Observers are computational algorithms designed to estimate unmeasured state variables
due to the lack of appropriate estimating devices or to replace high-priced sensors in a plant …

While state estimation techniques are routinely applied to systems represented by ordinary
differential equation (ODE) models, it remains a challenging task to design an observer for a …

Abstract Computational Fluid Dynamics (CFD) and Machine Learning (ML) approaches are
implemented and compared in an industrial Chemical Vapor Deposition process for the …

This paper presents a new approach for sensor location for state and parameter estimation
for stable nonlinear systems. The unique feature of this technique is that sensor locations for …

A reinforcement learning-based method is proposed for optimal sensor placement in the
spatial domain for modeling distributed parameter systems (DPSs). First, a low-dimensional …

Accurate measurement of micro-climates that include temperature and relative humidity is
the bedrock of the control and management of plant life in protected cultivation systems …

Selection of sparse sensors to recover the global signal field is a crucial task in many areas.
Most of the existing algorithms tackle this problem by optimizing the surrogates of …

A class of nonlinear distributed processes with component and actuator faults is presented.
An adaptive detection observer with a time varying threshold is proposed that provides …

A data-driven framework is presented, that enables the prediction of quantities, either
observations or parameters, given sufficient partial data. The framework is illustrated via a …

In the last years, many European countries have experienced the effects of climate change,
in the form of a scarcity of drinking water resources, prolonged periods of drought, and …