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Titlebook: Machine Learning for Cyber Physical Systems; Selected papers from Jürgen Beyerer,Christian Kühnert,Oliver Niggemann Conference proceedings‘

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樓主: aspirant
11#
發(fā)表于 2025-3-23 12:15:55 | 只看該作者
12#
發(fā)表于 2025-3-23 15:25:45 | 只看該作者
13#
發(fā)表于 2025-3-23 18:34:27 | 只看該作者
14#
發(fā)表于 2025-3-23 22:55:28 | 只看該作者
Making Industrial Analytics work for Factory Automation Applications,example, we consider a machine learning use case in the area of industry compressors. We discuss the importance of scalability and reusability of data analytics pipelines and present a container-based system architecture.
15#
發(fā)表于 2025-3-24 02:44:14 | 只看該作者
16#
發(fā)表于 2025-3-24 10:16:08 | 只看該作者
17#
發(fā)表于 2025-3-24 11:56:02 | 只看該作者
A Random Forest Based Classifier for Error Prediction of Highly Individualized Products, complex and hinders the usage of machine learning algorithms straight out-of-the-box. The findings regarding these features and how to treat the concluded challenges are highlighted in a abstracted and generalized manner.
18#
發(fā)表于 2025-3-24 17:50:33 | 只看該作者
19#
發(fā)表于 2025-3-24 19:47:32 | 只看該作者
Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinen algorithms, kinematics data is collected exemplarily for an overrun of a cable channel and a bulb plate. A recurrent neuronal network (RNN), based on the autoencoder principle, could be trained successfully with this data. The described RNN architecture looks promising to be used for realtime anomaly detection and also to quantify path quality.
20#
發(fā)表于 2025-3-25 00:23:27 | 只看該作者
Enabling Self-Diagnosis of Automation Devices through Industrial Analytics,e maintenance strategy, while drastically reducing the realization effort. Furthermore, the benefits of a flexible architecture combining edge- and cloud-computing for the realization of such monitoring system are discussed.
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