Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
An Accurate And Computationally Efficient Method For Battery Capacity Fade Modeling, D. M. Ajiboye, Jonathan W. Kimball, R.(Robert) G. Landers, John (T.) Park
An Accurate And Computationally Efficient Method For Battery Capacity Fade Modeling, D. M. Ajiboye, Jonathan W. Kimball, R.(Robert) G. Landers, John (T.) Park
Electrical and Computer Engineering Faculty Research & Creative Works
The Industry Demand for Accurate and Fast Algorithms that Model Vital Battery Parameters, E.g., State-Of-Health, State-Of-Charge, Pulse-Power Capability, is Substantial. One of the Most Critical Models is Battery Capacity Fade. the Key Challenge with Physics-Based Battery Capacity Fade Modeling is the High Numerical Cost in Solving Complex Models. in This Study, an Efficient and Fast Model is Presented to Capture Capacity Fade in Lithium-Ion Batteries. Here, the High-Order Chebyshev Spectral Method is Employed to Address the Associated Complexity with Physics-Based Capacity Fade Models. its Many Advantages, Such as Low Computational Memory, High Accuracy, Exponential Convergence, and Ease of Implementation, Allow …
Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker
Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker
Electrical and Computer Engineering Faculty Research & Creative Works
Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes …