This paper proposes an efficient algorithmic approach that overcomes the critical challenges in real-time unbalanced distribution system state estimation, topology error processing, and outage identification simultaneously. These include (1) limited locations of measurement devices unsynchronized data as well missing bad data, (2) complicated mixed-phase switch actions mutual impedances shunt a...

An automated registration system named AISIR (Automated inter-sensor/inter-band satellite image registration) has been designed and implemented for the purpose of registering satellite images acquired using different sensors and spectral bands. Sensor and environmental noise, contrast non-uniformities, and inter-sensor and inter-band intensity mapping differences are addressed in the AISIR syst...

This paper describes a rather non-standard variant of state feedback controller. The design is based on controller tuning according to degree of similarity between responses of the measurable variables of the real process and the same variables calculated by estimator. There is also described unusual structure of controller with unequal control and estimation steps. This structural modification...

This paper presents a comprehensive formulation of a linearized state space process model for a generic two-reactant-two-product reactive distillation system. The development of the model requires the knowledge of the desired steady state design data, including liquid holdups and composition profiles. The application of the developed linear process model in composition estimation and control of...

State estimation is necessary in diagnosing anomalies in Water Demand Systems (WDS). In this paper we present a neural network performing such a task. State estimation is performed by using optimization, which tries to reconcile all the available information. Quantification of the uncertainty of the input data (telemetry measures and demand predictions) can be achieved by means of robust estate...

We describe the formalism for optimally estimating and controlling both the state of a spin ensemble and a scalar magnetic field with information obtained from a continuous quantum limited measurement of the spin precession due to the field. The full quantum parameter estimation model is reduced to a simplified equivalent representation to which classical estimation and control theory is applie...

This paper presents a new robust adaptive Gaussian mixture sigma-point particle filter by adopting the concept of robust adaptive estimation to the Gaussian mixture sigma-point particle filter. This method approximates state mean and covariance via Sigma-point transformation combined with new available measurement information. It enables the estimations of state mean and covariance to be adjust...

Abstract This paper presents a new robust adaptive filtering method for SINS/SAR (Strap-down Inertial Navigation System / Synthetic Aperture Radar) integrated navigation system. This method adopts the principle of robust estimation to adaptive filtering of observational data. A robust adaptive filter is developed to adaptively determine the covariance matrix of observation noise, and adaptively...