to the state-of-the-art. Copyright © 2023, The Authors. All rights reserved.

关键词： Singular value decomposition

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Bishoy Gerges Barbara Bazzana Nicolò Botteghi Youssef Aboudorra Antonio Franchi Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Department of Applied Mathematics University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.

关键词： Location awareness Visualization Pipelines Noise reduction Autonomous aerial vehicles Visual servoing Trajectory Planning Standards Robots

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Towards an Action Recognition Framework for Endovascular Surgery

Towards an Action Recognition Framework for Endovascular Sur...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jochem Bos Dennis Kundrat Giulio Dagnino Robotics and Mechatronics (RAM) Group Faculty of Electrical Engineering Mathematics and Computer Science (EEMCS) University of Twente The Netherlands

Objective knowledge about instrument manoeuvres in endovascular surgery is essential for evaluating surgical skills and developing advanced technologies for cathlab routines. To the recent day, endovascular navigation has been exclusively assessed in laboratory scenarios. By contrast, information contained in available fluoroscopy data from clinical cases has been disregarded. In this work, we pioneer a learning-based framework for motion activity recognition in fluoroscopy sequences. The architecture is composed of two networks for instrument segmentation and action recognition. In this preliminary study, we demonstrate feasibility of recognising instrument manoeuvres automatically in our ex vivo *** relevance—The proposed framework contributes to image-based and automated assessment of endovascular tasks. This facilitates robotic control development, surgical education, and smart clinical documentation.

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Parallel affine transformation tuning of Markov Chain Monte Carlo 24

Parallel affine transformation tuning of Markov Chain Monte ...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Philip Schär Michael Habeck Daniel Rudolf Microscopic Image Analysis Group Friedrich Schiller University Jena Jena Germany Faculty of Computer Science and Mathematics University of Passau Passau Germany

The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior. We explore the use of bijective affine transformations of the sample space to improve the properties of the target distribution and thereby the performance of samplers running in the transformed space. In particular, we propose a flexible and user-friendly scheme for adaptively learning the affine transformation during sampling. Moreover, the combination of our scheme with Gibbsian polar slice sampling is shown to produce samples of high quality at comparatively low computational cost in several settings based on real-world data.

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