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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/28756689a5f23fa376ec8f97cd9cfcaba/bjose",         
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         "label" : "Domain-randomised instance-segmentation benchmark for soot in PIV images",
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         "date" : "2025-12-19 13:48:16",
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         "journal": "Machine Learning: Science and Technology","publisher":"IOP Publishing",
         "year": "2025", 
         "url": "https://doi.org/10.1088/2632-2153/ae2565", 
         
         "author": [ 
            "Basil Jose","Klaus Peter Geigle","Fabian Hampp"
         ],
         "authors": [
         	
            	{"first" : "Basil",	"last" : "Jose"},
            	{"first" : "Klaus",	"last" : "Peter Geigle"},
            	{"first" : "Fabian",	"last" : "Hampp"}
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         "volume": "6","number": "4","pages": "040504","abstract": "Access to high-level statistical information from scientific image-based diagnostics is facilitated by the capacity to segment quantity-of-interest (QoI) signal accurately from measurement noise and interferences. In particular under conditions with diverse nuisance signatures, deep learning (DL) pipelines can outperform algorithm-based computer vision (CV) strategies and simultaneously enhance domain-invariance. DL-pipelines aim to overcome the generalisation bottleneck through augmentations and learnable parameters, supplemented by huge amounts of data. Yet, manual pixel-accurate annotation for scientific tasks is prohibitively expensive and the robustness of the trained models is often impeded by the underlying narrow and domain-specific set of training data. In the current study, a previously developed domain-randomised pipeline for automatic annotation and synthetic training data generation is benchmarked using a physics-aware composite score. An ablation study for background variance, QoI placement strategy and QoI object source proportions is conducted. The benchmark, consisting of 72 synthetic training data generation recipes, highlights the optimal set of domain-randomisation parameters to strike the balance between domain-invariance and segmentation accuracy. Synthetic training datasets with a balanced mix of QoI objects, fused onto realistic background instances, are found to provide the most accurate models for segmentation. The best model is subsequently used for the inference on semantically challenging Mie scattering images containing particle-image velocimetry (PIV) tracer particles and soot filaments. The model\u2019s ability to detect soot structures with good accuracy is demonstrated and high-level soot filament area and contour statistics (e.g. curvature and fractal dimension) are used to delineate the effects of turbulent flow on soot filament structures. The present study highlights key parameters to tune domain randomisation strategies for DL-training pipelines and the pipeline usability and transferability is proved for autonomous semi-supervised learning. This eases access to high-level statistics in scientific image-based diagnostics.",
         "doi" : "10.1088/2632-2153/ae2565",
         
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         "journal": "International Journal of Multiphase Flow",
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         "url": "https://www.sciencedirect.com/science/article/pii/S0301932225000576", 
         
         "author": [ 
            "Basil Jose","Oliver Lammel","Fabian Hampp"
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            	{"first" : "Basil",	"last" : "Jose"},
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         "volume": "187","pages": "105179","abstract": "Fuel spray atomization in gas turbine systems significantly impacts the combustion process and thereby emission formation. Considering the necessity for quantitative description of the influence of operating conditions on the spray breakup mechanisms, a machine learning (ML) based methodology is introduced to accurately segment the dispersed liquid from the continuous gaseous phase in shadowgraphy images. The segmented images subsequently facilitate a high-level statistical analysis of gas-liquid-interface contours and ultimately instability dynamics. For this purpose, multiple ML models varying in architecture (Semantic FPN and DeepLabV3+), datasets and augmentations are benchmarked to achieve the best performance. Subsequently, the best model is validated and used to obtain conditional statistics on the detected spray contours of three different spray types (jet-in-crossflow, pressure swirl spray and prefilming airblast spray). The model showcases high robustness, transferability across spray configurations and accuracy along multiple never-seen sprays thereby illustrating the superiority of deep learning methods for scientific image segmentation tasks. Moreover, the inferred high-level statistical analysis provides novel quantitative insights into the involved turbulence-spray interactions aiding the understanding of jet, sheet and film atomization under highly turbulent flow conditions.",
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         "author": [ 
            "Basil Jose","Fabian Hampp","Yeonse Kang"
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            	{"first" : "Basil",	"last" : "Jose"},
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            	{"first" : "Yeonse",	"last" : "Kang"}
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         "author": [ 
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         "label" : "Supervised learning without labelling: Applied domain randomization on scientific images",
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         "journal": "8th WAW Workshop Machine Learning",
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         "author": [ 
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            	{"first" : "Klaus Peter",	"last" : "Geigle"},
            	{"first" : "Fabian",	"last" : "Hampp"}
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            	{"first" : "Basil",	"last" : "Jose"},
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         "note": "Related to: Jose, B., Hampp, F., 2024. Machine learning based spray process quantification. International Journal of Multiphase Flow 172,  104702. doi: 10.1016/j.ijmultiphaseflow.2023.104702","abstract": "This dataset contains the necessary code for using our spray segmentation model used in the paper, Machine learning based spray process quantification. More information can be found in the README.md.",
         "affiliation" : "Jose, Basil/Universität Stuttgart, Hampp, Fabian/Universität Stuttgart",
         
         "orcid-numbers" : "Jose, Basil/0009-0007-7707-8614, Hampp, Fabian/0000-0002-9895-4288",
         
         "doi" : "10.18419/darus-4147",
         
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         "author": [ 
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            	{"first" : "Basil",	"last" : "Jose"},
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         "author": [ 
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         "authors": [
         	
            	{"first" : "Basil",	"last" : "Jose"},
            	{"first" : "Klaus Peter",	"last" : "Geigle"},
            	{"first" : "Fabian",	"last" : "Hampp"}
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         "author": [ 
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         "author": [ 
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         "authors": [
         	
            	{"first" : "Sabrina",	"last" : "Gado"},
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            	{"first" : "Maria",	"last" : "Wirzberger"},
            	{"first" : "Mathias",	"last" : "Vukelić"}
         ],
         "volume": "23","number": "14","abstract": "Humans\u2019 performance varies due to the mental resources that are available to successfully pursue a task. To monitor users\u2019 current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.",
         "pubmedid" : "37514840",
         
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   ]
}