A flexible modular master programme in technology developped whithin a Tempus Project

In today’s competitive industry and in view of recent economic turbulences new frontiers of challenges emerge that require new educational paradigms accompanied by new tools and methodologies applicable to all aspects of engineering areas including the functional and organizational aspects. In accordance with the objectives stipulated by the Council of European Union work programme on the future of education and training, a Tempus project (2010-2013) has been mounted to develop a novel model for modular programmes to be used in education of technology specialities at master level. The model is implemented in manufacturing technology and management area and has general applicability for technology education in several fields. The main feature of this project consists in flexibility, adaptability, dynamic interactivity while consolidating theoretical and practical skills. MasTech is the name of a flexible modular master two-year programme in technology being developed according to the Bologna process that is to be adapted to the particular conditions of the universities in Algeria, Morocco and Tunisia. Three European Universities (Sweden, Germany, France) are involved in the project. This paper introduces MasTech and describes the different steps that have been followed to develop the master programme taking into account both academic and industrial needs and priorities. Results are expressed in terms of a professional master programme that has been submitted for accreditation.TEMPUS - MASTECH -2010 - 3369 / 001 - 00

Islamic Private Equity

The Islamic private equity (IPE) market has grown dramatically over the last few years. There are some similarities between venture capital (VC) and some traditional methods in Islamic financing. In medieval Islamic societies it is hard to pinpoint the starting of IPE but there were partnership arrangements similar to those practiced in conventional private equity (PE). But both academicians and professionals argue that the VC activity started in 1946 when General Doriot, a French born and a Harvard educated businessman, established the American Research and Development Corporation (ARDC). It grew in Silicon Valley in the 1970s. Intel and Microsoft are amongst the most famous projects financed through VC. Without the intervention of venture capitalists none of these would have seen light of day or at least achieved such a spectacular rate of development in such a short period (Queyrel, 2006).Islamic Finance, Islamic banks, Shari'ah compliance

Merging Bond Graph and Signed Directed Graph to improve FDI procedure

The Fuel Cell (FC) is an ideal electrical powersource. However, FC stacks and even more FC systems are vulnerable to faults (such as water flooding and membrane drying) that can cause the disruption or the permanent damage. To guarantee the safe operation of the FC systems, it is necessary to use systematic techniques to detect and isolate faults for the purpose of diagnosis. The problematic for the model-based Fault Detection and Isolation (FDI) of fuel cell is that the model is complex because of coupling multiple physical domains (electrochemical, electrical, thermofluidic...). This is why, we propose in this paper, the exploitation of the behavioral and structural properties of the Bond Graph (BG) as a multi-domain power exchange and unified graphical modeling language for qualitative analysis of monitoring ability (using Signed Directed Graph properties). This is obtained after generation of the fault indicators from one part, and by dealing with an automatically built Signed Directed Graph (SDG) of the system, from another part. By combining qualitative method (based on Signed Graph) and quantitative method (fault indicator generation) using only one representation, an innovative approach to perform (single and multiple faults) diagnosis is proposed. The proposed contribution is illustrated by an application to a Proton Exchange Membrane Fuel Cell (PEMFC)

Programme Flexible de Master en Technologies de Production

Un projet Tempus (2010-2013) entre 9 universités (trois Européennes et six Maghrébines) a conduit au montage d’un programme d’enseignement de Master en technologie de production mécanique (MASTECH), modulaire et enseigné en langue anglaise est présenté. Les objectifs de ce nouveau programme, le retour d’expérience ainsi que les contenus des matières sont présentés avec la spécificité de la mise au point d’un outil pédagogique d’enseignement (Package Education) qui facilitera le déroulement des enseignements, limitera les variances entre les partenaires maghrébins et devra permettre la mobilité des étudiants et des enseignants. Dans la même expérience, les points-clés de réussite de ce programme commun sont exposés. Il s’agit notamment de l’acquisition de matériels pédagogiques, de déroulement d’écoles d’été, de validations des contenus des modules pédagogiques, de transfert auprès des enseignants, des conventions avec des industriels locaux, de la convention entre partenaires maghrébins, du support pédagogique des universités européennes et des prévisions de mobilité. Les difficultés relatives à la coordination des différentes facettes du projet et à la langue d’enseignement sont aussi discutées. Le démarrage des enseignements élaborés par ce programme dans les cursus de formation est prévu pour septembre 2013 sous forme d’un master professionnalisant.programme européen TEMPUS 2010 - 3369 / 001 - 00

Einsatz von Text-Mining-Algorithmen bei der Realisierung eines semi-automatischen Erschließungswerkzeuges im Projekt META-AKAD

