On optimal relaying strategies for VANETs over double Nakagami-m fading channels

Wireless Networks - Vehicular ad hoc networks (VANETs) are able to facilitate data exchange among vehicles and provide diverse data services. The benefits of cooperative communications are such as...     

On modeling of wave propagation in a thermally affected GNP-reinforced imperfect nanocomposite shell

Due to rapid development of process manufacturing, composite materials with porosity have attracted commercial attention in promoting engineering applications. For this regard, in this research wave propagation-thermal characteristics of a size-dependent graphene nanoplatelet-reinforced composite (GNPRC) porous cylindrical nanoshell in thermal environment are investigated. The effects of small scale are analyzed based on nonlocal strain gradient theory (NSGT). The governing equations of the laminated composite cylindrical nanoshell in thermal environment have been evolved using Hamilton’s principle and solved with the assistance of the analytical method. For the first time, wave propagation-thermal behavior of a GNPRC porous cylindrical nanoshell in thermal environment based on NSGT is examined. The results show that by increasing the thickness, the effect of porosity on the phase velocity decreases. Another important result is that by increasing the value of the radius, the difference between the minimum and maximum values of the phase velocity increases. Finally, influence of temperature change, wave number, angular velocity and different types of porosity distribution on phase velocity are investigated using the mentioned continuum mechanics theory. As a useful suggestion, for designing of a GPLRC nanostructure should be attention to the GNP weight function and radius, simultaneously.

     

Information technology and e-business marketing strategy

The advancement of information technology and its benefits for organizations have made the field of e-business marketing competitive and successful although there have been failures as well. Failure happens due to the use of constant strategies while the e-business environment is dynamic and in such a context, strategy formulation and implementation is a challenge for e-business organization. In this research a questionnaire was designed by reviewing the literature and interviewing experts. Analyzing the collected data, the e-business key strategy factors such as technology, information systems, risk management and compliance were extracted. In the next stage, these key factors were presented as the e-business marketing strategy model. The results suggested that dissimilar dimensions of e-business strategy factors may not be equally conducive to different marketing strategies. In particular, risk management has an insignificant relationship with any of the marketing strategies; while high level of compliance is positively associated only with a focus strategy. IT governance is positively associated with price leadership and differentiation strategies but linked negatively with focus strategy.     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.     

Thermo-mechanical vibration analysis of sandwich beams with functionally graded carbon nanotube-reinforced composite face sheets based on a higher-order shear deformation beam theory

This article proposes a higher-order shear deformation beam theory for free vibration analysis of functionally graded carbon nanotube-reinforced composite sandwich beams in a thermal environment. The temperature-dependent material properties of functionally graded carbon nanotube-reinforced composite beams are supposed to vary continuously in the thickness direction and are estimated through the rule of mixture. The governing equations and boundary conditions are derived by using Hamilton's principle, and the Navier solution procedure is used to achieve the natural frequencies of the sandwich beam in a thermal environment. A parametric study is led to carry out the effects of carbon nanotube volume fractions, slenderness ratio, and core-to-face sheet thickness ratio on free vibration behavior of sandwich beams with functionally graded carbon nanotube-reinforced composite face sheets. Numerical results are also presented in order to compare the behavior of sandwich beams including uniformly distributed carbon nanotube-reinforced composite face sheets to those including functionally graded carbon nanotube-reinforced composite face sheets.     

Vibration analysis of parabolic shear-deformable piezoelectrically actuated nanoscale beams incorporating thermal effects

Thermoelectric-mechanical vibration behavior of functionally graded piezoelectric (FGP) nanobeams is first investigated in this article, based on the nonlocal theory and third-order parabolic beam theory by presenting a Navier-type solution. Electro-thermo-mechanical properties of a nanobeam are supposed to change continuously throughout the thickness based on the power-law model. To capture the small-size effects, Eringen's nonlocal elasticity theory is adopted. Using Hamilton's principle, the nonlocal governing equations for the third-order, shear deformable, piezoelectric, FG nanobeams are obtained and they are solved applying an analytical solution. By presenting some numerical results, it is demonstrated that the suggested model presents accurate frequency results of FGP nanobeams. The influences of several parameters, including external electric voltage, power-law exponent, nonlocal parameter, and mode number on the natural frequencies of the size-dependent FGP nanobeams are discussed in detail. The results should be relevant to the design and application of the piezoelectric nanodevices.     

Orthonormal function parametrisation of model-predictive control for linear time-varying systems

It is well known that some practical difficulties are involved in the implementation of stabilising model predictive control for time-varying systems. In order to address the difficulty of computational load, this paper extends the orthonormal function method for model predictive control to linear time-varying systems. We provide sufficient conditions for a sub-optimal model predictive controller to be stabilising for a time-varying system. It is also shown that the orthonormal parametrisation method enables us to reduce the number of decision variables significantly and with a satisfactory performance. In addition, it is shown that orthonormality and, the called for, long prediction horizons are not necessary for stability. Examples are provided, illustrating the effectiveness of the method for linear time-varying systems.     

A Decision-Tree Scheme for Responding to Uncertainties in Microgrid Protection Coordination

In addition to the dynamic nature of microgrids, uncertainty in the proper operation of protection system and communication links are other challenges affecting the protection coordination of these networks. Therefore, in this paper, a new protection coordination plan based on decision tree for considering uncertainties in the topology of microgrid, protection system, and communication links is presented. The proposed method allows the adaptive protection to make global decisions and adopt the best strategy to clear faults depending on considered uncertainties. Since circuit breakers are the most prone to failure equipment in the protection system due to fault-caused stress, this paper models uncertainty in the protection system with uncertainty in the performance of circuit breakers. In order to consider uncertainty in circuit breakers and communication links, their probability of correct operation are not considered fixed but variable, respectively, proportional to the fault current flowing through the circuit breakers and the latency of communication links. The proposed plan was tested on a sample microgrid in DIgSILENT Power Factory. Results prove that using the proposed method, adaptive protection can establish an optimal sequence of strategies so that with the failure of each strategy, the best backup strategy is replaced given the uncertainties.     

A multilevel inverter based on cascade connection of submultilevel units with reduced switch count

In this paper, a multilevel inverter based on cascade connection of new submultilevel inverters is presented. The suggested submultilevel inverter is constructed using series connection of basic switching units. The proposed multilevel inverter uses fewer power switches in comparison with some similar topologies which results in reduction of switch gate drivers and also converter size and cost. The proposed multilevel inverter can be implemented in both symmetric and asymmetric configurations. The multilevel inverter configuration and operation principle are described in detail, and then, design methods of symmetric and asymmetric configurations are given. Determination of the optimal number of basic units and cascaded submultilevel inverters regarding criteria such as number of switches and total blocking voltage (TBV) of switches is studied. Power losses of the proposed multilevel inverter are calculated, and then, its symmetric and asymmetric configurations are compared with each other and also with similar cascaded multilevel inverters in various items. The validity of the suggested cascaded multilevel inverter is verified using both computer simulations and laboratory prototype implementation.     

A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain

The prevalence of the use of third-party logistics (3PL) providers is noticeable. The complexity of the relationships pertinent to 3PL is greater than that of any traditional logistics supplier relationships. Moreover, they can be considered as truly strategic alliances. The use of the mentioned relationships to increase the flexibility of the organization to address the rapid changes occurring in market conditions has become popular while these relationships concentrate on the core competencies as well as the development of long-term growth strategies. A good number of studies have examined the selection of service providers. With respect to the selection of the service providers, the most recent studies approved the better performance of neural networks in comparison with the conventional methods to provide a solution for the real-world engineering problems, one of the sociopolitically inspired optimization strategies named imperialist competitive algorithm (ICA) is used. In order to select the 3PL, integration of the support vector regression (SVR) and self-adaptive ICA (SAICA) has offered a novel model, in which SAICA is utilized to adjust the parameters of the SVR. The suggested model is applied for cosmetics production. Moreover, the comparison of the suggested model and back-propagation neural networks, pure SVR, and ICA–SVR is presented. Higher estimation accuracy is achieved as the results of the proposed model reveal, which leads to the effective prediction.