All Stories

  1. Big data-driven predictive control for nonlinear systems based on kernel density estimation of data trajectories
  2. Machine Learning-Based Time-Series Forecasting for Off-Gas Characterization and Decarbonization in Electric Arc Furnace Steelmaking
  3. Reinforcement learning-based autonomous control of bench-scale primary separation vessel
  4. Causal discovery in industrial systems via physics-guided variational attention and probabilistic interventions
  5. Robust soft sensing with causal and injectivity-preserving Graph Neural Network
  6. State Estimation for High-Dimensional Wastewater Treatment Plants Based on Dynamic Mode Decomposition
  7. Recursive State Estimation with Non-Negativity Constraints using Constrained Extended Kalman and Particle Filters with Truncated Distributions and Positivity-Preserving ODE Solvers
  8. Uncertainty Predictive Observer-Based Model-Free Adaptive Disturbance Rejection Control
  9. Data-Driven Closed-Loop System Fault Diagnosis Using Feedback-Invariant Dynamic Residual Analysis
  10. StictionGPT: Detecting valve stiction in process control loops using large vision language model
  11. Breaking Information Granularity Heterogeneity: A Mutual Information-Inspired Causal Discovery Framework for Multi-Rate Time Series
  12. Operator-In-The-Loop Bayesian Optimization Toward Optimal Process Operation
  13. Real-time freeze point prediction using multirate measurements in the blending process
  14. A Novel Toeplitz Matrix and CNN-LSTM Based Method for Identifying Control Valve Stiction
  15. Physics-guided transfer learning for Bayesian optimization of chemical port-Hamiltonian systems
  16. M2D-VAE: Self-Supervised Probabilistic Temporal–Spatial Latent Representation Learning for Unsupervised Industrial Operational Applications Under Missing Value Interference
  17. Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis
  18. Robust to outlier image inpainting for interface detection in primary separation vessel
  19. A Variational Bayesian Inference-Based Robust Dissimilarity Analytics Model for Industrial Fault Detection
  20. Interpretable Dynamic Modelling and Prediction of Free Acid in Zinc Leaching Process
  21. Big Data-Driven Control of Nonlinear Processes Through Dynamic Latent Variables Using an Autoencoder
  22. A Robust Probabilistic Quality-Relevant Monitoring Model With Laplace Distribution
  23. Addressing Heterogeneous Time-Frequency Causality: Source Consistency Exploring for Industrial Root Cause Alignment and Diagnosis
  24. Factor Graph Optimization for Flexibly Modeled INS/GPS Navigation in Graphical State-Space
  25. From Static and Dynamic Perspectives: A Survey on Historical Data Benchmarks of Control Performance Monitoring
  26. Causality-Informed Data-Driven Predictive Control
  27. Compensatory Data-Driven Networked Iterative Learning Control With Communication Constraints and DoS Attacks
  28. Data-Driven Iterative Learning Temperature Control for Rubber Mixing Processes
  29. EKG-AC: A New Paradigm for Process Industrial Optimization Based on Offline Reinforcement Learning With Expert Knowledge Guidance
  30. Fault-Tolerant Soft Sensor Modeling Based on a Two-Dimensional Group Distributionally Robust Optimization Framework
  31. Incremental Learning-Enabled Fault Diagnosis of Dynamic Systems: A Comprehensive Review
  32. Laplace Distribution Based Robust Identification of Errors-in-Variables Systems With Outliers
  33. Sequential Image Restoration and Segmentation for Interface Detection in Primary Separation Cells
  34. Event-Triggered Direct Data-Driven Iterative Learning Control for Multiagent Systems
  35. A Conditional Invertible Neural Network-Based Fault Detection
  36. Spatiotemporal Topology-Informed Multiagent Reinforcement Learning Framework for Structured Multiprocess Collaborative Optimization
  37. Intrinsic Causality Embedded Concurrent Quality and Process Monitoring Strategy
  38. Fast Bayesian filtering for wastewater treatment plants with inaccurate process noise statistics
  39. Bayesian-Based Causal Structure Inference With a Domain Knowledge Prior for Stable and Interpretable Soft Sensing
  40. Double-Layered Iterative Learning Control for Nonlinear Systems
  41. Data-Driven Dynamic Internal Model Control
  42. Data-Driven Finite-Iteration Learning Control
  43. Sampled-Data Model-Free Adaptive Control for Nonlinear Continuous-Time Systems
  44. Nonlinear Slow Feature Analysis for Oscillating Characteristics Under Deep Encoder-Decoder Framework
  45. Unified Unit-Wise and Plantwide Monitoring: Application in Early Detection of Gas Flare Event
  46. Detection of poor controller tuning with Gramian Angular Field (GAF) and StackAutoencoder (SAE)
  47. Digital twin and control of an industrial-scale bitumen extraction process
  48. Sparse Robust Dynamic Feature Extraction Using Bayesian Inference
  49. Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation
  50. Distributed Data-Driven Predictive Control via Dissipative Behavior Synthesis
  51. An Unsupervised Fault Detection and Diagnosis With Distribution Dissimilarity and Lasso Penalty
  52. Machine learning for industrial sensing and control: A survey and practical perspective
