1167 research outputs found
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Classification of gravure printed patterns using singular value decomposition and machine learning (MATLAB code)
No full textThis dataset contains MATLAB code ('code_MachLearn_ImgClass.zip') for
automated classification of gravure printed patterns from the
[HYPA-p](https://doi.org/10.48328/tudatalib-1150) dataset. The developed
algorithm performs singular value decomposition (SVD) and training of several
machine learning classifiers, such as k-Nearest Neighbors (kNN). The
classifiers are trained and tested on labeled data. Afterwards, the trained
classifiers can be used for automated classification of unlabeled data.
Further information can be found in the provided README-file
Flame PDF - Direct Injecion for Cat-Heating Operating Points
No full textThe PIV raw images were used for flame detection and therefore have the same optical structure. Only the relevant parts of the cycles from the ignition point onwards were processed in Matlab. The raw images with a flame were also masked and the local standard deviation calculated. This local standard deviation was normalized using the standard deviation of an image without a flame. Areas of vaporized oil droplets differ in the standard deviation from areas with oil droplets and the associated high local intensity fluctuation. These differences in the values can be used to identify the area of burnt gas, referred to below as the flame, using a threshold value and to binarize the PIV images. The binarized images were averaged over the recorded cycles and normalized to one, indicating the probability that the flame reached this pixel at a given time. Statistical analyses were carried out on the basis of such flame probabilities.1.
Boundary Conditions - Direct Injecion for Cat-Heating Operating Points
No full textFor the investigations, the engine was operated at a speed of 1500 rpm and an intake pressure of 0.95 bar. Starting from the base operating point with early injection and optimized spark timing, variations relevant to engine operation during catalyst heating were performed. Variations in injection timing, number of injections, air-fuel ratio, and ignition timing were performed to investigate the influence of local mixing and turbulence on early flame propagation and the resulting combustion.
This dataset includes all 4 operating points1.
GRAFI-p: Gravure printing finger instability dataset (processed data)
No full textThe GRAFI-p dataset contains processed high speed videos of finger
instabilities during fluid splitting in gravure printing. The high speed
videos were recorded on a custom-built gravure printing research platform,
which enables optical access to the printing nip. The printing nip is the line
of contact between the engraved printing cylinder and the impression roller.
The aim of the dataset is the in-situ visualization and temporal analysis of
hydrodynamic pattern formation phenomena during fluid transfer. The influence
of the engraving raster on the finger instability can be investigated. The
GRAFI-p dataset comprises processed video data which was derived from raw
video data from the related [GRAFI-r](https://tudatalib.ulb.tu-
darmstadt.de/handle/tudatalib/3842) dataset.
GRAFI-p dataset stands for **gra** vure printing **f** inger **i** nstability
dataset. The suffix -p stands for **p** rocessed data.
Metadata for the GRAFI-p dataset can be found in the dissertation of Pauline
Rothmann-Brumm (2023) and in the provided README-file (last item in dataset,
click 'show more')
Code Publication for Methods to assess Spatio-Temporal Changes of Slum Populations
No full textThis publication contains all software used for assessing the spatio-temporal changes of slum population as published in https://dx.doi.org/10.2139/ssrn.4106192.
The publishers reserve the right to make further developments to the software, these will be made available on GITLAB (https://git.rwth-aachen.de/fst-tuda/public/assessment-of-spatio-temporal-changes-of-slum-populations). For this paper the commit tagged with V1.1.0 is used (https://git.rwth-aachen.de/fst-tuda/public/assessment-of-spatio-temporal-changes-of-slum-populations/-/commit/07c3b51b27779050a8d45aa6225536ec80f4a487).1.1.
Fast Axiomatic Attribution for Neural Networks
No full textMitigating the dependence on spurious correlations present in the training dataset is a quickly emerging and important topic of deep learning. Recent approaches include priors on the feature attribution of a deep neural network (DNN) into the training process to reduce the dependence on unwanted features. However, until now one needed to trade off high-quality attributions, satisfying desirable axioms, against the time required to compute them. This in turn either led to long training times or ineffective attribution priors. In this work, we break this trade-off by considering a special class of efficiently axiomatically attributable DNNs for which an axiomatic feature attribution can be computed with only a single forward/backward pass. We formally prove that nonnegatively homogeneous DNNs, here termed X-DNNs, are efficiently axiomatically attributable and show that they can be effortlessly constructed from a wide range of regular DNNs by simply removing the bias term of each layer. Various experiments demonstrate the advantages of X-DNNs, beating state-of-the-art generic attribution methods on regular DNNs for training with attribution priors
Figure S1 Polarization curves RDE + GDE IL modified PtC
No full textFigure S1: ORR activity of Pt/C catalyst modified with [BMIM][beti]. A) ORR polarization curves obtained in RDE measurements at room temperature in oxygen saturated 0.1 M HClO4 and 1600 rpm at a catalyst loading of 20 µgPt cm-2. B) ORR polarization curves obtained in GDE measurements at room temperature in oxygen atmosphere in 2 M HClO4 at a catalyst loading of 100 µgPt cm-2
Vapour Imaging: Python code to evaluate interferograms of evaporating liquid films and sample data.
No full textThis submission contains Python code to evaluate interferograms of evaporating liquid films and sample data.
It mainly consists of a module for preprocessing, a module to analyze the fringe pattern of the interferograms and a phase-unwrapping module.
All is accessible from a graphical user interface and a separate Python script, which focuses on the sequential evaluation of multiple interferograms and includes the calculation of a concentration distribution above evaporating rectangular liquid films.
Please refer to the PhD thesis for a discussion of the implemented algorithms and check 'README.txt' for installation instructions.2.