*.csv (zip); *.json (zip); *.pickle (zip); *.npz (zip);
Soft robots are a promising area of research due to their potential use in various applications. Learning the kinematics of soft robots is crucial for their advancement and application. This dataset is designed to provide training data for the development of machine learning models that can learn the kinematics of soft robots with different actuation types. The dataset includes the positional data of three soft robots, specifically the simulated pneumatic soft robot, simulated tendon-driven soft robot, and real-world tendon-driven soft robot.
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42 Views
ATTENTION: THIS DATASET DOES NOT HOST ANY SOURCE VIDEOS. WE PROVIDE ONLY HIDDEN FEATURES GENERATED BY PRE-TRAINED DEEP MODELS AS DATA
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6310 Views
This dataset contains world news related to Covid-19 and vaccine and also with the news article's available metadata.
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701 Views
Track and monitor Covid-19 related news from the world
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519 Views
We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals.
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764 Views
This dataset has been created from a collection of 56403 multidisciplinary book titles from Springer, available through the Hellenic Academic Libraries Link (https://www.heal-link.gr/en/home-2/) subscription. To obtain this dataset, a parser was created for extracting relevant information, such as the title, subtitle and ToC, from each book. The extracted information was stored in a database for further processing. Each book title in the database includes information regarding the bookid, title, and ToC.
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1508 Views
The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced.
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5773 Views