Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (2080) (remove)
Document Type
- Article (1594)
- Conference Proceeding (243)
- Book (97)
- Part of a Book (62)
- Doctoral Thesis (28)
- Patent (17)
- Report (15)
- Other (9)
- Habilitation (4)
- Lecture (3)
- Preprint (3)
- Conference: Meeting Abstract (1)
- Course Material (1)
- Review (1)
- Talk (1)
- Working Paper (1)
Keywords
- Biosensor (25)
- Finite-Elemente-Methode (16)
- CAD (15)
- civil engineering (14)
- Bauingenieurwesen (13)
- Einspielen <Werkstoff> (13)
- shakedown analysis (9)
- FEM (6)
- Limit analysis (6)
- Shakedown analysis (6)
Es wurde ein automatisiertes, computerunterstütztes Testsystem für die Funktionsprüfung und Charakterisierung von (bio-)chemischen Sensoren auf Waferebene entwickelt und in einen konventionellen Spitzenmessplatz integriert. Das System ermöglicht die Charakterisierung und Identifizierung „funktionstauglicher“ Sensoren bereits auf Waferebene zwischen den einzelnen Herstellungsschritten, wodurch weitere, bisher übliche Verarbeitungsschritte wie das Fixieren, Bonden und Verkapseln für die defekten oder nicht funktionstauglichen Sensorstrukturen entfällt. Außerdem bietet eine speziell entworfene miniaturisierte Durchflussmesszelle die Möglichkeit, bereits auf Waferlevel die Sensitivität, Drift, Hysterese und Ansprechzeit der (bio-)chemischen Sensoren zu charakterisieren. Das System wurde exemplarisch mit kapazitiven, pH-sensitiven EIS- (Elektrolyt-Isolator-Silizium) Strukturen und ISFET- (ionensensitiver Feldeffekttransistor) Strukturen mit verschiedenen Geometrien und Gate-Layouts getestet.
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
We study the estimation of some linear functionals which are based on an unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the estimated functional which is based on this semi-parametric estimator is asymptotically at least as efficient as the corresponding one which rests on the nonparametric Kaplan–Meier estimator.
In this paper we show that the estimated functional which is based on this semi-parametric estimator is asymptotically efficient with respect to the class of all regular estimators under this semi-parametric model.