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PublicationTowards Small Anomaly Detection( 2024)In this position paper, we describe the design of a camera-based FOD (Foreign Object Debris) detection system intended for use in the parking position at the airport. FOD detection, especially the detection of small objects, requires a great deal of human attention. The transfer of ML (machine learning) from the laboratory to the field calls for adjustments, especially in testing the model. Automated detection requires not only high detection performance and low false alarm rate, but also good generalization to unknown objects. There is not much data available for this use case, so in addition to ML methods, the creation of training and test data is also considered.
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PublicationDelineating morbidity patterns in preterm infants at near-term age using a data-driven approach( 2024)Background: Long-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles. Methods: Our study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP). For analysis, we employed two independent, prospective cohorts, comprising a total of 530 very preterm infants: AIRR ("Attention to Infants at Respiratory Risks") and NEuroSIS ("Neonatal European Study of Inhaled Steroids"). Using a data-driven strategy, we successfully characterized morbidity profiles of preterm infants in a stepwise approach and (1) quantified pairwise morbidity correlations, (2) assessed the discriminatory power of BPD (complemented by imaging-based structural and functional lung phenotyping) in relation to these morbidities, (3) investigated collective co-occurrence patterns, and (4) identified infant subgroups who share similar morbidity profiles using machine learning techniques. Results: First, we showed that, in line with pathophysiologic understanding, BPD and ROP have the highest pairwise correlation, followed by BPD and PH as well as BPD and mild cardiac defects. Second, we revealed that BPD exhibits only limited capacity in discriminating morbidity occurrence, despite its prevalence and clinical indication as a driver of comorbidities. Further, we demonstrated that structural and functional lung phenotyping did not exhibit higher association with morbidity severity than BPD. Lastly, we identified patient clusters that share similar morbidity patterns using machine learning in AIRR (n=6 clusters) and NEuroSIS (n=8 clusters). Conclusions: By capturing correlations as well as more complex morbidity relations, we provided a comprehensive characterization of morbidity profiles at discharge, linked to shared disease pathophysiology. Future studies could benefit from identifying risk profiles to thereby develop personalized monitoring strategies.
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PublicationVariational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments( 2024)
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PatentVerfahren und Vorrichtungen für automatische, kooperative Manöver( 2023-03-23)Offenbart ist ein Verfahren, umfassend die Schritte:- Erhalten einer gemeinsamen Umgebungsinformation wenigstens zweier Maschinen;- Generieren einer Mehrzahl von Kooperationsmanövern, welche jeweils ein Manöver für jede der zwei Maschinen umfassen, auf Basis der Umgebungsinformation;- Bewerten jedes Kooperationsmanövers auf Basis eines vorgegebenen Gütekriteriums;- Auswählen eines Kooperationsmanövers, welches einen vorbestimmten Wert des Gütekriteriums, insbesondere einen Bestwert, aufweist;- Bereitstellen des ausgewählten Kooperationsmanövers für die zwei Maschinen.
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PublicationSensing and Machine Learning for Automotive Perception: A Review( 2023)Automotive perception involves understanding the external driving environment as well as the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.
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PublicationConcept Correlation and its Effects on Concept-Based Models( 2023)Concept-based learning approaches for image classification, such as Concept Bottleneck Models, aim to enable interpretation and increase robustness by directly learning high-level concepts which are used for predicting the main class. They achieve competitive test accuracies compared to standard end-to-end models. However, with multiple concepts per image and binary concept annotations (without concept localization), it is not evident if the output of the concept model is truly based on the predicted concepts or other features in the image. Additionally, high correlations between concepts would allow a model to predict a concept with high test accuracy by simply using a correlated concept as a proxy. In this paper, we analyze these correlations between concepts in the CUB and GTSRB datasets and propose methods beyond test accuracy for evaluating their effects on the performance of a concept-based model trained on this data. To this end, we also perform a more detailed analysis on the effects of concept correlation using synthetically generated datasets of 3D shapes. We see that high concept correlation increases the risk of a model's inability to distinguish these concepts. Yet simple techniques, like loss weighting, show promising initial results for mitigating this issue.
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PublicationBenchmarking the Variational Quantum Eigensolver using different quantum hardware( 2023)
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PublicationEntwicklung und Zertifizierung klinischer KI-Software( 2023)Das derzeitige Interesse an Künstlicher Intelligenz (KI) wird weitgehend durch die beeindruckende Leistung großer Sprachmodelle wie ChatGPT getrieben, was erhebliche mediale Aufmerksamkeit erregt hat. Obwohl bereits zahlreiche KI-Lösungen für verschiedene klinische Anwendungen wie Radiologie, Pathologie, Kaloskopie und Krebstherapie entwickelt wurden, sind bisher nur wenige in Kliniken implementiert. Das wirft die Frage auf, warum dies der Fall ist. Um diesen Umstand näher zu beleuchten, bietet der vorliegende Artikel einen kurzen Überblick darüber, wie Kl funktioniert, und beschreibt den Prozess der Herstellung und der Zertifizierung eines KI-Systems. Außerdem werden die Herausforderungen skizziert, die bei der Garantie von Zuverlässigkeit und Sicherheit klinischer KI-Systeme auftreten.
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PublicationEffects of defects in automated fiber placement laminates and its correlation to automated optical inspection results( 2023)Automated Fiber Placement (AFP) is a widely used production process for the manufacturing of large scale CFRP parts. However, the occurrence of manufacturing defects such as gaps or overlaps is still a common problem in today’s AFP production environments. This study investigates the effect of different defect configurations on the mechanical performance (i.e., tensile strength, flexural strength, and shear strength) of AFP laminates. The results are then linked to the data generated “inline” by a ply inspection system. We use the Pearson correlation in order to relate the measured defect volume to the strength of samples containing different types of defects. A clear knockdown in tensile strength was found for specimens with gaps or overlaps that caused a high amount of fiber undulations in the laminate. The sensor data analysis showed a similar trend. Specimens with a high defect volume had significantly lower values for the tensile strength. A correlation coefficient of -0.98 between these two values was calculated. The obtained results are a promising step towards automated quality inspection for the AFP process.
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PublicationSafe, Ethical and Sustainable: Framing the Argument( 2023)The authors have previously articulated the need to think beyond safety to encompass ethical and environmental (sustainability) concerns, and to address these concerns through the medium of argumentation. However, the scope of concerns is very large and there are other challenges such as the need to make trade-offs between incommensurable concerns. The paper outlines an approach to these challenges through suitably framing the argument and illustrates the approach by considering alternative concept designs for an autonomous mobility service.