Ähnlich wie Data-Mining die Analyse strukturierter numerischer Daten bezeichnet, beschreibt der Begriff des Text-Mining eine Menge von Methoden zur (halb-) automatischen Auswertung großer Mengen natürlichsprachlicher Texte. Das Gebiet des Text Mining umfaßt vielfältige Methoden zur Extraktion von Informationen aus natürlichsprachlichen Texten. In diesem Gebiet bzw. in dem Forschungsgebiet der Namenserkennung und Namensklassifikation werden viele Algorithmen vorgestellt und dokumentiert. Bei diesen Namenerkennungssystemen, die Teile der sogenannten "Information-Extraction-Systems" sind, werden hauptsächlich zwei Ansätze verwendet und zwar der sogenannte "Knowledge-Engineering-Approach" und "Automatic-Training-Approach

SBG for Health Monitoring of Fuel Cell System

To guarantee the safe operation of the Fuel Cell (FC) systems, it is necessary to use systematic techniques to detect and isolate faults for diagnosis purposes. The problematic for Fault Detection and Isolation (FDI) model-based of fuel cell consists in that such system is bad instrumented, its model is complex (because of coupling of multi-physical phenomena such as electrochemical, electrical, thermo fluidic…) and the numerical values related to it are not always known. This is why qualitative model (based on existence or not of the links between variables and the relations) is well suited for fuel cell diagnosis. In this paper, we propose a new graphical model (named Signed Bond Graph) allowing to combine both qualitative and quantitative features for health monitoring (in terms of diagnosis and prognosis) of the fuel cell. The innovative interest of the presented paper is the use of only one representation for not only structural model but also diagnosis of faults which may affect the fuel cell. The developed theory is illustrated by an application to a Proton Exchange Membrane Fuel Cell (PEMFC).

A Bond Graph Modeling for Health Monitoring and Diagnosis of the Tennessee Eastman Process

Data-driven fault detection and diagnosis approaches are widely applicable in many real-time practical applications. Among these applications, the industrial benchmark of Tennessee Eastman Process (TEP) is widely used to illustrate and compare control and monitoring studies. However, due to the complexity of physical phenomena occurring in such process, no model-based approach for fault diagnosis has been developed and most of the diagnosis approaches applied to the TEP are based on experiences and qualitative reasoning that exploit the massive amount of available measurement data. In this paper, we propose to use the Bond Graph formalism as a multidisciplinary energetic approach that enables to obtain a graphical nonlinear model of the TEP not only for simulation purposes but also for monitoring tasks by generating formal fault indicators. In this study, the proposed BG model is validated from the experiment data and the problem of the TEP model design is hence overcome. A Bond Graph Modeling for Health Monitoring and Diagnosis of the Tennessee Eastman Process (PDF Download Available). Available from: https://www.researchgate.net/publication/314032904_A_Bond_Graph_Modeling... [accessed May 30, 2017]

Robust fault decision : Contribution to Omni directional Mobile Robot

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems and avoiding the execution of an unsafe behaviour. This chapter deals with Robust Decision Making (RDM) for fault detection of electromechanical systems by combining the advantages of Bond Graph (BG) modeling and Fuzzy logic reasoning. A fault diagnosis method implemented in two stages is proposed. In the first stage, the residuals are deduced from the BG model allowing the building of a Fault Signature Matrix (FSM) according to the sensitivity of residuals to different parameters. In the second stage, the result of FSM and the robust residual thresholds are used by the fuzzy reasoning mechanism in order to evaluate a degree of detectability for each set of components. Finally, in order to make robust decision according to the detected fault component, an analysis is done between the output variables of the fuzzy system and components having the same signature in the FSM. The performance of the proposed fault diagnosis methodology is demonstrated through experimental data of an omni directional robot. - See more at: http://www.eurekaselect.com/102039/chapter/robust-fault-decision%3A-appl...

Model-based approach for fault diagnosis using set-membership formulation

This paper describes a robust model-based fault diagnosis approach that enables to enhance the sensitivity analysis of the residuals. A residual is a fault indicator generated from an analytical redundancy relation which is derived from the structural and causal properties of the signed bond graph model. The proposed approach is implemented in two stages. The first stage consists in computing the residuals using available input and measurements while the second level leads to moving horizon residuals enclosures according to an interval consistency technique. These enclosures are determined by solving a constraint satisfaction problem which requires to know the derivatives of measured outputs as well as their boundaries. A numerical differentiator is then proposed to estimate these derivatives while providing their intervals. Finally, an inclusion test is performed in order to detect a fault upon occurrence. The proposed approach is well suited to deal with different kinds of faults and its performances are demonstrated through experimental data of an omni-directional robot