Open Access
Refine
Year of publication
Document Type
- Article (141)
- Conference Proceeding (72)
- Bachelor Thesis (12)
- Master's Thesis (12)
- Report (7)
- Doctoral Thesis (3)
Language
- English (247) (remove)
Keywords
- virtual reality (9)
- Business Process Management Systems (4)
- Fuzzy Logic (4)
- visualization (4)
- Myopia (3)
- Assignment Automation (2)
- Augmented Reality (2)
- Business Process Modeling Notation (2)
- Git (2)
- Studie (2)
Institute
Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD.
In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts.
Lower bounds on the sum of 25th-powers of univariates lead to complete derandomization of PIT
(2020)
WAR FOR TALENTS MEETS FACIAL EXPRESSION - leveraging recruiting videos in professional service firms
(2020)
Highlighting Thermal Post-Treatment for Improving Long-Term Media-Tightness of Polymer-Metal Hybrids
(2021)
Purpose
To determine the stereo threshold and inherent variability with a monitor-based two-rod test at various eccentricities of the visual field. Additionally, to evaluate the duration of this procedure.
Subjects and methods
A pilot trial was conducted in five ophthalmologically normal subjects (2 male and 3 female) aged 21 – 23 years. Two black rods on white background, which appeared under an angle of 1° were presented in a viewing distance of 5.0 meters. The right rod was stationary, whilst the left rod appeared under a stereoscopic parallax, with an either proximal or distal displacement to the image plane. Threshold determination was assessed at seven eccentricities of the visual field by a staircase method. Eccentricities were 0° centrally, 5° to the right and left, 10° to the right and left and 15° to the right and left of the visual field. Proximal and distal displacement as well as the sequence of eccentricities were presented in random order. Stereo acuity was measured in two different sessions for four subjects and in five different sessions for one subject. For all sessions the duration was recorded. All sessions were separated by a time interval of at least 24 hours and no longer than 7 days. Evaluation was made by Wilcoxon test and Kruskal Wallis test at the 95% confidence level (CI) and the median was assessed for all thresholds.
Results
Stereo acuity declines with increasing eccentricities of the retina similar to visual acuity. While at 0° eccentricity thresholds were found to be lowest with (median) 5.0 seconds of arc (‘’) and the CI (0.5’’, 30.5’’) for all measurements, they increased to 112.2’’ at 15° eccentricity to the left in proximal displacement. Distal it was 19.9’’ centrally and 112.2’’ to the right at 15° eccentricity with CI (0.5’’, 30.5’’) for all measurements.
Repeatability of the threshold determination was found to be best at 0° eccentricity with proximal displacement showing the exact same result in the repetitive session and poorest repetition was found at 15° eccentricity to the left with distal displacement. Distal repeatability was worse than proximal. Median and CI of duration time was 5.3 (3.2, 8.3) minutes.
Conclusion
Stereo acuity thresholds are repeatable however increase with increasing eccentricity. Repetitions of the threshold determination do not vary considerably. The duration of these measurements indicates the monitor-based two-rod test as a fast procedure, that can be applied in future studies. The test program is limited by an imperfect algorithm and the stereoscopic images evoke cues, this should be reworked.