IIOT for legacy and intelligent factory machines with AR and LLM feedback with a Digital Twin demonstration of real-time IOT for architecture/building applications using Omniverse.
PI: Prof. Sam Anand (Director of Smart-Manufacturing Lab, Dept. of Mechanical Engineering, CEAS)
co-PI: Prof. Ming Tang (Director of XR-Lab, School of Architecture & Interior Design, DAAP)
$40,000. UC Industry 4.0/5.0 Institute Consortium Research Project: 01.2024-01.2025
Primary Objective: To develop a conversational large language modeling system that acquires data from legacy machines, digital machines, environmental data, real-time data, and historical data within an IIoT environment to create a digital twin for assisting in real-time maintenance and assistance (Application Use Case: Digital Future’s Building)
This paper presents a ” SpaceXR ” project that integrates data science, astronomy, and Virtual Reality (VR) technology to deliver an immersive and interactive educational tool. It is designed to cater to a diverse audience, including students, academics, space enthusiasts, and professionals, offering an easily accessible platform through VR headsets. This VR application offers a data-driven representation of celestial bodies, including planets and the sun within our solar system, guided by data from the NASA and Gaia databases. The VR application empowers users with interactive capabilities encompassing scaling, time manipulation, and object highlighting. The potential applications span from elementary educational contexts, such as teaching the star system in astronomy courses, to advanced astronomical research scenarios, like analyzing spectral data of celestial objects identified by Gaia and NASA. By adhering to emerging software development practices and employing a variety of conceptual frameworks, this project yields a fully immersive, precise, and user-friendly 3D VR application that relies on a real, publicly available database to map celestial objects.
Objective To examine the impact of an investigative checklist on evidence collection by police officers responding to a routine burglary investigation.
Methods A randomized control trial was conducted in virtual reality to test the effectiveness of an investigative checklist. Officers in the randomly assigned treatment group (n = 25) were provided with a checklist during the simulated investigation. Officers in the control group (n = 26) did not have access to the checklist at any time. The checklist included five evidence items commonly associated with burglary investigations.
Results Officers who were randomly provided with an investigative checklist were significantly more likely to collect two evidence items located outside of the virtual victim’s home. Both treatment and control officers were about equally as likely to collect three evidence items located inside the residence.
Conclusions Investigative checklists represent a promising new tool officers can use to improve evidence collection during routine investigations. More research is needed, however, to determine whether checklists improve evidence collection or case clearances in real-life settings. Virtual reality simulations provide a promising tool for collecting data in otherwise difficult or complex situations to simulate