Network management integrating SNMP/CMIP protocol implementations

In this paper, a new approach to integratingSNMP andCMIP protocols in a network management system is introduced. It is based on the use of proxy systems allowing to integrate SNMP network management agents in a general network management framework based onCMIP. The system architecture for marrying the protocols is first presented. Then the key features of a new protocol gateway implementing the proxy function are described, with emphasis on the explanation of theSNMP/CMIP mapping algorithm and the threshold/event reporting functions.     

Preparation,characterization and wear behaviour of TiNx-coated cermets obtained by plasma-enhanced chemical vapour deposition

Commercial cermet inserts were coated with titanium nitride by plasma-enhanced chemical vapour deposition (PECVD) using a pulsed direct current (d.c.) glow discharge. The influence of the coating parameters on the deposition rate, on the layer composition, on the layer-substrate interface, on the structure and on the microhardness of the layers was investigated for deposition temperatures in the range 500–700 °C. The adhesive strengths, and some mechanical properties, of the coated cermets were characterized by scratch tests, by friction wear investigations and by measurement of the transverse rupture strength. The wear behaviour was examined in the cutting tests. It was found that TiN _x -coatings deposited with a sufficiently high deposition rate and plasma power density have a low oxygen and chlorine content and that they are nearly stoichiometric. The layers usually have a columnar structure with a 200 texture. A granular, equiaxed structure was observed within a small range of deposition conditions. In interrupted and continuous turning tests with steel and grey cast iron, a high cutting performance of the coated inserts, which depended on the coating thickness and on the deposition temperature, was achieved.     

Licence de brevet et partage du risque: Résultats de simulation

Annals of Telecommunications - Le partage du risque est une des raisons les plus souvent avancées pour expliquer les clauses financières des contrats de licence. Pour autant, aucune...     

State of the Art of Molecular Visualization in Immersive Virtual Environments

Visualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head-mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.     

Using I2P (Invisible Internet Protocol) Encrypted Virtual Tunnels for a Secure and Anonymous Communication in VANets: I2P Vehicular Protocol (IVP)

Wireless Personal Communications - The Invisible Internet Project (I2P) as a secure protocol uses robust mechanisms and strong algorithms to reinforce the security and the anonymity of the...     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

Anticipating Cutoff Diameters in Deterministic Lateral Displacement (DLD) Microfluidic Devices for an Optimized Particle Separation

Deterministic lateral displacement (DLD) devices enable to separate nanometer to micrometer‐sized particles around a cutoff diameter, during their transport through a microfluidic channel with slanted rows of pillars. In order to design appropriate DLD geometries for specific separation sizes, robust models are required to anticipate the value of the cutoff diameter. So far, the proposed models result in a single cutoff diameter for a given DLD geometry. This paper shows that the cutoff diameter actually varies along the DLD channel, especially in narrow pillar arrays. Experimental and numerical results reveal that the variation of the cutoff diameter is induced by boundary effects at the channel side walls, called the wall effect. The wall effect generates unexpected particle trajectories that may compromise the separation efficiency. In order to anticipate the wall effect when designing DLD devices, a predictive model is proposed in this work and has been validated experimentally. In addition to the usual geometrical parameters, a new parameter, the number of pillars in the channel cross dimension, is considered in this model to investigate its influence on the particle trajectories.     

Comparison of the identification performance of conventional FEM updating and integrated DIC

Full‐field identification methods are increasingly used to adequately identify constitutive parameters to describe the mechanical behavior of materials. This paper investigates the more recently introduced one‐step method of integrated digital image correlation (IDIC) with respect to the most commonly used two‐step method of finite element model updating (FEMU), which uses a subset‐based DIC algorithm. To make the comparison as objective as possible, both methods are implemented in the most equivalent manner and use the same FE model. Various virtual test cases are studied to assess the performance of both methods when subjected to different error sources: (1) systematic errors, (2) poor initial guesses for the constitutive parameters, (3) image noise, (4) constitutive model errors, and (5) experimental errors. Results show that, despite the mathematical similarity of both methods, IDIC produces less erroneous and more reliable results than FEMU, particularly for more challenging test cases exhibiting small displacements, complex kinematics, misalignment of the specimen, and image noise. Copyright © 2015 John Wiley & Sons, Ltd.     

Achieving robust design through statistical effect screening

This work proposes a method for statistical effect screening to identify design parameters of a numerical simulation that are influential to performance while simultaneously being robust to epistemic uncertainty introduced by calibration variables. Design parameters are controlled by the analyst, but the optimal design is often uncertain, while calibration variables are introduced by modeling choices. We argue that uncertainty introduced by design parameters and calibration variables should be treated differently, despite potential interactions between the two sets. Herein, a robustness criterion is embedded in our effect screening to guarantee the influence of design parameters, irrespective of values used for calibration variables. The Morris screening method is utilized to explore the design space, while robustness to uncertainty is quantified in the context of info‐gap decision theory. The proposed method is applied to the National Aeronautics and Space Administration Multidisciplinary Uncertainty Quantification Challenge Problem, which is a black‐box code for aeronautic flight guidance that requires 35 input parameters. The application demonstrates that a large number of variables can be handled without formulating simplifying assumptions about the potential coupling between calibration variables and design parameters. Because of the computational efficiency of the Morris screening method, we conclude that the analysis can be applied to even larger‐dimensional problems. (Approved for unlimited, public release on October 9, 2013, LA‐UR‐13‐27839, Unclassified.) Copyright © 2015 John Wiley & Sons, Ltd.     

Noncontact Sonic NDE and Defect Imaging Via Local Defect Resonance

A selective acoustic activation of defects based on the concept of local defect resonance enables to enhance considerably the intensity of defect vibrations and makes it possible to reduce the input acoustic powers to the levels permissible for noncontact nondestructive inspection. Since for cm-size defects in composite materials, the LDR frequencies lie in the low kHz-range, the resonant noncontact activation shifts to an audible frequency range and can be provided by conventional sonic equipment. In this paper, the feasibility of the resonant noncontact inspection is validated for the most “problematic” methodologies of nonlinear, thermosonic and shearosonic NDE that usually require an elevated acoustic power and, therefore, a reliable contact between the specimen and the transducer. In contrast, the noncontact versions developed employ commercial loudspeakers which can simultaneously insonify large areas and be applied for a contactless sonic inspection of different materials and various scale components.