Composite hierarchical antidisturbance control for a class of discrete‐time stochastic systems

In this paper, antidisturbance control and estimation problem are discussed for a class of discrete‐time stochastic systems with nonlinearity and multiple disturbances, which include the disturbance with partially known information and a sequence of random vectors. A disturbance observer is constructed to estimate the disturbance with partially known information. A composite hierarchical antidisturbance control scheme is proposed by combining disturbance observer and H_∞ control. It is proved that the 2 different disturbances can be rejected and attenuated, and the corresponding desired performances can be guaranteed for discrete‐time stochastic systems with known and unknown nonlinear dynamics, respectively. Simulation examples are given to demonstrate the effectiveness of the proposed scheme.     

Global output feedback control for a class of nonlinear systems with unknown homogenous growth condition

This paper investigates the problem of global control for a class of nonlinear systems via output feedback. The system nonlinearities satisfy the homogenous growth condition with unknown growth rate. First, a homogenous observer is constructed for estimating the system state. Then, two novel dynamic gains are presented to deal with the unknown growth rate. Subsequently, by adding a power integrator technique, a dynamic output feedback controller is designed to guarantee that all the signals of the closed‐loop system are bounded and the system states globally converge to origin. Finally, an example is provided to illustrate the validity of the proposed control scheme.     

Prescribed performance adaptive neural output control for a class of switched nonstrict‐feedback nonlinear time‐delay systems: State‐dependent switching law approach

This paper investigates the tracking problem for a class of uncertain switched nonlinear delayed systems with nonstrict‐feedback form. To address this problem, by introducing a new common Lyapunov function (CLF), an adaptive neural network dynamic surface control is proposed. The state‐dependent switching rule is designed to orchestrate which subsystem is active at each time instance. In order to compensate unknown delay terms, an appropriate Lyapunov‐Krasovskii functional is considered in the constructing of the CLF. In addition, a novel switched neural network–based observer is constructed to estimate system states through the output signal. To maintain the tracking error performance within a predefined bound, a prescribed performance bound approach is employed. It is proved that by the proposed output‐feedback control, all the signals of the closed‐loop system are bounded under the switching law. Moreover, the transient and steady‐state tracking performance is guaranteed by the prescribed performance bound. Finally, the effectiveness of the proposed method is illustrated by two numerical and practical examples.     

DAWA: Defending against wormhole attack in MANETs by using fuzzy logic and artificial immune system

Mobile ad hoc networks (MANETs) are mobile networks, which are automatically outspread on a geographically limited region, without requiring any preexisting infrastructure. Mostly, nodes are both self-governed and self-organized without requiring a central monitoring. Because of their distributed characteristic, MANETs are vulnerable to a particular routing misbehavior, called wormhole attack. In wormhole attack, one attacker node tunnels packet from its position to the other attacker nodes. Such wormhole attack results in a fake route with fewer hop count. If source node selects this fictitious route, attacker nodes have the options of delivering the packets or dropping them. For this reason, this paper proposes an improvement over AODV routing protocol to design a wormhole-immune routing protocol. The proposed protocol called defending against wormhole attack (DAWA) employs fuzzy logic system and artificial immune system to defend against wormhole attacks. DAWA is evaluated through extensive simulations in the NS-2 environment. The results show that DAWA outperforms other existing solutions in terms of false negative ratio, false positive ratio, detection ratio, packet delivery ratio, packets loss ratio and packets drop ratio.     

Distributed cooperative target detection and localization in decentralized wireless sensor networks

In many network applications such as surveillance systems, it is crucial to detect the target and estimate its location. Distributed processing algorithms are capable of providing fast, secure, scalable and robust solutions. In this paper, we study the problem of target detection and localization in a wireless sensor network. Most of the current researches have focused on centralized algorithms and the works done on distributed algorithms usually need center assistance and practical issues such as communication link failure is not addressed in them. In this paper, we first propose a distributed consensus-based algorithm for target detection and then propose a distributed consensus-based localization algorithm. We assume that the target transmits a radio signal that is received in sensors equipped with limited computational and power resources. We consider the communication link failure and use the collaboration of sensor nodes to detect the presence of target. In the proposed target localization algorithm, sensor nodes estimate their distance toward the target using the received signal strength. In both the proposed algorithms, sensor nodes exchange information only with their neighbors and each makes an individual decision. We further prove the convergence of the proposed algorithms. Computer simulations confirm that the proposed algorithms are very fast and applicable in high-performance networks. We improve the localization accuracy at least by 25 % in terms of localization error compared with some recent algorithms.     

