2011
In this paper, analytical models of drain current and small signal parameters for undoped symmetric Gate Stack Double Gate (GSDG) MOSFETs including the interfacial hot-carrier degradation effects are presented. The models are used to study the device behavior with the interfacial traps densities. The proposed model has been implemented in the SPICE circuit simulator and the capabilities of the model have been explored by circuit simulation example. The developed approaches are verified and validated by the good agreement found with the 2D numerical simulations for wide range of device parameters and bias conditions. GSDG MOSFET design and the accurate proposed model can alleviate the critical problem and further improve the immunity of hot-carrier effects of DG MOSFET-based circuits after hot-carrier damage.
In the present work, a comprehensive drain current model including the interfacial hot-carrier degradation effects for undoped symmetric gate stack double gate (GSDG) MOSFET and the expressions of transconductance and drain conductance have been obtained. Exploiting this new device model, we have found that the incorporation of a high-k layer between oxide region and gate metal leads to drain current enhancement, improved output conductance, increased transconductance parameter and enhanced interfacial hot-carrier immunity. The proposed model has been validated by comparing the analytical I-V characteristics with 2-D numerical results. The obtained results may provide a theoretical basis and physical insights for multigate MOSFET-based circuits design including the hot-carrier degradation effects. (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an manufacturing systems. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker , cement factory of Ain Touta " Batna, Algeria ".
This work appears in man-machine interface. Our goal is to study the integration of Arabic language in Mobile Phone, in order to achieve a man-machine interface Arabic. The correct display of Arabic character is essential in a MMI. Since Arabic characters change their forms according to their position in a word, then it is necessary to make a contextual analysis on every word, to find the correct form of each character. The transformation of two or more characters in one form, demand special treatment, as in the case of Arabic ligature LAM-ALEF. The Arabic language has a different direction of writing in relation to other languages embedded in mobile phone, which requires finding an algorithm that provides a bidirectional display of SMS messages. These messages may contain characters from different direction, from right to left, left to right or characters that have no direction. It allows you to make the message understandable.
We describe in this paper an overview of artificial immune system algorithms to solve the classification problem in industrial monitoring. We present artificial immune system algorithms, starting with the negative selection that happens to be a rich source of inspiration. We also, detail the clonal selection algorithm, which is based on the clonal selection theory. Finally, we detail other algorithms based of agent including the immune system and dendritic cell algorithm. In the end, we summarize the differences and similarities of the works discussed and we conclude on the prospects related to the approach of the algorithms of artificial immune systems for industrial monitoring to solve the classification problem. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved
Dans cet article, nous présentons un nouvel
algorithme pour réduire la dimension de vecteur d’état de
fonctionnement d’un système industriel. Notre algorithme
permet de sélectionner un sous-ensemble de paramètres qui
offre une détection plus rapide de dysfonctionnement et une
bonne qualité de classification. Cet algorithme est basé sur le
comportement observé chez les fourmis réelles. Nous montrons
ici que l’émergence des déplacements et les interactions des
fourmis permet de trouver un ensemble réduit de paramètres
qui caractérisent le fonctionnement d’un système industriel
dynamique et complexe. L’algorithme offre aussi la possibilité
d’utiliser des bases de données de grandes tailles. Les
expériences effectuées sur les bases de données Iris et Vehicle
montrent que notre algorithme fournit de très bons résultats.
MOTS-CLES: Colonie de fourmis, Classification, Diagnostic
industriel, Sélection de paramètres, Système complexe et dynamique.
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria
In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process. Copyright © 2011 Praise Worthy Prize S.r.l. -All rights reserved
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria
In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process. Copyright © 2011 Praise Worthy Prize S.r.l. -All rights reserved.
Dans cet article, nous présentons un nouvel algorithme pour réduire la dimension de vecteur d’état de fonctionnement d’un système industriel. Notre algorithme permet de sélectionner un sous-ensemble de paramètres qui offre une détection plus rapide de dysfonctionnement et une bonne qualité de classification. Cet algorithme est basé sur le comportement observé chez les fourmis réelles. Nous montrons ici que l’émergence des déplacements et les interactions des fourmis permet de trouver un ensemble réduit de paramètres qui caractérisent le fonctionnement d’un système industriel dynamique et complexe. L’algorithme offre aussi la possibilité d’utiliser des bases de données de grandes tailles. Les expériences effectuées sur les bases de données Iris et Vehicle montrent que notre algorithme fournit de très bons résultats.
