Process monitoring using latent variable methods
Variable Selection Methods for Multivariate Process Monitoring
... new methods to select a reduced number of relevant variables for multivariate SPC that makes use of engineering, cost and variability evaluation ...were monitoring all original ...Various variable ...
5
Approximation methods for latent variable models
... approximation methods might compete with each other as choices in a given application, it is pertinent to describe the features they have that should influence any decisions regarding their ...when using ...
216
Wrestling With Issues in Scale Development Using Joint Latent Variable Methods
... of latent variables in a single ...the latent variable literature as well as the univariate ZIPO model, the MZIPO model allows for the estimation of a traditional joint latent variable ...
147
Gaussian Process Latent Variable Alignment Learning
... process objective for ...minimisation methods are able to perform this task as they have no knowledge of the underlying structure of the ...data using joint Gaussian or non- parametric models on the ...
14
Real-time Body Tracking Using a Gaussian Process Latent Variable Model
... by using appropriate im- age features, our tracking framework can also be modified to track human motions from monocular ...tracker using ’balanced GPDM’ similar to ...
8
Online Tensor Methods for Learning Latent Variable Models
... Compared to the state-of-the-art method for learning MMSB models using the stochas- tic variational inference algorithm of (Gopalan et al., 2012), we obtain several orders of magnitude speed-up in the running time ...
39
A gaussian process latent variable model for BRDF inference
... Abstract The problem of estimating a full BRDF from partial ob- servations has already been studied using either paramet- ric or non-parametric approaches. The goal in each case is to best match this sparse set of ...
9
Latent Variable Models and Big Data in the Process Industries
... Keywords: Latent variables, Big Data, Process analysis, Monitoring, Optimization, Control, Batch processes, Image analysis, ...the process industries work centred on Big Data has been around ...
5
Discriminative Gaussian Process Latent Variable Model for Classification
... sian Process Latent Variable Models can discover low dimensional manifolds given only a small number of examples, but learn a latent space without regard for class ...the latent space, ...
10
Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model
... The TCGA (The Cancer Genome Atlas) project has made widely available for the first time multiple modes of genomic data from the same large number of samples. This has motivated the devel[r] ...
116
Nonlinear dynamic process monitoring using kernel methods
... closer process management due to increased data availability can often be offset by losses arising from time spent in dealing with unexpected ...of process measurements has also increased the pressure on ...
136
Particle methods for maximum likelihood estimation in latent variable models
... is monitoring the marginal posterior of every parameter combi- nation which is sampled and using that set of parameters associated with the largest value ...
16
Latent Variable Modeling of String Transductions with Finite State Methods
... to latent classes and regions improves our results dramatically, even on small training data ...sizes. Using these we outper- form moses9 and moses15, which use long context windows, reducing error rates by ...
10
Bayesian latent variable methods for longitudinal processes with applications to fetal growth
... Directions Latent variable methods provide a flexible approach for complex modeling of correlation in longitudinal ...discussed methods for using latent variables to aggre- gate ...
150
Detecting Damage on Wind Turbine Bearings Using Acoustic Emissions and Gaussian Process Latent Variable Models
... vibration monitoring it is already too late. AE monitoring is therefore desirable to identify the early onset of a ...crack. Using AE techniques for this application has been successfully studied and ...
9
Monitoring and diagnosis of process systems using kernel-based learning methods
... eective process control is vital to the e cient operation of chemical pro- cess ...the process dynamics of the sys- ...of process systems requires knowledge of the nature of disturbance (stochastic ...
200
Applying Conditional Distributions To Individuals: Using Latent Variable Models
... statistically-based methods has been obvious from the historical point of view, as the scientific method of accumulating knowledge in the post-Galilean era has contributed to valid knowledge and astonishing ...
106
Robustness in Latent Variable Models
... in Latent Variable ...involving latent variables are widely used in many areas of applications, such as biomedical science and social ...inferential methods are used to make statistical ...
90
Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process
... from process units that induce inertia, and the high sampling rates used in modern data acquisition instrumentation [2], ...for monitoring processes (especially univariate processes) with auto-correlated ...
6
Open Domain Event Extraction Using Neural Latent Variable Models
... As shown in Figure 1, compared with tradi- tional event extraction task exemplified by MUC 4 (Sundheim, 1992), the task of ODEE poses ad- ditional challenges to modeling, which have not been considered in traditional ...
12