Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Gühmann, Clemens"'
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the
Externí odkaz:
http://arxiv.org/abs/2209.04213
Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision
RUL estimation suffers from a server data imbalance where data from machines near their end of life is rare. Additionally, the data produced by a machine can only be labeled after the machine failed. Semi-Supervised Learning (SSL) can incorporate the
Externí odkaz:
http://arxiv.org/abs/2108.08721
Autor:
Weber, Daniel, Gühmann, Clemens
The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transfe
Externí odkaz:
http://arxiv.org/abs/2105.02027
Publikováno v:
AI 2021, 2, 444-463
Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of distu
Externí odkaz:
http://arxiv.org/abs/2104.07391
Publikováno v:
In IFAC PapersOnLine 2024 58(4):240-245
Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and stat
Externí odkaz:
http://arxiv.org/abs/2005.06897
Publikováno v:
In Journal of Manufacturing Processes 24 November 2023 106:338-346
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Advanced Engineering Informatics January 2023 55
Publikováno v:
SAE International Journal of Engines, 2020 Jan 01. 13(2), 253-266.
Externí odkaz:
https://www.jstor.org/stable/27034052