This paper describes the design and implementation of a real-time multivariate process control system that features a graphical user interface (GUI) and provides useful information for both line operators and engineers.
Posterior inference for fixed parameters and dynamic latent factors is performed via a custom tailored Markov chain Monte Carlo scheme for multivariate dynamic systems that combines extended Kalman filter-based Metropolis–Hastings proposal densities with block-sampling schemes.
Then, the sensor responses are sent to a computer system that uses multivariate data analysis methods to distinguish differences in the data based on the VOCs in the sample headspace.
The result of this research is a multi-agent system that applied to a multivariate process monitoring.
For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity.
It is an open source system that can be freely distributed for multivariate analysis and visualization.
In this article, we present a case study using a prototype system that is used for quantifying multiple sclerosis lesions from multivariate MRI.
Multivariate analysis methods can be a powerful tool to determine the macroscopic stoichiometry of a biological system that cannot easily be determined by intuition.
To realize a complete monitoring system for multivariate process and simplify the reconfiguration task to the operators that are not specializing in the realm, we developed an expert system that assures the process correction.
Notably, the new TNM staging system that was based on the LND ratio was consistently superior to the traditional system in all of the selected survival measures (Table 2 and Supplementary Table 3 for the multivariate analysis).