  53. On Approximation of System Behavior From Large Noisy Data Using Statistical Properties of Measurement Noise
  54. Robust-to-occlusion machine vision model for predicting quality variables with slow-rate measurements
  55. A Novel CVAE-Based Sequential Monte Carlo Framework for Dynamic Soft Sensor Applications
  56. Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives
  57. Shape-Based Pattern Recognition Approaches toward Oscillation Detection
  58. Physics‐informed sparse causal inference for source detection of plant‐wide oscillations
  59. Reinforcement Learning in Process Industries: Review and Perspective
  60. A Novel Chattering-Free Discrete Sliding Mode Controller With Disturbance Compensation for Zinc Roasting Temperature Distribution Control
  61. A Probabilistic Quality-Relevant Monitoring Method With Gaussian Mixture Model
  62. An Approach to Data-Based Linear Quadratic Optimal Control
  63. Data-Driven Internal Model Learning Control for Nonlinear Systems
  64. Data-Driven Robust Finite-Iteration Learning Control for MIMO Nonrepetitive Uncertain Systems
  65. Explainable Fault Diagnosis Using Invertible Neural Networks—Part I: A Left Manifold-Based Solution
  66. A Robust Dissimilarity Distribution Analytics With Laplace Distribution for Incipient Fault Detection
  67. Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
  68. Image restoration and analysis with application to quality variable prediction in flotation process
  69. Dynamic Linearization and Extended State Observer-Based Data-Driven Adaptive Control
  70. Fault-Tolerant Soft Sensors for Dynamic Systems
  71. Hammerstein–Wiener Model Identification for Oil-in-Water Separation Dynamics in a De-Oiling Hydrocyclone System
  72. Fault Detection for Nonlinear Dynamic Systems With Consideration of Modeling Errors: A Data-Driven Approach
  73. Data-Driven Virtual Reference Set-Point Learning of PD Control and Applications to Permanent Magnet Linear Motors
  74. Double Dynamic Linearization-Based Higher Order Indirect Adaptive Iterative Learning Control
  75. Generalized Robust MPC with zone-tracking
  76. Variational Bayesian Inference for Robust Identification of PWARX Systems With Time-Varying Time-Delays
  77. Reinforcement learning for soft sensor design through autonomous cross-domain data selection
  78. Statistical Test-Based Practical Methods for Detection and Quantification of Stiction in Control Valves
  79. Enhanced P-Type Control: Indirect Adaptive Learning From Set-Point Updates
  80. A Transferable Multistage Model With Cycling Discrepancy Learning for Lithium-Ion Battery State of Health Estimation
  81. Process Monitoring Using Domain-Adversarial Probabilistic Principal Component Analysis: A Transfer Learning Framework
  82. Deep Bayesian Slow Feature Extraction With Application to Industrial Inferential Modeling
  83. No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models
  84. Tuning-Free Bayesian Estimation Algorithms for Faulty Sensor Signals in State-Space
  85. ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling
  86. Data-Driven Indirect Iterative Learning Control
  87. Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes
  88. Identification of Errors-in-Variable System With Heteroscedastic Noise and Partially Known Input Using Variational Bayesian
  89. Skew Filtering for Online State Estimation and Control
  90. Variational Bayesian Approach to Nonstationary and Oscillatory Slow Feature Analysis With Applications in Soft Sensing and Process Monitoring
  91. A Deep Probabilistic Transfer Learning Framework for Soft Sensor Modeling With Missing Data
  92. Variational Progressive-Transfer Network for Soft Sensing of Multirate Industrial Processes
  93. Transfer Learning for Dynamic Feature Extraction Using Variational Bayesian Inference
  94. Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning
  95. Event-Triggered Distributed Moving Horizon State Estimation of Linear Systems
  96. A Single-Side Neural Network-Aided Canonical Correlation Analysis With Applications to Fault Diagnosis
  97. Event-Triggered ILC for Optimal Consensus at Specified Data Points of Heterogeneous Networked Agents With Switching Topologies
  98. Multisource-Refined Transfer Network for Industrial Fault Diagnosis Under Domain and Category Inconsistencies
  99. Data-Driven Adaptive Consensus Learning From Network Topologies
  100. Discrete-Time-Distributed Adaptive ILC With Nonrepetitive Uncertainties and Applications to Building HVAC Systems
  101. MoniNet With Concurrent Analytics of Temporal and Spatial Information for Fault Detection in Industrial Processes
  102. Overexpression of heat shock protein 70 induces apoptosis of intestinal epithelial cells in heat-stressed pigs: A proteomics approach
  103. Parallel Interaction Spatiotemporal Constrained Variational Autoencoder for Soft Sensor Modeling
  104. Community detection based process decomposition and distributed monitoring for large‐scale processes
  105. Data-Driven Communication Efficient Distributed Monitoring for Multiunit Industrial Plant-Wide Processes
  106. Offline and Online Parameter Learning for Switching Multirate Processes With Varying Delays and Integrated Measurements
