Semi-analytical hybrid approach for modelling wave motion excited by a piezoelectric transducer in a laminate with multiple cracks

A semi-analytical hybrid approach is presented here to simulate the dynamic behaviour of a multi-layered elastic waveguide with a system of delaminations and a piezoelectric transducer mounted on the surface of the waveguide. The proposed hybrid approach combines the advantages of the frequency domain spectral element method, which is applied to discretize a complex-shaped piezoelectric structure, and the boundary integral equation method employed to simulate wave propagation in multi-layered waveguide with multiple delaminations. The proposed method is applicable to the multi-parameter analysis of the phenomena related to elastic wave scattering and excitation. The advantages of the presented extended semi-analytical hybrid approach method along with the results of the parametric analysis of wave propagation in the considered structures are discussed

Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

Hybrid-Vehcloud: An Obstacle Shadowing Approach for VANETs in Urban Environment

Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due to obstacle shadowing regions with high vehicle densities, which leads to frequent disconnection problems and blocks radio wave propagation between vehicles. Previous researchers used multi-hop, vehicular cloud or roadside infrastructures to solve the routing issue among the vehicles, but they suffer from significant packet delays and frequent packet losses arising from obstacle shadowing. We proposed a vehicular cloud based hybrid technique called Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. The novelty of our approach lies in the fact that our proposed technique dynamically adapts between obstacle shadowing and non-obstacle shadowing regions. Simulation based performance analysis of Hybrid-Vehcloud showed improved performance over Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high vehicle densities

Analysis of Guided Wave Propagation in a Multi-Layered Structure in View of Structural Health Monitoring

Guided waves (GW) are of great interest for non-destructive testing (NDT) and structural health monitoring (SHM) of engineering structures such as for oil and gas pipelines, rails, aircraft components, adhesive bonds and possibly much more. Development of a technique based on GWs requires careful understanding obtained through modelling and analysis of wave propagation and mode-damage interaction due to the dispersion and multimodal character of GWs. The Scaled Boundary Finite Element Method (SBFEM) is a suitable numerical approach for this purpose allowing calculation of dispersion curves, mode shapes and GW propagation analysis. In this article, the SBFEM is used to analyse wave propagation in a plate consisting of an isotropic aluminium layer bonded as a hybrid to an anisotropic carbon fibre reinforced plastics layer. This hybrid composite corresponds to one of those considered in a Type III composite pressure vessel used for storing gases, e.g., hydrogen in automotive and aerospace applications. The results show that most of the wave energy can be concentrated in a certain layer depending on the mode used, and by that damage present in this layer can be detected. The results obtained help to understand the wave propagation in multi-layered structures and are important for further development of NDT and SHM for engineering structures consisting of multiple layers

A hybrid time-frequency domain approach for numerical modeling of reciprocating compressors

In the reciprocating compressor field, strong attention is paid to the study of pressure wave propagation in the discharge and suction pipelines. Oscillating pressure waves may lead to mechanical vibrations and failures and affect the machine performance. For this reason, an accurate analysis of the acoustic response of suction and discharge pipelines in a reciprocating compressor plant is of great interest. By solving a linear system of equations, the acoustic domain of a piping system can be easily determined. Usually, the acoustic pulsation analysis of the pipelines is carried out without considering the interaction between the machinery and the pipelines. Consequently, the reciprocal interaction between the compressor and the pipelines can not be considered. The aim of this work is to perform a fluid-dynamic analysis of the full compressor-pipelines system. For this purpose, a hybrid time-frequency domain approach is adopted. The reciprocating compressor thermodynamic cycle is simulated with a 0D timedomain model, while the pressure wave propagation in the pipelines is modelled by mean of a transfer matrix approach in the frequency domain. This analysis allows one to take into account the mutual interaction between the compressor and its pipelines by using the FFT and the Inverse FFT alternatively. The methodology was assessed by comparing the results of the simulation of a test case performed with both the hybrid approach and a commercial 1D code

Protection against Code Obfuscation Attacks based on control dependencies in Android Systems

International audienceIn Android systems, an attacker can obfuscate an application code to leak sensitive information. TaintDroid is an information flow tracking system that protects private data in smartphones. But, TainDroid cannot detect control flows. Thus, it can be circumvented by an obfuscated code attack based on control dependencies. In this paper, we present a collection of obfuscated code attacks on TaintDroid system. We propose a technical solution based on a hybrid approach that combines static and dynamic analysis. We formally specify our solution based on two propagation rules. Finally, we evaluate our approach and show that we can avoid the obfuscated code attacks based on control dependencies by using these propagation rules

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks