https://www.eng.tau.ac.il/%7Eemilia/Aut14a.pdf

Technical communique: Comments on finite-time stability of time-delay systems

Heart diseases are becoming a common public health problem worldwide, mostly due to a lack of health awareness, a poor lifestyle, and unhealthy consumption. Its accurate prediction and diagnosis are major challenges faced by medical centers and hospitals. The advancement in computing technologies has allowed the healthcare industry to collect and store routine medical data that can be used to support medical decisions. In many developed countries, the patients’ data are collected and stored in digital form. The data is then analyzed to make the necessary medical decisions involving prediction, diagnosis, image analysis, and line of treatment. Machine learning algorithms have been playing a vital role in solving complex, highly nonlinear classification and prediction problems. Further, different machine learning algorithms are ensembled in order to increase the classification and prediction accuracy. In this paper, different machine learning techniques have been ensembled using the majority voting technique to predict heart diseases, which achieves an accuracy of 88.88%. The results have been compared with other published works, which show a higher accuracy of the ensemble method over individual classifiers.

Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.

/pdf/computational-diagnosis-of-skin-lesions-from-dermoscopic-3h5f0wtk8g.pdf

Computational diagnosis of skin lesions from dermoscopic images using combined features

The Unmanned Aerial Vehicles (UAV) are widely used for capturing images in border area surveillance, disaster intensity monitoring, etc. An aerial photograph offers a permanent recording solution a...

/pdf/log-transform-based-optimal-image-enhancement-using-firefly-1a2ogtt1l4.pdf

Log Transform Based Optimal Image Enhancement Using Firefly Algorithm for Autonomous Mini Unmanned Aerial Vehicle: An Application of Aerial Photography

Robust finite-time sampled-data control of linear systems subject to random occurring delays and its application to Four-Tank system

This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2nd order electrical linear system.

Input fault detection and estimation using PI observer based on the ARX-Laguerre model

The﻿Discrete﻿Event﻿Systems﻿ (DES)﻿ is﻿ a﻿nonlinear﻿dynamic﻿ system﻿with﻿discrete﻿ state﻿ and﻿event﻿ evolution.﻿In﻿this﻿article,﻿we﻿are﻿interested﻿in﻿the﻿diagnosis﻿of﻿failures﻿with﻿Timed﻿Automata.﻿The﻿ proposed﻿approach﻿is﻿based﻿on﻿the﻿operating﻿time﻿and﻿it﻿is﻿applicable﻿to﻿any﻿system﻿whose﻿dynamic﻿ evolution﻿depends﻿not﻿only﻿on﻿the﻿order﻿of﻿discrete﻿events﻿but﻿also﻿on﻿their﻿periods﻿as﻿in﻿industrial﻿ processes.﻿ The﻿ most﻿ important﻿ part﻿ of﻿ this﻿ work﻿ is﻿ the﻿ construction﻿ of﻿ a﻿ diagnoser﻿ which﻿ uses﻿ observables﻿events﻿to﻿detect﻿and﻿locate﻿the﻿faults.﻿We﻿present﻿on﻿the﻿last﻿part﻿of﻿this﻿work﻿results﻿of﻿ the﻿study﻿of﻿the﻿performance﻿of﻿the﻿diagnosis﻿showing﻿the﻿power﻿of﻿this﻿diagnostic﻿approach.﻿An﻿ implementation﻿on﻿a﻿hydraulic﻿system﻿is﻿made﻿to﻿illustrate﻿the﻿proposed﻿steps.﻿It﻿put﻿in﻿evidence﻿the﻿ effectiveness﻿of﻿this﻿approach.﻿The﻿model﻿of﻿the﻿simulation﻿phase﻿is﻿done﻿using﻿Matlab﻿/﻿Simulink﻿ /﻿stateflow. KeywoRDS Detection, Diagnosis, Discrete Event System, Localization, Timed Automata

New Approach of Diagnosis by Timed Automata

The novelty of this work consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output. This is in order to estimate the unknown outputs presented as faults and to detect the time instant corresponding to the system malfunction. The stability and the convergence properties of the proposed PIO are ensured by using Linear Matrix Inequality. Furthermore an optimal identification of both Laguerre poles is achieved by a genetic algorithm approach where a parametric significant reduction is ensured to guarantee a reduced observer. The performances of the identification approach and the resulting PIO are tested on an experimental 2
 nd
 order electrical system.

/pdf/pio-output-fault-diagnosis-by-arx-laguerre-model-applied-to-e7f1v5ir3l.pdf

PIO Output Fault Diagnosis by ARX-Laguerre Model Applied to 2 nd Order Electrical System

In this paper, we address the problem of finite-time stabilization of nonlinear affine systems. The idea is to propose an explicit formula for a state feedback finite-time stabilizing controller expressed directly in terms of the vector fields defining the system model and an extra positive scalar. The method is simple and can be seen as an extension in the context of finite-time stability of the Sontag's method concerning a universal formula for nonlinear stabilization (Sontag, 1991). The method can also be applied to the case of certain and uncertain linear systems. Some examples illustrate the proposed theory and point out the simplicity of the proposed approach. RESUME. Dans cet article, on considere le probleme de stabilisation en temps fini des systemes non lineaires affines. L'idee est de proposer une formule explicite d'une loi de commande par retour d'etat exprimee en fonction des vecteurs definissant le modele du systeme et d'une certaine constante positive. La methode est simple et peut etre consideree comme une extension dans le cadre de la stabilisation en temps fini de la methode donnee par Sontag (1991) concernant la stabilisation des systemes affines. La methode peut etre etendue pour traiter aussi bien le cas des systemes lineaires certains qu'incertains. Certains exemples montrent les potentialites de la theorie ainsi que la simplicite de l'approche proposee.

Finite-time stabilization of nonlinear affine systems

We consider the finite time stability and stabilization of linear systems described in continuous time. First, we provide a condition for the stability over time using the state transition matrix standard. Then we give conditions to design a state feedback control that stabilizes the system over time. In some cases where there is uncertainty in the system model, the previous conditions are extended to a certain class of uncertain systems. The considered uncertainties are the polytopic and norm bounded ones. To reveal the proposed approach, an application to the four tanks system was made.

/pdf/finite-time-stabilization-of-the-four-tanks-system-psdteugsqj.pdf

Chakib Ben Njima

Papers

Input fault detection and estimation using PI observer based on the ARX-Laguerre model

New Approach of Diagnosis by Timed Automata

PIO Output Fault Diagnosis by ARX-Laguerre Model Applied to 2 nd Order Electrical System

Finite-time stabilization of nonlinear affine systems

Finite Time Stabilization of the Four Tanks System: Extensions to the Uncertain Systems