‘Signal Processing’ encompasses the handling of signal data. This includes all applications of time series and sensor data in general, such as vibration or temperature data. It mainly includes methods of supervised learning, which means learning algorithms that use labeled training datasets.
an example in engineering is the analysis of machine signals to detect anomalies and associated faulty behavior.
‘Decision Support’ deals with methods of decision support in complex environments.
an example of this is case-based reasoning, where problem solutions are determined through analogy, meaning similar problems are approached with similar solutions.
‘Modeling Languages’ deal with formal models. In the context of engineering, this particularly refers to diagrams of modeling languages such as UML or SysML.
important examples include the transformation of platform-independent models into platform-specific models or the translation between different formalisms.
‘Natural Language Processing’ encompasses all methods for handling human language in speech and text.
examples in the engineering context include the analysis of requirement documents or customer reviews.
‘Computer Vision’ encompasses all methods for handling image and video data. This includes activities related to analyzing and understanding camera images.
a classic example is object recognition, where objects such as components in an image are localized and labeled.
‘Knowledge Discovery’ encompasses activities related to discovering and representing knowledge. This includes the discovery of knowledge in large data sets using methods of data mining, as well as the handling of structured knowledge, such as knowledge graphs. It mainly involves methods of unsupervised learning, which means learning without labeled training data.
EA classic example is cluster analysis, which involves grouping data based on underlying similarity structures.
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