domain-specific Natural Language processing (NLP) tasks need prepared, ground-truth data. However, practical constraints make this task challenging. domain engineers, who are the subject matter experts, are required t...
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domain-specific Natural Language processing (NLP) tasks need prepared, ground-truth data. However, practical constraints make this task challenging. domain engineers, who are the subject matter experts, are required t...
domain-specific Natural Language processing (NLP) tasks need prepared, ground-truth data. However, practical constraints make this task challenging. domain engineers, who are the subject matter experts, are required to create high-quality training data. However, their availability is limited because they are busy with theirday-to-day high-priority project and organizational tasks. Moreover, the data needed to be annotated to train NLP models may not be available at the outset itself. As the prepareddata grows, NLP models should take advantage of the new data and train improved machine learning models. Hence, in this paper, we present KIKO, a domain-specific continuous learning NLP framework that allows domain engineers to perform annotations on one document at a time, andretraining when a large enough collection of documents becomes available. KIKO is made of several tools that pipeline together to processdocuments from the industrial domain. The pipeline enables parsing, pattern-matching, annotation, training, relationship mining, and continuous learning basedretraining of the model by using a weaker form of active learning.
Industrial plants can contain thousands of devices, each with associateddocumentation. Using Industry 4.0 technologies, an abbdemonstrator shows how to manage the administrative overhead of making sure documentation...
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Skill-based engineering is gaining attention as a means to increase flexibility and changeability in engineering industrial automation systems. This paper proposes the Skill-based Engineering Model (SEM), which formal...
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Skill-based engineering is gaining attention as a means to increase flexibility and changeability in engineering industrial automation systems. This paper proposes the Skill-based Engineering Model (SEM), which formally describes the core entities that play a role in skill-based engineering. Accordingly, we propose a fourdimensional classification scheme for skills, and assess the suitability of a property model and OWL ontologies to describe and match skills. For each case, we identify a list of challenges that must be addressed to make skill-based engineering a reality in industrial automation systems. We believe that this paper can guide researchers to study various open aspects of skill-based engineering to make it feasible in complex industrial automation systems.
Abstract Gas source localization is the concept of locating the source of a chemical substance spreading in the environment. Within the oil, gas and petrochemical industry, there is an extreme focus on health, safety ...
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Abstract Gas source localization is the concept of locating the source of a chemical substance spreading in the environment. Within the oil, gas and petrochemical industry, there is an extreme focus on health, safety and environment (HSE) issues. Hence, being able to locate the source of a gas leakage in an accurate, reliable and quick manner, is of greatest interest to the industry. Although robotic gas source localization has been an active field of research for over fifteen years, large scale real world applications are still lacking. Towards closing this gap, this work presents the results of a comparative study between five different algorithms forrobotic gas source localization. The first three are taken from the literature, while the last two are novel modifications and combinations of them. In addition to describing these algorithms, this paper presents the details of the conducted comparative study and the the results thereof.
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