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DARPA Forward conference comes to CSU Aug 30-31
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Systems Engineering Professor Steve Simske elected to National Academy of Inventors - Walter Scott, Jr College of Engineering
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LJUBLJANA, Slovenia, April 23, 2021 /PRNewswire/
The Working Group on Supply Chain Data Interoperability and Semantic Web was established on the 1st of April, kicking off an inclusive collaboration to facilitate the use of globally recognized data standards (such as GS1 and W3C) and Semantic Web technologies. The initiative s overarching goal is to advance interoperability and positively impact sustainability in supply ecosystems by enabling trusted data exchanges beyond organizational, institutional and proprietary silos. An important part of the working group are the academics and research institutes, providing the normative backbone of the group s ambitions.
The first task of the working group is to complete a systematic literature review of peer-reviewed publications and document the current state of knowledge on data interoperability and Semantic Web in supply ecosystems. The research work will be complemented by facilitated discussions with other stakeholders in the w
Meet the academics and research institutes taking part in the Trace Alliance Data Interoperability and Semantic Web Working Group
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VISIMO Awarded Air Force STTR for Landscape Image Generation
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PITTSBURGH, March 2, 2021 /PRNewswire/ VISIMO has won an Air Force Phase I Small Business Technology Transfer (STTR) award to conduct research on the synthetic generation of landscape training data. Work will be conducted in partnership with Colorado State University (CSU), and will play a vital role in advancing technologies reliant on vast quantities of landscape imagery such as flight simulators, unmanned aerial vehicles (UAVs), and even video games.
The goal of the project, Generative Adversarial Network (GAN) for Synthetic Data Generation, is to use artificial intelligence (AI) and machine learning (ML) to create synthetic landscape images and 3D environments. The technology will be trained on real-world images, and then produce original images that look real. This unlimited supply of landscape data can be used to train ML models, and significantly reduc