![]() ![]() Furthermore, the benefits of collaborating with a virtual cobot most vividly manifested when the user had to position the robotic arm with higher precision. In the face of a similar self-reported mental demand when maneuvering the virtual or physical cobot, operators showed shorter operation times and lower implicit workload when interacting with the virtual cobot compared to its physical counterpart. Performance and implicit and explicit workload were assessed as a function of pupil size variation and self-reporting questionnaires. Mental workload was measured in participants working in synergistic co-operation with a physical and a virtual collaborative robot (cobot) under different levels of task demands. In the present work, we aimed to fill this gap by conducting a systematic assessment of a human–robot collaborative framework from a user-centric perspective. ![]() Interest in the virtualization of human–robot interactions is increasing, yet the impact that collaborating with either virtual or physical robots has on the human operator’s mental state is still insufficiently studied. Additionally, its application to publicly available benchmark data sets shows, that these results are transferable to other domains. Experimental validation of the algorithm on real-world manufacturing data shows, that the recall for the retrieval of fault patterns is considerably higher than that of other state-of-the-art adaptive search algorithms. No mans sky fleet warping series#As the search progresses, the algorithm constructs a library of time series patterns that are used to accurately find objects of the target class. Additionally, we propose a mechanism that allows the algorithm to self-adapt to new patterns without requiring any user input. In this paper, we present a novel adaptive search algorithm that refines the query based on relevance feedback provided by the user. Indexing manufacturing databases to speed up the exploratory search is often not feasible as it may result in an unacceptable reduction in recall. This is a well-documented phenomenon in information retrieval and not unique to the manufacturing domain. In practice, the search often amounts to an iterative query–response cycle in which users define new queries (time series patterns) based on results of previous queries. This allows domain experts to identify parts that exhibit specific process faults. In the manufacturing domain, these systems are used to query large databases of manufacturing process data that contain terabytes of time series data from millions of parts. ![]() Improving the recall of information retrieval systems for similarity search in time series databases is of great practical importance. ![]()
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