Works (3)

Updated: August 7th, 2023 21:15

2021 journal article

Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis


By: J. Zhang n, Y. Zeng* & B. Starly n

author keywords: Recurrent neural networks; Long term temporal dependencies; Long short term memory; Tool wear; Remaining useful life
TL;DR: A recent deep learning method, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process is proposed. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: April 19, 2021

2017 journal article

Electrical Cell-Substrate Impedance Spectroscopy Can Monitor Age-Grouped Human Adipose Stem Cell Variability During Osteogenic Differentiation


By: R. Nordberg n, J. Zhang n, E. Griffith n, M. Frank n, B. Starly n & E. Loboa n

author keywords: Electrical cell-substrate impedance spectroscopy; Adipose stem cells; Osteogenesis; Bioimpedance
MeSH headings : Adipose Tissue / cytology; Adult; Age Factors; Aged; Aged, 80 and over; Bone and Bones / cytology; Cell Differentiation; Cell Proliferation; Cells, Cultured; Dielectric Spectroscopy / methods; Electric Impedance; Humans; Middle Aged; Osteogenesis; Phenotype; Stem Cells / metabolism; Stem Cells / physiology; Time Factors; Young Adult
TL;DR: This is the first study to use ECIS to predict osteogenic potential of multiple hASC populations and to show that donor age may temporally control onset of osteogenesis, which could be critical for development of patient‐specific bone tissue engineering and regenerative medicine therapies. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2017 journal article

Particle learning in online tool wear diagnosis and prognosis


By: J. Zhang n, B. Starly n, Y. Cai n, P. Cohen n & Y. Lee n

author keywords: Particle learning; Tool wear; Intelligent manufacturing; Remaining useful life (RUL)
TL;DR: A probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process to avoid developing a complex closed form formulation for a specific tool wear model. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

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