This paper proposes a new neural model of Generative Adversarial Networks (GANs) that is suitable for designing the intermediate shapes for die forging. Because the shape of heated metal gradually changes during the press, GANs should be aware of the timing and physical phenomena. In this respect, we proposed Physical Context and Timing aware sequence generating GANs (PCTGAN) that generates images in a sequence, considering the time sequence and physical quantities.
This new GAN is partly patented (U.S. Patent No: 10,970,601).
Our paper has been accepted for publication on Frontiers in Research Metrics and Analytics, 2021.
This paper proposes a new Knowledge Graph dataset that includes not only heterogeneous node relations but also textual descriptions. The dataset targets pharmaceutical areas but it can contribute to the Knowledge Graph research community by providing evidences in the usefulness of textural information integrated in a knowledge graph.
Masaki Asada, Gunasekaran Nallappan, Makoto Miwa and Yutaka Sasaki, Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information, Front. Res. Metr. Anal., doi: 10.3389/frma.2021.670206, 2021.
SC-CoMIcs, an MI information extraction corpus, is now ready to make it publicly available. The corpus is on our resource page. Licenses of the data are different between data types. See the “Resources” page from the upper right link on this page.