Functional Resonance Analysis Research Articles

How Big Data & Analytics can improve process and plant safety and become an indispensable tool for risk management

With the advances in digitization, Information Technology (IT), and connected devices, data are becoming plentiful. And with the past 30 years of developments of Artificial Intelligence tools leading to great enhancements in dealing with various levels and types of uncertainty, much has become tangible, where in the past it used to remain vague and fuzzy. Tools like neural networks can distil information from datasets, while probabilistic methods can characterize randomness. Bayesian causation networks enable finding critical pathways and help to design and monitor effective safeguards, while Petri nets enable analysis of time-critical events. Interval analysis, Dempster-Shafer theory, and fuzzy logic can assist in delimiting uncertainty in measurement results and expert judgment. System dynamics modeling and Functional resonance analysis may unravel interactively degrading processes. All this can improve understanding about communication lines and mechanisms of interactions within a plant socio-technical system, and the influences on achievement and performance. This will result in reformed work processes, manufacturing conditions and help in identifying abnormal trends. Therefore, while planning and prediction are based on observational evidence and trends, the new technologies will be a strong support for management, in recognizing and evaluating risks, including safety risks. Although applications of big data and analytics are still young, nevertheless in process control and reliability prediction of equipment a few achievements have already been demonstrated. However, much more is possible. For example, in the case of process safety performance indicators, lagging indicators are usually available but the techniques may stimulate the recording of the more important leading indicators for the prediction of safety and culture trend in a company in relation to its economic health. The paper will present more details on the methods and an example of dynamic risk mapping.

Bayesian wavelet-based analysis of functional magnetic resonance time series

Wavelet methods for image regularization offer a data-driven alternative to Gaussian smoothing in functional magnetic resonance (fMRI) analysis. Their impact has been limited by the difficulties in integrating regularization in the wavelet domain and inference in the image domain, precluding the probabilistic decision on which areas are activated by a task. Here we present an integrated framework for Bayesian estimation and regularization in wavelet space that allows the usual voxelwise hypothesis testing. This framework is flexible, being an adaptation to fMRI time series of a more general wavelet-based functional mixed-effect model. Through testing on a combination of simulated and real fMRI data, we show evidence of improved signal recovery, without compromising test accuracy in image space.