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Predicting streamflow with LSTM networks using global datasets

Wilbrand, K. (author), Taormina, R. (author), ten Veldhuis, Marie-claire (author), Visser, Martijn (author), Hrachowitz, M. (author), Nuttall, Jonathan (author), Dahm, Ruben (author)

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually...

journal article 2023

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DeepEgo: Deep Instantaneous Ego-Motion Estimation Using Automotive Radar

Zhu, S. (author), Yarovoy, Alexander (author), Fioranelli, F. (author)

The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from input point clouds and applies weighted least squares (WLS) for...

journal article 2023

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SoK: Deep Learning-based Physical Side-channel Analysis

Picek, S. (author), Perin, G. (author), Mariot, L. (author), Wu, L. (author), Batina, Lejla (author)

Side-channel attacks represent a realistic and serious threat to the security of embedded devices for already almost three decades. A variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated. Deep learning...

journal article 2023

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Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI

Yarally, Tim (author), Cruz, Luis (author), Feitosa, Daniel (author), Sallou, J. (author), van Deursen, A. (author)

Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results"often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy consumption as a metric of equal importance to accuracy and to...

conference paper 2023

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Automatic landmark correspondence detection in medical images with an application to deformable image registration

Grewal, M. (author), Wiersma, Jan (author), Westerveld, Henrike (author), Bosman, P.A.N. (author), Alderliesten, T. (author)

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural...

journal article 2023

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Shape Sensing of Flexible Robots Based on Deep Learning

Ha, Xuan Thao (author), Wu, D. (author), Ourak, Mouloud (author), Borghesan, Gianni (author), Dankelman, J. (author), Menciassi, Arianna (author), Poorten, Emmanuel Vander (author)

In this article, a deep learning method for the shape sensing of continuum robots based on multicore fiber bragg grating (FBG) fiber is introduced. The proposed method, based on an artificial neural network (ANN), differs from traditional approaches, where accurate shape reconstruction requires a tedious characterization of many...

journal article 2023

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Label Correlation in Deep Learning-Based Side-Channel Analysis

Wu, L. (author), Weissbart, L.J.A. (author), Krcek, M. (author), Li, H. (author), Perin, G. (author), Batina, Lejla (author), Picek, S. (author)

The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is...

journal article 2023

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Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction

Huang, Y. (author), Yu, Suihuai (author), Chu, Jianjie (author), Su, Zhaojing (author), Zhu, Yaokang (author), Wang, Hanyu (author), Wang, Mengcheng (author), Fan, Hao (author)

Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided)...

journal article 2023

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The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

Estebanez Camarena, M. (author), Curzi, Fabio (author), Taormina, R. (author), van de Giesen, N.C. (author), ten Veldhuis, Marie-claire (author)

West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products...

journal article 2023

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Convolutional neural network framework for wind turbine electromechanical fault detection

Stone, Emilie (author), Giani, Stefano (author), Zappalá, D. (author), Crabtree, C.J. (author)

Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high-dimensional raw condition monitoring data for the automatic...

journal article 2023

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Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Zheng, Feifei (author), Yin, Hang (author), Ma, Yiyi (author), Duan, Huan Feng (author), Gupta, Hoshin (author), Savic, Dragan (author), Kapelan, Z. (author)

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real-time urban flood forecasting. Using existing rainfall...

journal article 2023

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Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

Joseph, G. (author), Zhong, Chen (author), Gursoy, M. Cenk (author), Velipasalar, Senem (author), Varshney, Pramod (author)

We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that...

journal article 2023

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Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings

Oyibo, P.O. (author), Meulah, Brice (author), Bengtson, Michel (author), Lieshout, Lisette van (author), Oyibo, Wellington (author), Diehl, J.C. (author), Vdovin, Gleb (author), Agbana, T.E. (author)

Purpose: Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage...

journal article 2023

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Toward automatic generation of control structures for process flow diagrams with large language models

Hirtreiter, E.J. (author), Schulze Balhorn, L. (author), Schweidtmann, A.M. (author)

Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data-driven method for the prediction of control structures. Our methodology is inspired by end-to-end transformer-based human language translation models. We cast the control structure prediction as a translation task where...

journal article 2023

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Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread

Croft, V.M. (author), van Iersel, Senna C. J. L. (author), Della Santina, C. (author)

The spread of an epidemic over a population is influenced by a multitude of factors having both spatial and temporal nature, which are hard to completely capture using first principle methods. This paper concerns regional forecasting of SARS-Cov-2 infections 1 week ahead using machine learning. We especially focus on the Dutch case study for...

journal article 2023

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Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction

Zhao, Wenzhao (author), Fan, Yuling (author), Wang, Hongjian (author), Gemmeke, Hartmut (author), van Dongen, K.W.A. (author), Hopp, Torsten (author), Hesser, Jürgen (author)

Objective. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled...

journal article 2023

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Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning

van der Voort, Sebastian R. (author), Incekara, Fatih (author), Wijnenga, Maarten M.J. (author), Kapsas, Georgios (author), Schouten, J.W. (author), French, P.J. (author), Niessen, W.J. (author), Smits, M. (author), Klein, Stefan (author)

BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can...

journal article 2023

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The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA

Perin, G. (author), Wu, L. (author), Picek, S. (author)

The adoption of deep neural networks for profiling side-channel attacks opened new perspectives for leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: an extensive...

journal article 2023

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Deep Learning for Size-Agnostic Inverse Design of Random-Network 3D Printed Mechanical Metamaterials

Pahlavani, H. (author), Tsifoutis-Kazolis, Kostas (author), Cruz Saldivar, M. (author), Mody, Prerak (author), Zhou, J. (author), Mirzaali, Mohammad J. (author), Zadpoor, A.A. (author)

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures...

journal article 2023

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The Potential of Deep Learning for Satellite Rainfall Detection over Data-Scarce Regions, the West African Savanna

Estebanez Camarena, M. (author), Taormina, R. (author), van de Giesen, N.C. (author), ten Veldhuis, Marie-claire (author)

Food and economic security in West Africa rely heavily on rainfed agriculture and are threatened by climate change and demographic growth. Accurate rainfall information is therefore crucial to tackling these challenges. Particularly, information about the occurrence and length of droughts as well as the onset date of the rainy season is...

journal article 2023