  107. Reinforcement Learning With Constrained Uncertain Reward Function Through Particle Filtering
  108. Quantitative Data-Driven Adaptive Iterative Learning Control: From Trajectory Tracking to Point-to-Point Tracking
  109. Reinforcement learning approach to autonomous PID tuning
  110. Sparse Inverse Covariance Estimation for Causal Inference in Process Data Analytics
  111. Robust probabilistic principal component regression with switching mixture Gaussian noise for soft sensing
  112. Sensor Fault Estimation in a Probabilistic Framework for Industrial Processes and its Applications
  113. Spatial Linear Dynamic Relationship of Strongly Connected Multiagent Systems and Adaptive Learning Control for Different Formations
  114. Data-Driven Adaptive Iterative Learning Bipartite Consensus for Heterogeneous Nonlinear Cooperation-Antagonism Networks
  115. Distributed Process Monitoring for Multi-Agent Systems Through Cognitive Learning
  116. Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning
  117. Incremental Variational Bayesian Gaussian Mixture Model With Decremental Optimization for Distribution Accommodation and Fine-Scale Adaptive Process Monitoring
  118. Multirate Sensor Fusion in the Presence of Irregular Measurements and Time-Varying Time Delays Using Synchronized, Neural, Extended Kalman Filters
  119. Robust Variational Bayesian-Based Soft Sensor Model for LPV Processes With Delayed and Integrated Output Measurements
  120. Sparse and Time-Varying Predictive Relation Extraction for Root Cause Quantification of Nonstationary Process Faults
  121. Practical Linear Regression-Based Method for Detection and Quantification of Stiction in Control Valves
  122. A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes
  123. Active Disturbance Rejection Control for Nonaffined Globally Lipschitz Nonlinear Discrete-Time Systems
  124. Adversarial smoothing tri-regression for robust semi-supervised industrial soft sensor
  125. Latent variable modeling and state estimation of non-stationary processes driven by monotonic trends
  126. Observer-Based Sampled-Data Model-Free Adaptive Control for Continuous-Time Nonlinear Nonaffine Systems With Input Rate Constraints
  127. Event-Triggered Nonlinear Iterative Learning Control
  128. State Estimation for Multirate Measurements in the Presence of Integral Term and Variable Delay
  129. A Holistic Probabilistic Framework for Monitoring Nonstationary Dynamic Industrial Processes
  130. Online Probabilistic Estimation of Sensor Faulty Signal in Industrial Processes and Its Applications
  131. Siamese Neural Network-Based Supervised Slow Feature Extraction for Soft Sensor Application
  132. Data-driven multi-model minimum variance controller design based on support vectors
  133. Identification of Two-Dimensional Causal Systems With Missing Output Data via Expectation–Maximization Algorithm
  134. Online reinforcement learning for a continuous space system with experimental validation
  135. Auxiliary Predictive Compensation-Based ILC for Variable Pass Lengths
  136. Convergence Analysis of Sampled-Data ILC for Locally Lipschitz Continuous Nonlinear Nonaffine Systems With Nonrepetitive Uncertainties
  137. Mixture robust semi-supervised probabilistic principal component regression with missing input data
  138. Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis
  139. Event-Triggered Model-Free Adaptive Control
  140. Parameter estimation for nonlinear systems with multirate measurements and random delays
  141. A Variational Bayesian Causal Analysis Approach for Time-Varying Systems
  142. Soft sensor based on eXtreme gradient boosting and bidirectional converted gates long short-term memory self-attention network
  143. Extended State Observer-Based Data-Driven Iterative Learning Control for Permanent Magnet Linear Motor With Initial Shifts and Disturbances
  144. Multimodal process monitoring based on variational Bayesian PCA and Kullback-Leibler divergence between mixture models
  145. Valve Stiction Detection and Quantification Using a K-Means Clustering Based Moving Window Approach
  146. Forward–Backward Smoothers With Finite Impulse Response Structure
  147. Hidden Markov Model-Based Attack Detection for Networked Control Systems Subject to Random Packet Dropouts
  148. Stationary Subspace Analysis-Based Hierarchical Model for Batch Processes Monitoring
  149. Data-Driven Fault Detection for Dynamic Systems With Performance Degradation: A Unified Transfer Learning Framework
  150. Dual Neural Extended Kalman Filtering Approach for Multirate Sensor Data Fusion
  151. Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions
  152. Consensus‐based approach for parameter and state estimation of agro‐hydrological systems
  153. Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control
  154. Discrete-Time Extended State Observer-Based Model-Free Adaptive Control Via Local Dynamic Linearization
  155. Data-Driven Modeling Based on Two-Stream ${\rm{\lambda }}$ Gated Recurrent Unit Network With Soft Sensor Application
  156. Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
  157. Real-Time Mode Diagnosis for Processes With Multiple Operating Conditions Using Switching Conditional Random Fields
