17007 research outputs found
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Porter Library
No full textBlack and white photo of Porter Library as seen from the Ovalhttps://digitalcommons.pittstate.edu/porterbuilding/1183/thumbnail.jp
Willard Plaza - Concept Art
No full textColor painting of the Lindsburg Plaza design concept. The square brickwork, pillars connected by vines, and the trees are all present in the concept. The Cadence sculpture and fountain by Jon Haverene is not.https://digitalcommons.pittstate.edu/willardbuilding/1100/thumbnail.jp
Willard Hall - c. 1930s
No full textBlack and white photo of Willard Hall, built in 1923. Based on the car parked in front of the building, this photo was taken around the mid-1930s.https://digitalcommons.pittstate.edu/willardbuilding/1107/thumbnail.jp
Willard Plaza - Completed
No full textColor photo of Willard Plaza post-renovation. A student walks along the sidewalk.https://digitalcommons.pittstate.edu/willardbuilding/1108/thumbnail.jp
Preserving the Printing Plates of the Haldeman-Julius Publishing Company
No full textA video documenting the conservation process of the printing plates for the publications by the Haldeman-Julius Company of Girard, Kansas. Angel Abshire, student employee at Pittsburg State University\u27s Special Collections, walks through the assessing, cleaning, cataloging, and storing of the plates
Using the artificial intelligence technique of logic tensor networks to predict aurora borealis visibility
Full text linkBuilding upon previous research, an AI technique called logic tensor networks is used to predict where to view the aurora borealis. This technique uses a logic-based neural network to create these predictions. The model outputs probabilities of sightings. Classification, a machine learning technique used to sort data into categories, will be used to compare with the logic tensor networks. Work is ongoing to gather and format data collected by satellite and from the Aurorasaurus website to use for training our model. The Aurorasaurus website collects reports from people around the world and stores data such as the date, time, geographical coordinates, and the duration of the sighting. This site also uses a model that predicts viewing locations, called Ovation Prime. Since the Ovation Prime model gives the probability of sighting the aurora overhead, view lines are used to adjust the probabilities of Ovation Prime to compensate that the aurora may be sighted closer to the horizon. The Ovation Prime model does not accurately predict where the aurora is visible. Thus, logic tensor networks will be used to combine the Ovation Prime model with the reports of sighting to increase the accuracy of the Aurorasaurus predictions.
This research is a continuation of that funded by the NASA Rapid Response Research Grant Appendix F: A Neural-Symbolic Aurora Model Driven by Aurorasaurus Data in Citizen Science and the Kansas National Space Grant College and Fellowship Program – Opportunities in NASA STEM FY 2020-2024. It is currently supported by the NSF ASTER-LSAMP grant at PSU.https://digitalcommons.pittstate.edu/ai-posters-2025/1005/thumbnail.jp
AI in the Workforce Panel
No full textPanelists Santiago Morel Berni, Scott Parish, Magdalene Moy, and moderator Andra Stefanoni on stage. Santiago answers a question.https://digitalcommons.pittstate.edu/aisymp-photos-2025/1028/thumbnail.jp
AI Symposium Q&A
No full textLocal business owner asks a question at the podium during the Q&A session.https://digitalcommons.pittstate.edu/aisymp-photos-2025/1034/thumbnail.jp
Poster Break
No full textJhonatan Granadeno at his poster.https://digitalcommons.pittstate.edu/aisymp-photos-2025/1026/thumbnail.jp
AI in the Workforce Panel
No full textAI in Workforce panelists, Santiago Morel, Berni, Scott Parish, Magdalane Moy, and moderator Andra Stefanoni seated at the table. Magdalene answers a question. On either side of the table, survey results for AI usage are projected on the screens.https://digitalcommons.pittstate.edu/aisymp-photos-2025/1032/thumbnail.jp