Sensor fault detection with low computational cost : a proposed neural network-based control scheme

The paper describes a low computational power method for detecting sensor faults. A typical fault detection unit for multiple sensor fault detection with modelbased approaches, requires a bank of estimators. The estimators can be either observer or artificial intelligence based. The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as ‘i-FD’. In contrast with the bank-estimators approach the proposed i-FD unit is using only one estimator for multiple sensor fault detection. The efficacy of the scheme is tested on an Electro-Magnetic Suspension (EMS) system and compared with a bank of Kalman estimators in simulation environment

On the correspondence from Bayesian log-linear modelling to logistic regression modelling with gg-priors

Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Within the Bayesian framework, the $g$-prior and mixtures of $g$-priors are commonly assigned to the parameters of a generalized linear model. We prove that assigning a $g$-prior (or a mixture of $g$-priors) to the parameters of a certain log-linear model designates a $g$-prior (or a mixture of $g$-priors) on the parameters of the corresponding logistic regression. By deriving an asymptotic result, and with numerical illustrations, we demonstrate that when a $g$-prior is adopted, this correspondence extends to the posterior distribution of the model parameters. Thus, it is valid to translate inferences from fitting a log-linear model to inferences within the logistic regression framework, with regard to the presence of main effects and interaction terms.Comment: 27 page

A novel, divergence based, regression for compositional data

In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science amongst others. The goal of this paper is to propose a new, divergence based, regression modelling technique for compositional data. To do so, a recently proved metric which is a special case of the Jensen-Shannon divergence is employed. A strong advantage of this new regression technique is that zeros are naturally handled. An example with real data and simulation studies are presented and are both compared with the log-ratio based regression suggested by Aitchison in 1986.Comment: This is a preprint of the paper accepted for publication in the Proceedings of the 28th Panhellenic Statistics Conference, 15-18/4/2015, Athens, Greec

Regression analysis with compositional data containing zero values

Regression analysis with compositional data containing zero valuesComment: The paper has been accepted for publication in the Chilean Journal of Statistics. It consists of 12 pages with 4 figure

MRI image segmantation based on edge detection

Cílem této práce je představit základní segmentační techniky používáné v oblasti medicínského zpracování obrazových dat a pomocí 3D prohlížeče schopného zobrazit 3D obrazy implementovat segmentační modul založený na hranové detekci a vyhodnotit výsledky. Navrhovaný prohlížeč je sestavený v prostředi Matlab GUI a je schopen načíst objem 3D snímků představující lidskou hlavu. Navrhovaný segmentační modul je založen na použití hranových detektorů, zejména Cannyho detektoru.The aim of this thesis is to present the basic segmentation techniques uses in the field of medical image processing and by using a 3D viewer able to visualize 3D images, implement a segmentation module based on edges detection and evaluate the results. The proposed viewer is a 3D viewer build using matlab GUI and is able to load a volume of images representing the human head. The proposed segmentation module is based on the use of edge detectors particularly the Canny algorithm.

Forward Exponential Performances: Pricing and Optimal Risk Sharing

In a Markovian stochastic volatility model, we consider financial agents whose investment criteria are modelled by forward exponential performance processes. The problem of contingent claim indifference valuation is first addressed and a number of properties are proved and discussed. Special attention is given to the comparison between the forward exponential and the backward exponential utility indifference valuation. In addition, we construct the problem of optimal risk sharing in this forward setting and solve it when the agents' forward performance criteria are exponential.Comment: 29 page

Switching probability of all-perpendicular spin valve nanopillars

In all-perpendicular spin valve nanopillars the probability density of the free-layer magnetization is independent of the azimuthal angle and its evolution equation simplifies considerably compared to the general, nonaxisymmetric geometry. Expansion of the time-dependent probability density to Legendre polynomials enables analytical integration of the evolution equation and yields a compact expression for the practically relevant switching probability. This approach is valid when the free layer behaves as a single-domain magnetic particle and it can be readily applied to fitting experimental data.Comment: 2 figures, 5 pages, double colum