  158. Gaussian process regression with heteroscedastic noises — A machine-learning predictive variance approach
  159. Distributed data‐driven observer for linear time invariant systems
  160. Supervised Variational Autoencoders for Soft Sensor Modeling With Missing Data
  161. Detecting the Direction of Information Flow in Instantaneous Relations Between Variables
  162. Iterative Identification of Hammerstein Parameter Varying Systems With Parameter Uncertainties Based on the Variational Bayesian Approach
  163. 3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks
  164. Probabilistic just-in-time approach for nonlinear modeling with Bayesian nonlinear feature extraction
  165. Distributed control performance assessment and corresponding optimal controller design considering communication delays
  166. Neighborhood Variational Bayesian Multivariate Analysis for Distributed Process Monitoring With Missing Data
  167. Feature Extraction of Constrained Dynamic Latent Variables
  168. Simultaneous Static and Dynamic Analysis for Fine-Scale Identification of Process Operation Statuses
  169. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes
  170. Variational Bayesian Approach for Causality and Contemporaneous Correlation Features Inference in Industrial Process Data
  171. A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic chemical process
  172. Robust filter design for asymmetric measurement noise using variational Bayesian inference
  173. Parameter estimation of Markov-switching Hammerstein systems using variational Bayesian approach
  174. An Improved Data-Driven Point-to-Point ILC Using Additional On-Line Control Inputs With Experimental Verification
  175. Data rectification for multiple operating modes: A MAP framework
  176. Multiple-Model State Estimation Based on Variational Bayesian Inference
  177. Mixtures of Probabilistic PCA With Common Structure Latent Bases for Process Monitoring
  178. Probabilistic Monitoring of Sensors in State-Space With Variational Bayesian Inference
  179. Hierarchically Distributed Monitoring for the Early Prediction of Gas Flare Events
  180. Distributed multiple step ahead prediction considering communication delays
  181. Robust FIR State Estimation of Dynamic Processes Corrupted by Outliers
  182. Deep Discriminative Representation Learning for Nonlinear Process Fault Detection
  183. Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach
  184. Computationally Efficient Data-Driven Higher Order Optimal Iterative Learning Control
  185. Multivariate Gaussian process regression for nonlinear modelling with colored noise
  186. Chance-Constrained Model Predictive Control for SAGD Process Using Robust Optimization Approximation
  187. An Augmented Model Approach for Identification of Nonlinear Errors-in-Variables Systems Using the EM Algorithm
  188. Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes
  189. Distributed Dynamic Modeling and Monitoring for Large-Scale Industrial Processes under Closed-Loop Control
  190. Control Performance Assessment for ILC-Controlled Batch Processes in a 2-D System Framework
  191. Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE
  192. Approaches to robust process identification: A review and tutorial of probabilistic methods
  193. Localization of Indoor Mobile Robot Using Minimum Variance Unbiased FIR Filter
  194. Distributed Student's t filtering algorithm for heavy-tailed noises
  195. Incipient Fault Detection for Complex Industrial Processes with Stationary and Nonstationary Hybrid Characteristics
  196. A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data
  197. Robust Estimation of ARX Models With Time Varying Time Delays Using Variational Bayesian Approach
  198. Minimum Variance Bound and Minimum Variance Controller for Convex Nonlinear Systems with Input Constraints
  199. Extracting dynamic features with switching models for process data analytics and application in soft sensing
  200. Triggered Communication in Distributed Adaptive High-Gain EKF
  201. A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
  202. Molecular-Based Bayesian Regression Model of Petroleum Fractions
  203. Expectation Maximization Approach for Simultaneous Gross Error Detection and Data Reconciliation Using Gaussian Mixture Distribution
  204. Robust Identification of Nonlinear Errors-in-Variables Systems With Parameter Uncertainties Using Variational Bayesian Approach
  205. Iteration Tuning of Disturbance Observer-Based Control System Satisfying Robustness Index for FOPTD Processes
  206. A Data-Based Augmented Model Identification Method for Linear Errors-in-Variables Systems Based on EM Algorithm
  207. Iterative Residual Generator for Fault Detection With Linear Time-Invariant State–Space Models
  208. Nonlinear robust optimization for process design
  209. Detection and Diagnosis of Multiple Faults With Uncertain Modeling Parameters
  210. Interaction Analysis of Multivariate Control Systems Under Bayesian Framework
  211. Bayesian Learning for Dynamic Feature Extraction With Application in Soft Sensing
  212. An E-HOIM Based Data-Driven Adaptive TILC of Nonlinear Discrete-Time Systems for Non-Repetitive Terminal Point Tracking
