Výsledky vyhledávání - "Brian Gallagher"

Akademický článek

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

Autor: Shusen Liu, Brandon Bocklund, James Diffenderfer, Shreya Chaganti, Bhavya Kailkhura, Scott K. McCall, Brian Gallagher, Aurélien Perron, Joseph T. McKeown

Publikováno v: npj Computational Materials, Vol 10, Iss 1, Pp 1-14 (2024)

Abstract Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions of composition and temperature, is essential for understanding alloy properties and screening desirable materials. Traditional methods like CALPHA

Externí odkaz: https://doaj.org/article/1f8e4ed3c60146f39a683328fd705a16

Zobrazit plný text záznamu

Akademický článek

Explainable machine learning in materials science

Autor: Xiaoting Zhong, Brian Gallagher, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, T. Yong-Jin Han

Publikováno v: npj Computational Materials, Vol 8, Iss 1, Pp 1-19 (2022)

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artifi

Externí odkaz: https://doaj.org/article/599f9fa1c28a451e9cc5dc0278c44c89

Zobrazit plný text záznamu

Akademický článek

A study of real-world micrograph data quality and machine learning model robustness

Autor: Xiaoting Zhong, Brian Gallagher, Keenan Eves, Emily Robertson, T. Nathan Mundhenk, T. Yong-Jin Han

Publikováno v: npj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collect

Externí odkaz: https://doaj.org/article/15813301f7d54bfcb75f7018545b366d

Zobrazit plný text záznamu

Akademický článek

Device Classification for Industrial Control Systems Using Predicted Traffic Features

Autor: Indrasis Chakraborty, Brian M. Kelley, Brian Gallagher

Publikováno v: Frontiers in Computer Science, Vol 4 (2022)

To achieve a secure interconnected Industrial Control System (ICS) architecture, security practitioners depend on accurate identification of network host behavior. However, accurate machine learning based host identification methods depends on the av

Externí odkaz: https://doaj.org/article/ffd23b1203ae4030bd76bea9be85160a

Zobrazit plný text záznamu

Akademický článek

Industrial control system device classification using network traffic features and neural network embeddings

Autor: Indrasis Chakraborty, Brian M. Kelley, Brian Gallagher

Publikováno v: Array, Vol 12, Iss , Pp 100081- (2021)

Characterization of modern cyber–physical Industrial Control System (ICS) devices is critical to the evaluation of their security posture and an understanding of the underlying industrial processes with which they interact. In this work, we address

Externí odkaz: https://doaj.org/article/2d99a56d645240f78516966dbba39b6b

Zobrazit plný text záznamu

Akademický článek

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

Autor: Brian Gallagher, Matthew Rever, Donald Loveland, T. Nathan Mundhenk, Brock Beauchamp, Emily Robertson, Golam G. Jaman, Anna M. Hiszpanski, T. Yong-Jin Han

Publikováno v: Materials & Design, Vol 190, Iss , Pp - (2020)

We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based o

Externí odkaz: https://doaj.org/article/b0c0c231d88d41bd920defda87945393

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání