The worker productivity is now widely recognized as a key factor in determining a company long-term viability. The productivity of an organization workers is a major factor in the company success. Employee productivit...
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The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS 2 integration for the Robotino available. In this paper...
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Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective....
Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.
Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely i...
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In recent years, the availability of modern technology increased drastically with the raising availability of integrated devices and applications, such as mobile phones, voice assistant systems smart home appliances a...
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ISBN:
(纸本)9781665442084
In recent years, the availability of modern technology increased drastically with the raising availability of integrated devices and applications, such as mobile phones, voice assistant systems smart home appliances and many more. This paved the way for the semiconductor industry to become one of the fastest growing industries. Knowing, planning, and stabilizing the yield of semiconductor manufacturing is highly important to meet the rising demand. One indication for potential root causes is the identification of defect patterns on wafers. Wafers are the base silicon layer on which sets of chips are manufactured. If a certain process damages chips, then this produces characteristic patterns of failing chips on the wafer, which are then investigated by engineers to isolate the root-cause. According to studies, human-expert based defect pattern recognition methods have a maximum accuracy of about 45%. To help engineers to improve recognition and root cause analysis of defect patterns, this paper introduces a novel process for analysis. For this, unsupervised machine learning and clustering techniques are utilized to identify and group unknown defect patterns. A tailored process is introduced, using autoencoders and an iterative classification, which is tested on a use case with a pre-known root cause.
Due to the lacking standardisation of Digital Shadow (DS) of changeable assembly processes, these processes have not yet been fully integrated into the World Wide Lab (WWL). We propose generalising, standardising, and...
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We present golog++, a high-level agent programming and interfacing framework that offers a temporal constraint language to explicitly model layer-penetrating contingencies in low-level platform behavior. It can be use...
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Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the con...
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We introduce the Riskman ontology & shapes for representing and analysing information about risk management for medical devices. Risk management is concerned with taking necessary precautions so a medical device d...
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