  213. Mixture semisupervised probabilistic principal component regression model with missing inputs
  214. Computationally-Light Non-Lifted Data-Driven Norm-Optimal Iterative Learning Control
  215. Data-driven high-order terminal iterative learning control with a faster convergence speed
  216. Distributed adaptive high-gain extended Kalman filtering for nonlinear systems
  217. A Probabilistic Just-in-Time Learning Framework for Soft Sensor Development With Missing Data
  218. A Data-Driven Hybrid ARX and Markov Chain Modeling Approach to Process Identification With Time-Varying Time Delays
  219. Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR
  220. Wavelet Transform Based Methodology for Detection and Characterization of Multiple Oscillations in Nonstationary Variables
  221. Adaptive soft sensor based on time difference Gaussian process regression with local time-delay reconstruction
  222. Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method
  223. JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy
  224. Robust probabilistic principal component analysis for process modeling subject to scaled mixture Gaussian noise
  225. Robust Gaussian process modeling using EM algorithm
  226. Double locally weighted principal component regression for soft sensor with sample selection under supervised latent structure
  227. New results on the robust stability of PID controllers with gain and phase margins for UFOPTD processes
  228. Slow feature analysis for monitoring and diagnosis of control performance
  229. Dynamic higher-order cumulants analysis for state monitoring based on a novel lag selection
  230. Monitoring of operating point and process dynamics via probabilistic slow feature analysis
  231. Predicting GHS toxicity using RTCA and discrete-time Fourier transform
  232. Robust Diagnosis of Operating Mode Based on Time-Varying Hidden Markov Models
  233. Robust optimization under correlated uncertainty: Formulations and computational study
  234. GMM and optimal principal components-based Bayesian method for multimode fault diagnosis
  235. Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference
  236. Hellinger distance based probability distribution approach to performance monitoring of nonlinear control systems
  237. Predicting wellbore dynamics in a steam-assisted gravity drainage system: Numeric and semi-analytic model, and validation
  238. Generalized expectation–maximization approach to LPV process identification with randomly missing output data
  239. Optimal continuous-time state estimation for linear finite and infinite-dimensional chemical process systems with state constraints
  240. Process monitoring using kernel density estimation and Bayesian networking with an industrial case study
  241. Fault Detection and Diagnosis of Multiple-Model Systems With Mismodeled Transition Probabilities
  242. State estimation incorporating infrequent, delayed and integral measurements
  243. Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling
  244. Diagnosis of Oscillations Between Controller Tuning and Harmonic External Disturbances
  245. A unified data-driven design framework of optimality-based generalized iterative learning control
  246. Nonlinear process identification in the presence of multiple correlated hidden scheduling variables with missing data
  247. Bayesian method for simultaneous gross error detection and data reconciliation
  248. Analysis of inter-/intra-E-plate repeatability in the real-time cell analyzer
  249. High-throughput screening assay for the environmental water samples using cellular response profiles
  250. A Bayesian sparse reconstruction method for fault detection and isolation
  251. Minimum variance unbiased FIR filter for discrete time-variant systems
  252. Minimal required excitation for closed-loop identification: Some implications for data-driven, system identification
  253. Process monitoring based on factor analysis: Probabilistic analysis of monitoring statistics in presence of both complete and incomplete measurements
  254. Expectation–Maximization Approach to Fault Diagnosis With Missing Data
  255. Detecting and isolating abrupt changes in linear switching systems
  256. Bias-eliminated subspace model identification under time-varying deterministic type load disturbance
  257. State Estimation in Batch Process Based on Two-Dimensional State-Space Model
  258. Adaptive monitoring of the process operation based on symbolic episode representation and hidden Markov models with application toward an oil sand primary separation
  259. Multi-input–Multi-output (MIMO) Control System Performance Monitoring Based on Dissimilarity Analysis
  260. A Bayesian framework for real-time identification of locally weighted partial least squares
  261. Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations
  262. Development of soft sensor by incorporating the delayed infrequent and irregular measurements
  263. Frequency analysis and compensation of valve stiction in cascade control loops
  264. Model predictive control of axial dispersion chemical reactor
  265. Expectation Maximization method for multivariate change point detection in presence of unknown and changing covariance
  266. Parameter estimation for a dual-rate system with time delay
  267. Robust multiple-model LPV approach to nonlinear process identification using mixture t distributions
  268. A unified recursive just-in-time approach with industrial near infrared spectroscopy application
  269. Multiple-Model Based Linear Parameter Varying Time-Delay System Identification with Missing Output Data Using an Expectation-Maximization Algorithm
  270. Performance Assessment of Industrial Linear Controllers in Univariate Control Loops for Both Set Point Tracking and Load Disturbance Rejection
  271. Nonlinear semisupervised principal component regression for soft sensor modeling and its mixture form
  272. Recursive constrained state estimation using modified extended Kalman filter
  273. Automatic Detection and Frequency Estimation of Oscillatory Variables in the Presence of Multiple Oscillations
  274. A probabilistic framework for real-time performance assessment of inferential sensors
  275. Control-loop diagnosis using continuous evidence through kernel density estimation
  276. Mode of action classification of chemicals using multi-concentration time-dependent cellular response profiles
  277. Inequality constrained parameter estimation using filtering approaches
  278. Constrained particle filtering methods for state estimation of nonlinear process
  279. Performance assessment, diagnosis, and optimal selection of non-linear state filters
  280. Bayesian Control Loop Diagnosis by Combining Historical Data and Process Knowledge of Fault Signatures
  281. Bayesian and Expectation Maximization methods for multivariate change point detection
  282. In vitro cytotoxicity assessment based on KC50 with real-time cell analyzer (RTCA) assay
  283. Mixture semisupervised principal component regression model and soft sensor application
  284. Control loop diagnosis with ambiguous historical operating modes: Part 2, information synthesis based on proportional parametrization
  285. Design of inferential sensors in the process industry: A review of Bayesian methods
  286. Moving horizon estimation for switching nonlinear systems
  287. Information transfer methods in causality analysis of process variables with an industrial application
  288. Parameter estimation in batch process using EM algorithm with particle filter
  289. Statistical properties of signal entropy for use in detecting changes in time series data
  290. Data quality assessment of routine operating data for process identification
  291. Model Predictive Control: Algorithmic Development and Applications
  292. Soft sensors for online steam quality measurements of OTSGs
  293. FIR model identification of multirate processes with random delays using EM algorithm
  294. A moving horizon approach to a noncontinuum state estimation
  295. Bayesian method for state estimation of batch process with missing data
  296. Recursive Wavelength-Selection Strategy to Update Near-Infrared Spectroscopy Model with an Industrial Application
  297. Improved DCT-based method for online detection of oscillations in univariate time series
  298. Soft sensor solutions for control of oil sands processes
  299. Control loop diagnosis with ambiguous historical operating modes: Part 1. A proportional parametrization approach
  300. On simultaneous on-line state and parameter estimation in non-linear state-space models
  301. Development and industrial application of soft sensors with on-line Bayesian model updating strategy
  302. Cytotoxicity assessment based on the AUC50 using multi-concentration time-dependent cellular response curves
  303. A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry
  304. 4th Symposium on Advanced Control of Industrial Processes (ADCONIP)
  305. Compensation of control valve stiction through controller tuning
  306. 4th Symposium on advanced control of industrial processes (Adconip)
  307. High-throughput quantitative analysis with cell growth kinetic curves for low copy number mutant cells
  308. A Bayesian approach to robust process identification with ARX models
  309. Guest Editorial: 4TH symposium on advanced control of industrial processes (ADCONIP)
  310. Deterministic vs. stochastic performance assessment of iterative learning control for batch processes
  311. Microelectronic-sensing assay to detect presence of Verotoxins in human faecal samples
  312. Dual particle filters for state and parameter estimation with application to a run-of-mine ore mill
  313. Recognition of chemical compounds in contaminated water using time-dependent multiple dose cellular responses
  314. Tuning a Soft Sensor’s Bias Update Term. 1. The Open-Loop Case
  315. Tuning a Soft Sensor’s Bias Update Term. 2. The Closed-Loop Case
  316. Estimation of bitumen froth quality using Bayesian information synthesis: An application to froth transportation process
  317. Identification of nonlinear parameter varying systems with missing output data
  318. Model analysis and performance analysis of two industrial MPCs
  319. On-line estimation of glucose and biomass concentration in batch fermentation process using particle filter with constraint
