Zobrazeno 1 - 10
of 1 823
pro vyhledávání: '"P, Kemnitz"'
Vibration-based condition monitoring systems are receiving increasing attention due to their ability to accurately identify different conditions by capturing dynamic features over a broad frequency range. However, there is little research on clusteri
Externí odkaz:
http://arxiv.org/abs/2305.06753
Autor:
Pruckovskaja, Viktorija, Weissenfeld, Axel, Heistracher, Clemens, Graser, Anita, Kafka, Julia, Leputsch, Peter, Schall, Daniel, Kemnitz, Jana
Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning m
Externí odkaz:
http://arxiv.org/abs/2304.11101
Autor:
Zhihao Zheng, Christopher S. Own, Adrian A. Wanner, Randal A. Koene, Eric W. Hammerschmith, William M. Silversmith, Nico Kemnitz, Ran Lu, David W. Tank, H. Sebastian Seung
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larg
Externí odkaz:
https://doaj.org/article/cd50b5dc025f445b9fa3853971c5a1c2
Autor:
Chuan Qin, Leonie G. Graf, Kilian Striska, Markus Janetzky, Norman Geist, Robin Specht, Sabrina Schulze, Gottfried J. Palm, Britta Girbardt, Babett Dörre, Leona Berndt, Stefan Kemnitz, Mark Doerr, Uwe T. Bornscheuer, Mihaela Delcea, Michael Lammers
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-22 (2024)
Abstract The AMP-forming acetyl-CoA synthetase is regulated by lysine acetylation both in bacteria and eukaryotes. However, the underlying mechanism is poorly understood. The Bacillus subtilis acetyltransferase AcuA and the AMP-forming acetyl-CoA syn
Externí odkaz:
https://doaj.org/article/dc277252131d4568afb2aac51547d6c8
Autor:
Holly, Stephanie, Heel, Robin, Katic, Denis, Schoeffl, Leopold, Stiftinger, Andreas, Holzner, Peter, Kaufmann, Thomas, Haslhofer, Bernhard, Schall, Daniel, Heitzinger, Clemens, Kemnitz, Jana
Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies
Externí odkaz:
http://arxiv.org/abs/2210.08011
We propose a new sampling strategy, called smart active sapling, for quality inspections outside the production line. Based on the principles of active learning a machine learning model decides which samples are sent to quality inspection. On the one
Externí odkaz:
http://arxiv.org/abs/2209.11464
Publikováno v:
Materials & Design, Vol 244, Iss , Pp 113226- (2024)
The increasing demand for materials capable of withstanding high temperatures and harsh environments necessitates the discovery of advanced alloys. This study introduces a computational routine to predict solid-state phase stability and calculates el
Externí odkaz:
https://doaj.org/article/0c4353f7ff57494e878bbd795bb76724
Autor:
Sergiy Popovych, Thomas Macrina, Nico Kemnitz, Manuel Castro, Barak Nehoran, Zhen Jia, J. Alexander Bae, Eric Mitchell, Shang Mu, Eric T. Trautman, Stephan Saalfeld, Kai Li, H. Sebastian Seung
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section ima
Externí odkaz:
https://doaj.org/article/0704e28ab41945b7975bc81970f13703
Autor:
Holly, Stephanie, Hiessl, Thomas, Lakani, Safoura Rezapour, Schall, Daniel, Heitzinger, Clemens, Kemnitz, Jana
Federated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business information. Th
Externí odkaz:
http://arxiv.org/abs/2110.08202
Autor:
Heistracher, Clemens, Jalali, Anahid, Suendermann, Axel, Meixner, Sebastian, Schall, Daniel, Haslhofer, Bernhard, Kemnitz, Jana
The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment. Recent advances in dee
Externí odkaz:
http://arxiv.org/abs/2110.04049