AHCI RESEARCH GROUP

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OUR RESEARCH

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2024

Shrestha, A.; Imamoto, K.

Generative AI based industrial metaverse creation methodology Proceedings Article

In: Proc. - Artif. Intell. Bus., AIxB, pp. 53–57, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835039103-9 (ISBN).

Abstract | Links | BibTeX | Tags: Generative adversarial networks, Generative AI, Industrial metaverse, Industrial railroads, Investments, Maintenance and operation, Metaverses, Natural languages, Railroad transportation, Railway, Railway maintenance, Railway operations, Simple++, simulation

@inproceedings{shrestha_generative_2024,

title = {Generative AI based industrial metaverse creation methodology},

author = {A. Shrestha and K. Imamoto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215066217&doi=10.1109%2fAIxB62249.2024.00017&partnerID=40&md5=d6d11729f16ccaa9f69fd5452befe492},

doi = {10.1109/AIxB62249.2024.00017},

isbn = {979-835039103-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - Artif. Intell. Bus., AIxB},

pages = {53–57},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The metaverse has been proposed as a suitable apparatus for the dissemination of information in a railway maintenance and operation context. However, the generation of such a metaverse environment requires significant investment with the creation of simple prototypes taking an extended duration. Although there are generative artificial intelligencebased methods to create small scenes, there is an absence of a method to do so for industrial applications. We devised a platform to create railway environments with the assistance of the language models for code creation and semantic inference without the need for reprogramming or editing of the project source meaning environments could be generated by the end users. With a natural language input and a coding paradigm output the code generation module is shown together with the example environments from real-life railway lines in Tokyo, Japan as preliminary results. By creating such environments leveraging the rapid generation with the help of generative artificial intelligence, we show generative artificial intelligence can be used to automate the task of the programmer to create new environments on demand from the user in natural language. © 2024 IEEE.},

keywords = {Generative adversarial networks, Generative AI, Industrial metaverse, Industrial railroads, Investments, Maintenance and operation, Metaverses, Natural languages, Railroad transportation, Railway, Railway maintenance, Railway operations, Simple++, simulation},

pubstate = {published},

tppubtype = {inproceedings}

}