  320. Designing priors for robust Bayesian optimal experimental design
  321. Identification of switched Markov autoregressive eXogenous systems with hidden switching state
  322. Multiple model based LPV soft sensor development with irregular/missing process output measurement
  323. Dynamic bayesian approach to gross error detection and compensation with application toward an oil sands process
  324. Prediction error method for identification of LPV models
  325. Solid oxide fuel cell: Perspective of dynamic modeling and control
  326. Determining the state of a process control system: Current trends and future challenges
  327. Dynamic output feedback robust model predictive control
  328. Estimation of distribution function for control valve stiction estimation
  329. Performance assessment of PID control loops subject to setpoint changes
  330. Closed-loop identification with routine operating data: Effect of time delay and sampling time
  331. Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences
  332. Closed-loop identification condition for ARMAX models using routine operating data
  333. Control Performance Assessment Subject to Multi-Objective User-Specified Performance Characteristics
  334. Development of a simultaneous continuum and noncontinuum state estimator with application on a distillation process
  335. Estimation of Instrument Variance and Bias Using Bayesian Methods
  336. Real-time cell-impedance sensing assay as an alternative to clonogenic assay in evaluating cancer radiotherapy
  337. Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources
  338. Determining the Harmonic Impacts of Multiple Harmonic-Producing Loads
  339. Reconciling continuum and non-continuum data with industrial application
  340. Monitoring of solid oxide fuel cell systems
  341. A decoupled multiple model approach for soft sensors design
  342. Multiple model LPV approach to nonlinear process identification with EM algorithm
  343. Constrained receding-horizon experiment design and parameter estimation in the presence of poor initial conditions
  344. Subspace Approach to Identification of Step-Response Model from Closed-Loop Data
  345. Performance assessment of advanced supervisory–regulatory control systems with subspace LQG benchmark
  346. Dynamic Bayesian Approach for Control Loop Diagnosis with Underlying Mode Dependency
  347. Bayesian method for multirate data synthesis and model calibration
  348. The DCT-based oscillation detection method for a single time series
  349. Early determination of toxicant concentration in water supply using MHE
  350. Online composition estimation and experiment validation of distillation processes with switching dynamics
  351. Constrained Bayesian state estimation – A comparative study and a new particle filter based approach
  352. Industrial implementation of controller performance analysis technology
  353. Estimation and control of solid oxide fuel cell system
  354. Multi-step prediction error approach for controller performance monitoring
  355. Stiction Estimation Using Constrained Optimisation and Contour Map
  356. Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking
  357. Robust identification of switched regression models
  358. Robust identification of piecewise/switching autoregressive exogenous process
  359. Implementation of FIR control for H ∞ output feedback stabilisation of linear systems
  360. Subspace method aided data-driven design of fault detection and isolation systems
  361. H∞structured model reduction algorithms for linear discrete systems via LMI-based optimisation
  362. Identification of Hammerstein systems without explicit parameterisation of non-linearity
  363. Closed-loop model validation based on the two-model divergence method
  364. Economic performance assessment of advanced process control with LQG benchmarking
  365. MPC Constraint Analysis—Bayesian Approach via a Continuous-Valued Profit Function
  366. Identifiability and estimability study for a dynamic solid oxide fuel cell model
  367. Preferential crystallization: Multi-objective optimization framework
  368. Validation of continuous-time models with delay
  369. A Bayesian approach for control loop diagnosis with missing data
  370. Dealing with Irregular Data in Soft Sensors: Bayesian Method and Comparative Study
  371. Bayesian methods for control loop monitoring and diagnosis
  372. Robust H2 optimal filtering for con...
  373. Sensitivity analysis for selective constraint and variability tuning in performance assessment of industrial MPC
  374. Control relevant on-line model validation criterion based on robust stability conditions
