Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
Design of inferential sensors in the process industry: A review of Bayesian methods
In many industrial plants, development and implementation of advanced monitoring and
control techniques require real-time measurement of process quality variables. However, on …
control techniques require real-time measurement of process quality variables. However, on …
Data driven discovery of cyber physical systems
Cyber-physical systems embed software into the physical world. They appear in a wide
range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber …
range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber …
Bayesian learning and inference in recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball teams traversing a
court, give rise to time series data with complex, nonlinear dynamics. We can gain insight …
court, give rise to time series data with complex, nonlinear dynamics. We can gain insight …
Deep learning helicopter dynamics models
We consider the problem of system identification of helicopter dynamics. Helicopters are
complex systems, coupling rigid body dynamics with aerodynamics, engine dynamics …
complex systems, coupling rigid body dynamics with aerodynamics, engine dynamics …
Dissimilarity-based sparse subset selection
Finding an informative subset of a large collection of data points or models is at the center of
many problems in computer vision, recommender systems, bio/health informatics as well as …
many problems in computer vision, recommender systems, bio/health informatics as well as …
Bayesian nonparametric inference of switching dynamic linear models
Many complex dynamical phenomena can be effectively modeled by a system that switches
among a set of conditionally linear dynamical modes. We consider two such models: the …
among a set of conditionally linear dynamical modes. We consider two such models: the …
A survey on switched and piecewise affine system identification
Recent years have witnessed a growing interest on system identification techniques for
switched and piecewise affine models. These model classes have become popular not only …
switched and piecewise affine models. These model classes have become popular not only …
Identification of switched linear systems via sparse optimization
L Bako - Automatica, 2011 - Elsevier
The work presented in this paper is concerned with the identification of switched linear
systems from input-output data. The main challenge with this problem is that the data are …
systems from input-output data. The main challenge with this problem is that the data are …
[BOOK][B] Automotive model predictive control: models, methods and applications
Automotive control has developed over the decades from an auxiliary te-nology to a key
element without which the actual performances, emission, safety and consumption targets …
element without which the actual performances, emission, safety and consumption targets …