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Advancing deep learning-based detection of floating litter using a novel open dataset

Jia, T. (author), Vallendar, A.J. (author), de Vries, Rinze (author), Kapelan, Z. (author), Taormina, R. (author)

Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labeled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA)....

journal article 2023

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Deep residual learning for acoustic emission source localization in A steel-concrete composite slab

Zhou, Y. (author), Liang, M. (author), Yue, X. (author)

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel...

journal article 2023

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Indications of marine benthos occurrence from multi-spectral multi-beam backscatter data: a case study in the North Sea

Bai, Q. (author), Mestdagh, S.M.F. (author), Snellen, M. (author), Simons, D.G. (author)

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of...

journal article 2023

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Grouped People Counting Using mm-wave FMCW MIMO Radar

Ren, Liyuan (author), Yarovoy, Alexander (author), Fioranelli, F. (author)

The problem of radar-based counting of multiple individuals moving as a single group is addressed using an mm-wave multiple-input-multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar. This problem is challenging because the different individuals are closer to each other than the range/azimuth resolution, and their bulk...

journal article 2023

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Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Giardina, Giorgia (author), Macchiarulo, V. (author), Foroughnia, Fatemeh (author), Jones, Joshua N. (author), Whitworth, Michael R.Z. (author), Voelker, Brandon (author), Milillo, Pietro (author), Penney, Camilla (author), Adams, Keith (author), Kijewski-Correa, Tracy (author)

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing...

journal article 2023

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Estimation of drone intention using trajectory frequency defined in radar's measurement phase planes

Yun, Joongsup (author), Anderson, David (author), Fioranelli, F. (author)

This paper presents a radar-based algorithm for autonomous estimation of drone intention. The algorithm is based on radar's kinematic measurements, providing fast and robust intention estimation for multiple targets. The core idea of the proposed algorithm is to build intention-specific features for each intention in advance and use them in...

journal article 2023

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Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data

Welvaars, Koen (author), Oosterhoff, J.H.F. (author), van den Bekerom, Michel P.J. (author)

Objective: When correcting for the “class imbalance” problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. Materials and Methods: Multiple classification algorithms were trained on 7 resampled...

journal article 2023

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Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

Haleem Medattil Ibrahim, M.I. (author), Sharma, Madhu (author), Subramaniam Rajkumar, Vetrivel (author)

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units ((Formula presented.) PMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed ...

journal article 2023

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Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO

Haifawi, Hani (author), Fioranelli, F. (author), Yarovoy, Alexander (author), van der Meer, Rob (author)

A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To...

conference paper 2023

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Characterizing seabed sediments using multi-spectral backscatter data in the North Sea

Bai, Q. (author), Mestdagh, S.M.F. (author), Snellen, M. (author), Amiri Simkooei, A. (author)

Acoustic classification using single-beam and multi-beam echosounders has been widely applied in characterizing seabed sediments. Although previous studies have shown a better discrimination of fine and coarse sediments using multi-spectral echosounder data, analysis regarding comprehensive seabed sediment properties is still needed. In this...

conference paper 2023

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Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis

van der Sar, Iris G. (author), van Jaarsveld, Nynke (author), Spiekerman, Imme A. (author), Toxopeus, Floor J. (author), Langens, Quint L. (author), Wijsenbeek, Marlies S. (author), Dauwels, J.H.G. (author), Moor, Catharina C. (author)

Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose disease, as no standard procedure or conclusive test exists. An accurate diagnostic model based on...

journal article 2023

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Value-Based Hybrid Intelligence

Sayin, Burcu (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)

In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people. We show that with this perspective we fundamentally change how we evaluate and select machine learning models.

conference paper 2023

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Radar Sensing in Healthcare: Challenges and Achievements in Human Activity Classification & Vital Signs Monitoring

Fioranelli, F. (author), Guendel, Ronny (author), Kruse, N.C. (author), Yarovoy, Alexander (author)

Driven by its contactless sensing capabilities and the lack of optical images being recorded, radar technology has been recently investigated in the context of human healthcare. This includes a broad range of applications, such as human activity classification, fall detection, gait and mobility analysis, and monitoring of vital signs such as...

conference paper 2023

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Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming

van der Linden, J.G.M. (author), de Weerdt, M.M. (author), Demirović, E. (author)

Global optimization of decision trees has shown to be promising in terms of accuracy, size, and consequently human comprehensibility. However, many of the methods used rely on general-purpose solvers for which scalability remains an issue. Dynamic programming methods have been shown to scale much better because they exploit the tree structure by...

conference paper 2023

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BoundED: Neural boundary and edge detection in 3D point clouds via local neighborhood statistics

Bode, Lukas (author), Weinmann, M. (author), Klein, Reinhard (author)

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios...

journal article 2023

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Simplex-based multinomial logistic regression with diverging numbers of categories and covariate

Fu, Sheng (author), Chen, P. (author), Liu, Yufeng (author), Ye, Zhisheng (author)

Multinomial logistic regression models are popular in multicategory classification analysis, but existing models suffer several intrinsic drawbacks. In particular, the parameters cannot be determined uniquely because of the over-specification. Although additional constraints have been imposed to refine the model, such modifications can be...

journal article 2023

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Tough Decisions? Supporting System Classification According to the AI Act

Hanif, Hilmy (author), Constantino Torres, J.E. (author), Sekwenz, M.T. (author), van Eeten, M.J.G. (author), Ubacht, J. (author), Wagner, Ben (author), Zhauniarovich, Y. (author)

The AI Act represents a significant legislative effort by the European Union to govern the use of AI systems according to different risk-related classes, linking varying degrees of compliance obligations to the system's classification. However, it is often critiqued due to the lack of general public comprehension and effectiveness regarding...

conference paper 2023

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Data Segmentation and Fusion for Classification of Armed Personnel Using Micro-Doppler Signatures

Focante, E. (author), Martin, Lilian De (author), Coutino, Mario (author)

In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available...

conference paper 2023

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Simplex-based Proximal Multicategory Support Vector Machine

Fu, Sheng (author), Chen, P. (author), Ye, Zhisheng (author)

The multicategory support vector machine (MSVM) has been widely used for multicategory classification. Despite its widespread popularity, regular MSVM cannot provide direct probabilistic results and suffers from excessive computational cost, as it is formulated on the hinge loss function and it solves a sum-to-zero constrained quadratic...

journal article 2023

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Evaluating classifiers in SE research: the ECSER pipeline and two replication studies

Dell'Anna, D. (author), Aydemir, Fatma Başak (author), Dalpiaz, Fabiano (author)

Context: Automated classifiers, often based on machine learning (ML), are increasingly used in software engineering (SE) for labelling previously unseen SE data. Researchers have proposed automated classifiers that predict if a code chunk is a clone, if a requirement is functional or non-functional, if the outcome of a test case is non...

journal article 2023