Hybrid discrete-continuous models, such as Jump Markov Linear Systems, are convenient tools for representing many real-world systems; in the case of fault detection, discrete jumps in the continuous dynamics are used to model system failures. Stochastic uncertainty in hybrid systems arises in both the continuous dynamics, in the form of uncertain state estimation, disturbances or uncertain mode...

In the detection of particles using digital in-line holography, measurement accuracy is substantially influenced by the hologram processing method. In particular, a number of methods have been proposed to determine the out-of-plane particle depth (z location). However, due to the lack of consistent uncertainty characterization, it has been unclear which method is best suited to a given measurem...

This paper proposes an efficient optimization approach to determine the optimal placement and size of Static VAR Compensator (SVC) in a hybrid power system. The hybrid power system consists of conventional thermal generators, wind farms and solar photovoltaic (PV) modules. In this paper, the optimal placement and size of SVC is determined by considering the voltage deviations minimizations and ...

In the present day scenario, there are large numbers of clustering algorithms available to group objects having similar characteristics. But the implementations of many of those algorithms are challenging when dealing with categorical data. While some of the algorithms available at present cannot handle categorical data the others are unable to handle uncertainty. Many of them have the stabilit...

In response to the increasing needs to manage the risk of complex socio-technical systems, a number of different approaches have been investigated over the decades. In particular, Probabilistic Risk Assessment (PRA) has been successfully utilized for engineering systems and human errors while System Dynamics (SD) has recently emerged as a useful tool to analyze organization factors. As a natura...

In this paper we propose modeling and analysis techniques for genetic networks that provide biologists with insight into the dynamics of such systems. Central to our modeling approach is the framework of hybrid systems and our analysis tools are derived from formal analysis of such systems. Given a set of states characterizing a property of biological interestP , we present the Multi-Affine Rec...

Scene object recognition is an essential requirement for intelligent mobile robots. In addition to geometric or appearance features, modern recognition systems strive to incorporate contextual information, normally modelled through Probabilistic Graphical Models (PGMs) or Semantic Knowledge (SK). However, these approaches, separately, show some weaknesses that limit their application, e.g., the...

In this paper we propose modeling and analysis techniques for genetic networks that provide biologists with insight into the dynamics of such systems. Central to our modeling approach is the framework of hybrid systems and our analysis tools are derived from formal analysis of such systems. Given a set of states characterizing a property of biological interestP , we present the Multi-Affine Rec...

A computational analysis of convective heat flux around an array of cylinders in a high Reynolds number flow is presented; we assume that both the inflow and the wall heat flux conditions are specified with a margin of uncertainty and our objective is to quantify the resulting effect on some functional of interest, mainly the cylinder wall temperature. We introduce a hybrid uncertainty propagat...

The paper discusses the hybrid sensing method and presents the hybrid detector (HD) which improves the sensing performance. The proposed HD takes advantage of the energy detection (ED) and a method based on the Covariance Absolute Value (CAV) or Cyclic Autocorrelation Function (CAF). The paper characterizes the limitations of the use of ED resulting from the uncertainty of spectral density of n...