Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Andreas Schwung"'
Publikováno v:
SoftwareX, Vol 28, Iss , Pp 101956- (2024)
Game theory, a fundamental aspect of mathematical economics and strategic decision-making, has been increasingly applied to various fields, including economics, biology, computer science, and engineering. Despite its growing importance, there is a si
Externí odkaz:
https://doaj.org/article/b340ac558ac7440dad58400383a70e87
Publikováno v:
IEEE Access, Vol 12, Pp 2236-2259 (2024)
Fault detection systems support the operator, providing insight during the decision-making while having an (unknown) fault. Data-based models are a common option for a detection system. However, systems that rely purely on data-based models are norma
Externí odkaz:
https://doaj.org/article/b9ac52812d24492f82cab8a1787dde89
Publikováno v:
Drones, Vol 8, Iss 7, p 307 (2024)
The complexities of decision-making in drone airlines prove to be pivotal and challenging as the dynamic environment introduces variability and many decisions are conventionally static. This paper introduces an advanced decision-making system designe
Externí odkaz:
https://doaj.org/article/a63469b32b444680a4aa781a9419b236
Publikováno v:
IEEE Access, Vol 11, Pp 53545-53587 (2023)
Multi-class ensemble classification remains a popular focus of investigation within the research community. The popularization of cloud services has sped up their adoption due to the ease of deploying large-scale machine-learning models. It has also
Externí odkaz:
https://doaj.org/article/0eb8000b0cd34d4883fa6b0b39bcba47
Publikováno v:
IEEE Access, Vol 10, Pp 74244-74258 (2022)
This paper proposes a novel deep learning architecture for estimating the remaining useful lifetime (RUL) of industrial components, which solely relies on the recently developed transformer architectures. The RUL estimation resorts to analysing degra
Externí odkaz:
https://doaj.org/article/99e598de00f24ef08ef93ba232b137ea
Autor:
Johannes Poppelbaum, Andreas Schwung
Publikováno v:
IEEE Access, Vol 10, Pp 82923-82943 (2022)
We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of information with a main focus on describing rigid body movements. After introducing the underlying dual quaternion math, we derive du
Externí odkaz:
https://doaj.org/article/4c48041f06334c41857c971787634fa3
Publikováno v:
IEEE Access, Vol 10, Pp 89134-89152 (2022)
Operational knowledge is one of the most valuable assets in a company, as it provides a strategic advantage over competitors and ensures steady and optimal operation in machines. An (interactive) assessment system on the shop floor can optimize the p
Externí odkaz:
https://doaj.org/article/7d5d4aa21bbe491ea968739fb1db3c19
Publikováno v:
Machine Learning with Applications, Vol 9, Iss , Pp 100341- (2022)
Nowadays there are numerous powerful software packages available for most areas of machine learning (ML). These can be roughly divided into frameworks that solve detailed aspects of ML and those that pursue holistic approaches for one or two learning
Externí odkaz:
https://doaj.org/article/5f870dc9099c43c6ac51c70578524dee
Publikováno v:
Intelligent Systems with Applications, Vol 10, Iss , Pp 200049- (2021)
We propose a novel sequence-to-sequence prediction approach for the estimation of the remaining useful lifetime (RUL) of technical components. The approach is based on deep recurrent neural network structures, namely bidirectional Long Short Term Mem
Externí odkaz:
https://doaj.org/article/a47fd5590b6d4633babaac6dc5d22e48
Publikováno v:
Sensors, Vol 21, Iss 16, p 5488 (2021)
This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture. As a core novelty, we split the autoenco
Externí odkaz:
https://doaj.org/article/60cecf0efafc4ace9ccc79ed188b6384