Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Srinivas, Sakhinana Sagar"'
Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge,
Externí odkaz:
http://arxiv.org/abs/2412.05937
Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property rights, play a critical role in technological innovation. The growing
Externí odkaz:
http://arxiv.org/abs/2409.19006
Autor:
Srinivas, Sakhinana Sagar, Sannidhi, Geethan, Gangasani, Sreeja, Ravuru, Chidaksh, Runkana, Venkataramana
Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we introduce a fully automated, end-to-end pipeline that leverages recent ad
Externí odkaz:
http://arxiv.org/abs/2409.12244
We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pre-trained multimodal
Externí odkaz:
http://arxiv.org/abs/2409.07463
Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device technology portfolio and aid in high-end device fabrication. In this paper,
Externí odkaz:
http://arxiv.org/abs/2408.15305
In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design and drug screening. However, existing graph deep learning methods face
Externí odkaz:
http://arxiv.org/abs/2408.14964
Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by their ability to handle large, complex datasets. To overcome this lim
Externí odkaz:
http://arxiv.org/abs/2408.14387
Characterizing materials with electron micrographs is a crucial task in fields such as semiconductors and quantum materials. The complex hierarchical structure of micrographs often poses challenges for traditional classification methods. In this stud
Externí odkaz:
http://arxiv.org/abs/2408.13661
Autor:
Srinivas, Sakhinana Sagar, Sannidhi, Geethan, Gangasani, Sreeja, Ravuru, Chidaksh, Runkana, Venkataramana
Characterizing materials using electron micrographs is crucial in areas such as semiconductors and quantum materials. Traditional classification methods falter due to the intricatestructures of these micrographs. This study introduces an innovative a
Externí odkaz:
http://arxiv.org/abs/2408.13621
We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves
Externí odkaz:
http://arxiv.org/abs/2408.14494