Anas Kamoun

Development of a WSN integrated weather station node for an irrigation alert program under Tunisian conditions

Recent advances in sensor and wireless radio frequency (RF) technologies offer vast opportunities for development and application of sensor systems for agriculture. It is the concept of precision agriculture, where Wireless Sensor Networks (WSN) is playing an important part in the handling and managing of water resources for irrigation, in understanding the changes in the crops to assess the optimum point for harvesting, in estimating fertilizers requirements and to predict crop performance more accurately. The field micro-climate monitoring is important for crop water requirements determination. Traditional weather stations are expensive, on demand data collection and not flexible for new sensors addition or WSN integration. This paper aims at the design of a custom low-cost Weather Station hardware and software node (WS-node) for irrigation scheduling in developing countries context. These sensors data are transmitted to the DSS (Decision Support System) in a Wireless Sensor Networks (WSN) infrastructure, taking into account all particularities of such environments constraints. Cost factors, field conditions, farmer knowledge, devices availability are the most important ones.

E.O.G classification based on fuzzified symbolic representation

This work is part of a study for a 3D navigation task of disabled person in virtual environment. The hardware used is an ocular command device called Cyclope which is based on the measurement and analyze of biological signals that vary with eye movements (Electro Oculo-Graphic, EOG). This paper presents a method to represent and classify EOG signal. The goal is to distinguish Ocular Command Movement which can be detected from pattern of EOG signals. Then, an adapted representation and classification of these patterns allows identifying a particular class of OCM. The proposed method extracts semi-qualitative linear episodes from biological signals. Two membership functions are implemented to transform the quantitative information in the linear episodes to a purely symbolic representation. Finally, the symbolic data is classified with qualitative similarity index.

Real time algorithm based on time series data abstraction and hybrid bond graph model for diagnosis of switched system

In this paper, we propose a real time algorithm to realize a diagnosis of switched systems for abrupt parametric faults. This algorithm is based on interaction between a Qualitative Diagnosis (QD) and a monitoring component that performs a Qualitative Trend Analysis (QTA) of residual signals generated from Bond Graph (BG) elements called residual sinks. The QTA is applied in order to on-line detect change in the mean of residual signal based on combination of Piecewise Aggregate Approximation (PAA) with Page-Hinkley Test (PHT). The QD procedure is performed in two stages. In the first off-line stage, Symbolic Fault Signature Matrix (SFSM) is generated from a Parameterized Temporal Causal Graph (PTCG). The PTCG is valid for all system modes and deduced from a unified Hybrid Bond Graph (HBG) model by converting its elements into node and labeled edge. Each entry in the SFSM matrix gives the residual symbolic signature which corresponds to the lower-order signature predicted using the PTCG model by propagating initial deviation from the fault parameter in the label of edge to the residual node. In the second on-line stage, trend extraction by linear regression is triggered after change detection in order to estimate the lower order time-derivative symbol for each residual sinks. Subsequently, we propose a stepwise similarity measure for fault isolation task. The functioning of this approach is illustrated in simulating with a switched quarter-car active suspension system. Hybrid BG extends the ability of BG to model switched systems using intuitive representation for structural discontinuities.HBG is used as a unified tool for a combination of Quantitative-Qualitative FDI appraoch that numerically evaluate residual signalsQualitative Trend Analysis and Page Hinkley Test are used for a real time symbolic signature estimation of residual signalsCausal Graph deduced from HBG and parameterized by the state of the controlled junction is used for symbolic prediction.

Linear algebraic formalism for state estimation of labeled Petri Net

In this paper we present an algebraical approach for the state estimation of discrete event systems, partially observed and modeled by labeled Petri nets. Our estimation approach is based on the on-line observation of firing occurrences of some transitions to determine the set of all possible actual sequences and markings. This approach has been used to minimize the computation time and space for the state estimation using an off-line procedure.

WITHDRAWN: Numerical residuals in model based single fault detection and isolation of hybrid dynamic systems.

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Modeling and simulation of thermofluid system using Hybrid Bond Graph

This paper presents a component oriented modeling environment for building hybrid dynamic models of physical system. The modeling environment is created using the Generic Modeling Environment (GME). The core of the modeling language itself is a hybrid extension of the bond graph modeling language. The advantages of an object-oriented approach to physical system modeling combined with the advanced features of GME foe managing model complexity are illustrated by building a library of thermofluid system components. A simulation model can be automatically generated from the physical system model using a model translator. As an example application we use the component library to build the model of a thermofluid system with autonomous hybrid behavior, and illustrate with a simulation example.

Graphical method for diagnosis of hybrid dynamic systems

This paper presents a graphical method used for Fault Detection and Isolation (FDI) of hybrid dynamic systems. The developed method is based on the notion of Global Analytical Redundancy Relations (GARRs) from the hybrid bond graph model, which describes the system behavior at all of its operating modes. We use a hybrid tank system to illustrate the concept of the GARR.

A qualitative approach to single fault isolation in switching systems

Hybrid Bond Graph (HBG) is a bond graph-based modeling approach which provides an effective tool not only for dynamic modeling but also for fault detection and isolation (FDI) of switching systems. Efficient algorithms exist for fault detection and isolation based on functional redundancy. In a qualitative approach, this redundancy can be captured by a temporal causal graph (TCG), a directed graph that may include temporal information. To avoid the combinatorial explosion when using a bank of TCGs in parallel, causal paths are parameterized by the state of local switches. This paper presents a qualitative approach to diagnose a hybrid system. As an example we use a coupled three tank system with autonomous hybrid behaviors, and illustrate this through a simulation.