Changqing Cheng, Ph.D.
Affiliations: | 2013 | Industrial Engineering & Management | Oklahoma State University, Stillwater, OK, United States |
Area:
General Engineering, Physical Chemistry, Chemical EngineeringGoogle:
"Changqing Cheng"Parents
Sign in to add mentorSatish Bukkapatnam | grad student | 2013 | Oklahoma State University | |
(Nonparametric localized Gaussian process models for accelerated meso-scale Monte Carlo simulation-based design and control of Carbon nanotube synthesis in chemical vapor deposition process.) |
BETA: Related publications
See more...
Publications
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
Shamsan A, Wu X, Liu P, et al. (2020) Intrinsic recurrence quantification analysis of nonlinear and nonstationary short-term time series. Chaos (Woodbury, N.Y.). 30: 093104 |
Cheng C. (2018) Multi-scale Gaussian process experts for dynamic evolution prediction of complex systems Expert Systems With Applications. 99: 25-31 |
Cheng C, Kan C, Yang H. (2016) Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events. Computers in Biology and Medicine. 75: 10-18 |
Cheng C, Sa-Ngasoongsong A, Beyca O, et al. (2015) Time series forecasting for nonlinear and non-stationary processes: a review and comparative study Iie Transactions. 47: 1053-1071 |
Cheng C, Wang Z, Hung W, et al. (2015) Ultra-precision Machining Process Dynamics and Surface Quality Monitoring Procedia Manufacturing. 1: 607-618 |
Le TQ, Cheng C, Sangasoongsong A, et al. (2013) Wireless Wearable Multisensory Suite and Real-Time Prediction of Obstructive Sleep Apnea Episodes. Ieee Journal of Translational Engineering in Health and Medicine. 1: 2700109 |
Cheng C, Bukkapatnam STS, Raff LM, et al. (2012) Monte Carlo simulation of carbon nanotube nucleation and growth using nonlinear dynamic predictions Chemical Physics Letters. 530: 81-85 |
Bukkapatnam ST, Cheng C. (2010) Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 82: 056206 |