  375. Performance assessment of MIMO control systems with time-variant disturbance dynamics
  376. Reformulation of LMI-based stabilisation conditions for non-linear systems in Takagi–Sugeno's form
  377. Identification from step responses with transient initial conditions
  378. 1-D dynamic modeling of SOFC with analytical solution for reacting gas-flow problem
  379. Dynamics and variance control of hot mill loopers
  380. Assessing Model Prediction Control (MPC) Performance. 1. Probabilistic Approach for Constraint Analysis
  381. Assessing Model Prediction Control (MPC) Performance. 2. Bayesian Approach for Constraint Tuning
  382. Output feedback model predictive control for nonlinear systems represented by Hammerstein–Wiener model
  383. New formulation of robust MPC by incorporating off-line approach with on-line optimization
  384. Comments on "A Feedback Min-Max MPC Algorithm for LPV Systems Subject to Bounded Rates of Change of Parameters
  385. Novel identification method from step response
  386. Constrained approximation of multiple input–output delay systems using genetic algorithm
  387. Constrained robust model predictive control for time-delay systems with polytopic description
  388. A blind approach to closed-loop identification of Hammerstein systems
  389. Data-driven predictive control for solid oxide fuel cells
  390. Performance Assessment of Model Pedictive Control for Variability and Constraint Tuning
  391. Control relevant modeling of planer solid oxide fuel cell system
  392. FIR modelling for errors-in-variables/closed-loop systems by exploiting cyclo-stationarity
  393. Improved identification of continuous-time delay processes from piecewise step tests
  394. Multirate Minimum Variance Control Design and Control Performance Assessment: A Data-Driven Subspace Approach
  395. Monitoring control performance via structured closed-loop response subject to output variance/covariance upper bound
  396. Dynamic modeling of a finite volume of solid oxide fuel cell: The effect of transport dynamics
  397. Performance monitoring of SISO control loops subject to LTV disturbance dynamics: An improved LTI benchmark
  398. Alternative solutions to multi-variate control performance assessment problems
  399. Parameter and delay estimation of continuous-time models using a linear filter
  400. Cyclo-period estimation for discrete-time cyclo-stationary signals
  401. Model Reduction of Uncertain Systems with Multiplicative Noise Based on Balancing
  402. Stochastic stability and robust control for sampled-data systems with Markovian jump parameters
  403. Dynamic modeling of solid oxide fuel cell: The effect of diffusion and inherent impedance
  404. Closed-loop identification with a quantizer
  405. A new method for stabilization of networked control systems with random delays
  406. Multirate robust digital control for fuzzy systems with periodic Lyapunov function
  407. Practical solutions to multivariate feedback control performance assessment problem: reduced a priori knowledge of interactor matrices
  408. On spectral theory of cyclostationary signals in multirate systems
  409. Closed-loop subspace identification: an orthogonal projection approach
  410. Fixed-order controller design for linear time-invariant descriptor systems: A BMI approach
  411. Minimum variance in fast, slow and dual-rate control loops
  412. Performance assessment and robustness analysis using an ARMarkov approach
  413. H2 approximation of multiple input/output delay systems
  414. Closed-loop identification via output fast sampling
  415. Robust Model Predictive Control of Singular Systems
  416. Robust Digital Model Predictive Control for Linear Uncertain Systems With Saturations
  417. Multirate sampled-data systems: computing fast-rate models
  418. Feedforward and Feedback Controller Performance Assessment of Linear Time-Variant Processes
  419. Industrial Applications of a Feedback Controller Performance Assessment of Time-Variant Processes
  420. Performance evaluation of two industrial MPC controllers
  421. H∞ model reduction of Markovian jump linear systems
  422. LMI synthesis of H/sup 2/ and mixed H/sub 2//H/sub ∞/ controllers for singular systems
  423. Model predictive control relevant identification and validation
  424. Improved Threshold for the Local Approach in Detecting Faults
  425. A pragmatic approach towards assessment of control loop performance
  426. Controller performance assessment in set point tracking and regulatory control
  427. On gramians and balanced truncation of discrete-time bilinear systems
  428. Estimation of the Dynamic Matrix and Noise Model for Model Predictive Control Using Closed-Loop Data