A rank correlation coefficient based particle filter to estimate parameters in non-linear models

a new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot

abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...

A Particle Filter to Estimate non-Markovian States

Common particle filter algorithms are unable to differentiate between sensor noise and kidnap situations. These two problems are usual in realistic applications and, in general, ad-hoc heuristic are developed to deal with them. In this paper, we present a particle filter algorithm where the state is enriched with information about the coherence of the sensor readings. This additional informatio...

Spearman Rank Correlation Coefficient

Spearman’s Rank Order Correlation Coefficient

• In this lesson, we will learn how to measure the coefficient of correlation for two sets of ranking. • The coefficient of correlation, r, measures the strength of association or correlation between two sets of data that can be measured. Sometimes, the data is not measurable but can only be ordered, as in ranking. • For example, two students can be asked to rank toast, cereals, and dim sum in ...

A TLD based method to estimate bowtie filter shape in PET/CT

Background: In CT systems, the machine utilizes a bowtie filter to shape the X-ray beam and remove lower energy photons. The shape of this bowtie filter is complicated and its geometry is often not available in detail. These renders the CT dose index (CTDI) to have different values in measurement versus Monte Carlo simulation studies and other analytical calculations especially in dosimetry of ...