782 results on '"Song, Zhihuan"'
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202. Scheduling and control of failure prone systems with demand uncertainty and job overlaps
203. Multimode Process Monitoring: Part 1
204. Dynamic Process Monitoring
205. Multimode Process Monitoring: Part 2
206. Nonlinear Process Monitoring: Part 2
207. Introduction
208. Time-Varying Process Monitoring
209. Non-Gaussian Process Monitoring
210. Fault Reconstruction and Identification
211. Plant-Wide Process Monitoring: Multiblock Method
212. An Overview of Conventional MSPC Methods
213. Nonlinear Process Monitoring: Part 1
214. Probabilistic Process Monitoring
215. AKL NETWORKS FOR INDUSTRIAL ANALYZER MODELING AND FAULT DETECTION*
216. Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction
217. Generalized Semisupervised Self-Optimizing Kernel Model for Quality-Related Industrial Process Monitoring
218. A Novel Incremental Gaussian Mixture Regression and Its Application for Time-varying Multimodal Process Quality Prediction
219. Fast Locally Weighted PLS Modeling for Large-Scale Industrial Processes
220. Optimal Weighting Distance-Based Similarity for Locally Weighted PLS Modeling
221. Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model
222. Locally linear back-propagation based contribution for nonlinear process fault diagnosis
223. Bayesian Nonlinear Gaussian Mixture Regression and its Application to Virtual Sensing for Multimode Industrial Processes
224. Bayesian Just-in-Time Learning and Its Application to Industrial Soft Sensing
225. AKL Networks for Industrial Analyzer Modeling and Fault Detection**This work was sponsored by the National Natural Science Foundation of China (Projects No.20206028, 20576116) and Alexander von Humboldt-Stiftung (H. Q. Wang), and partially by the EU grant NeCST (S. X. Ding).
226. Adaptive Kernel Leaning Networks with Application to Nonlinear System Identification
227. Grid-Based Fuzzy Support Vector Data Description
228. Analysis on the vibration modes of the electric vehicle motor stator
229. Experimental research on the sound recognition of the electric vehicle motor.
230. Multirate Dynamic Process Monitoring Based on Multirate Linear Gaussian State-Space Model
231. Accelerated Kernel Canonical Correlation Analysis with Fault Relevance for Nonlinear Process Fault Isolation
232. Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression
233. Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data
234. Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model
235. Multiphase and Multimode Monitoring of Batch Processes Based on Density Peak Clustering and Just-in-time Learning
236. Back-propagation Based Contribution for nonlinear fault diagnosis
237. Multirate Factor Analysis Models for Fault Detection in Multirate Processes
238. Concurrent Fault Detection and Anomaly Location in Closed-Loop Dynamic Systems With Measured Disturbances
239. Data-Driven Predictive Model Based on Locally Weighted Bayesian Gaussian Regression
240. Run-to-run Trajectory Prediction of Uneven-length Batch Processes Using DTW-LSTM
241. Multi-grain Cascade Recurrent Neural Network for Nonlinear Time-varying Process Soft Sensor Modeling
242. Data-Driven Dynamic Modeling and Online Monitoring for Multiphase and Multimode Batch Processes with Uneven Batch Durations
243. Recursive Gaussian Mixture Models for Adaptive Process Monitoring
244. Analysis on the vibration modes of the electric vehicle motor stator
245. Performance Analysis of Dynamic PCA for Closed-Loop Process Monitoring and Its Improvement by Output Oversampling Scheme
246. Development of Self-Learning Kernel Regression Models for Virtual Sensors on Nonlinear Processes
247. Primary-Auxiliary Statistical Local Kernel Principal Component Analysis and Its Application to Incipient Fault Detection of Nonlinear Industrial Processes
248. Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes
249. Robust Supervised Probabilistic Factor Analysis and Its Application to Industrial Soft Sensor Modeling
250. Statistical process monitoring using improved PCA with optimized sensor locations
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