In this note, we present several characterizations for some distinguished classes of bounded Hilbert space operators (self-adjoint operators, normal operators, unitary operators, and isometry operators) in terms of operator inequalities.

The Equilibrium Voltage Step (EVS) technique has been used for extraction of depth and energy concentration profile of traps situated in the oxide of a lightly stressed metal-oxide-semiconductor (MOS) structure. This has been achieved up to the very near Si-SiO2 interface. The results are discussed and compared with those obtained using charge pumping (CP) technique. A good agreement is achieved between the trap densities extracted using the two methods even though differences in the shape of the profiles can be observed. The results also very well agree with those published previously using current deep level transient spectroscopy (C-DLTS).

In this paper, a robust active fault tolerant control (AFTC) scheme is proposed for induction motor drives (IMD) via input-output linearization control (IOLC) and nonlinear observer. In order to estimate the states and to reconstruct the faults, two different observers are used; a Luenberger observer (LO) and an extended kalman filter (EKF). Further we introduce feedback linearization strategy by choosing the output function as the rotor speed and flux square. To provide a direct comparison between these FTCs schemes, the performance is evaluated using the control of IMD under failures, variable speed, and variable parameters, finally the obtained results show that the proposed controller with the proposed observers provides a good trajectory tracking, and these schemes are robust with respect to faults, parameter variations, and external load disturbances for induction motor drive system.

This work is mainly on the detection of the bulk acoustic waves (BAWs), where we have compared two Lithium Niobate cuts (Y-Z and Y-X), during the propagation of the acoustic microwaves in the piezoelectric LiNbO3-substrate. In this paper, we have used the classification by Probabilistic Neural Network (PNN) as a tool for numerical analysis in which we classify all the values of the real and imaginary parts of the attenuation coefficient for the propagation velocity. This analysis can allow us to demonstrate the best possibility in utilization for the BAW generation. This study will be very interesting in modeling and realization of acoustic wave devices (ultrasound) based on the propagation of acoustic microwaves.

Within the majority of contexts, it is persons that are considered to be competent or incompetent. However, in many cases it is the performance of groups and teams that is most important. This implies a concept of collective competence that integrates the set of skills in a group. In addition, the collective competence construction process is also enriched through collaboration which implies exchanges, confrontations, negotiations and interpersonal interactions. This paper presents our CSCW-based approach supporting collective competence construction. As a case of study, the industrial maintenance workspace is fundamentally a collaborative context. Our contribution in this area led us first, to analyse the related task in order to highlight collaborative maintenance vital needs and design the appropriate required group awareness supports which will be used to support collective competence. Finally, the experimentation study identifies the highly effective group awareness tools.

A continuous and accurate model based on the two-dimensional (2D) potential solution of a tunnel field-effect transistor (TFET) with undoped vertical surrounding-gate (VSG) structure is proposed. Both ambipolarity and dual modulation effects are included to obtain a more accurate analytical model, whose validity is demonstrated by comparison with two-dimensional numerical simulations using ATLAS-2D. The continuity of the proposed model enables extraction of analog/radiofrequency (RF) parameters and device figures of merit. Moreover, the effect of introducing a high-κ layer on the gate oxide in improving the behavior of the VSG-TFET is explored for use in high-performance analog/RF applications. The proposed continuous analytical model can be easily implemented in commercial simulators to study and investigate VSG-TFET-based nanoelectronic circuits.

Purpose The purpose of this paper is to employ lessons learned from the industrial accidents in Skikda refinery during the period from 2005 to 2016 as input data for the numerical simulation of risk consequences to identify the exposed areas to the various effects of industrial accidents. Design/methodology/approach In order to assess how the lessons learned can contribute to modeling the accidents effects in the refining activities, this paper presents a combined statistical/dynamic approach that combines two main tools, namely, lessons learned from petroleum refining in Algeria and Areal Locations of Hazardous Atmospheres software. Findings The results showed that fire is the most frequent accident at Skikda refinery that is mainly caused by equipment failures with a frequent involvement of crude oil and LNG. The NO2 toxic effects are unacceptable. This means that in the case of a similar accident, the entire population will be exposed to an intolerable concentration of NO2. Therefore, people must be relocated to a safer place. The results indicate that the concentration threshold can be met beyond the distance of 1 km. Research limitations/implications Due to the economic importance of Skikda refinery and the absence of data related to the accidents in the refineries of Algiers, Arzew and Hassi Messaoud, this study is limited to the statistical analysis of accidents related to Skikda refinery. Practical implications This approach makes the risk assessment more practical and effective for the appropriate utilization of safety barriers and for the whole decision-making process. Originality/value This work presents a review paper of accidents that occurred in the oil-refining sector in Algeria, whose objective is learning lessons from past accidents history, by identifying their immediate causes and effects on personnel, equipment and environment in order to propose prevention measures. The novelty of this work is highlighted by the fact that this statistical analysis of oil and gas refining accident is realized for the first time in Algeria. This is due to the difficulty of obtaining data on accidents in the Algerian refining sector; for this reason, the authors have limited the study to the Skikda refinery.

The declination of the dependability requirements of the complex systems is one of the most critical processes of systems conception as well as systems engineering. We consider that these dependability requirements must be globally considered at the complete system level if they are to be respected before being allocated on the components of the system. Our approach is based on a three-dimensional decomposition model of the system whose purpose is to describe the way in which the system operates in order to deduce its dysfunction that serves as a basic support for the declination of the dependability requirements. The proposed method is applied to a nitric acid cooling system before being introduced into a chemical reactor.

This article describes how fault tolerance is an essential issue for many WSN (Wireless Sensor Network) applications such as wildlife monitoring, battlefield surveillance and health monitoring. It represents a great challenge for researchers regarding to the characteristics of sensor nodes which are prone to failures due essentially to their limited resources. Faults may occur, not only when sensor nodes exhaust their energy, but also when the congestion phenomenon emerges, because of a high traffic in the network and limited storage capacity of the sensor nodes. In order to support fault tolerance in WSNs, the authors propose a new scheme which incorporates a link quality estimation algorithm and a congestion detection mechanism to enable nodes that present high quality links to be chosen for routing in a non-congested area in case of faults. Evaluations through simulations under NS2 show that our proposed protocol tolerates faults with a minimum cost relatively to HEEP protocol and improves network’s performances comparatively to other fault tolerant protocols such as EF-LEACH.

The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.