An MLP-based representation of neural tensor networks for the RDF data models

In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.

     

Shear vibration and buckling of double-layer orthotropic nanoplates based on RPT resting on elastic foundations by DQM including surface effects

Microsystem Technologies - In this article, shear vibration and buckling of double-layer orthotropic nanoplates resting on elastic foundations are analyzed subjected to in-plane loadings including...     

An analytical solution for thermal vibration of compositionally graded nanoplates with arbitrary boundary conditions based on physical neutral surface position

In this article, an analytical method is presented for thermo-mechanical vibration analysis of functionally graded (FG) nanoplates with different boundary conditions under various thermal loadings including uniform, linear, and nonlinear temperature rise via a four-variable plate theory considering neutral surface position. The temperature-dependent material properties of FG nanoplate vary gradually along the thickness according to the Mori-Tanaka homogenization scheme. The exactness of solution is confirmed by comparing obtained results with those provided in the literature. A parametric study is performed investigating the effects of nonlocal parameter, temperature fields, gradient index, and boundary conditions on vibration behavior of FG nanoplates.     

Enhanced antibacterial effect of azlocillin in conjugation with silver nanoparticles against Pseudomonas aeruginosa

In recent years, the problems associated with bacterial resistance to antibiotics caused nanodrugs to be considered as a new way for infectious diseases treatment. The main purpose of this study was to develop a new agent against Pseudomonas aeruginosa, a very difficult bacterium to treat, based on azlocillin antibiotic and silver nanoparticles (AgNPs). Azlocillin was conjugated with AgNPs by chemical methods and its antimicrobial activity was studied against P. aeruginosa using well diffusion agar method. Then, minimum inhibitory concentration and minimum bactericidal concentration of the new conjugate was specified with macro‐dilution method. The animal study showed the considerable enhanced antibacterial effect of azlocillin in conjugation with AgNPs against P. aeruginosa in comparison with azlocillin alone, AgNPs alone and azlocillin in combination with AgNPs.Inspec keywords: antibacterial activity, silver, nanoparticles, organic compounds, microorganisms, drugs, nanomedicine, biomedical materials, diseases, diffusion, nanofabricationOther keywords: Ag, macrodilution method, minimum bactericidal concentration, minimum inhibitory concentration, well diffusion agar method, P. aeruginosa, antimicrobial activity, chemical methods, azlocillin antibiotic nanoparticles, infectious diseases treatment, nanodrugs, bacterial resistance, Pseudomonas aeruginosa, silver nanoparticles, antibacterial effect     

Adaptive spacetime discontinuous Galerkin method for hyperbolic advection–diffusion with a non‐negativity constraint

Applications where the diffusive and advective time scales are of similar order give rise to advection–diffusion phenomena that are inconsistent with the predictions of parabolic Fickian diffusion models. Non‐Fickian diffusion relations can capture these phenomena and remedy the paradox of infinite propagation speeds in Fickian models. In this work, we implement a modified, frame‐invariant form of Cattaneo's hyperbolic diffusion relation within a spacetime discontinuous Galerkin advection–diffusion model. An h‐adaptive spacetime meshing procedure supports an asynchronous, patch‐by‐patch solution procedure with linear computational complexity in the number of spacetime elements. This localized solver enables the selective application of optimization algorithms in only those patches that require inequality constraints to ensure a non‐negative concentration solution. In contrast to some previous methods, we do not modify the numerical fluxes to enforce non‐negative concentrations. Thus, the element‐wise conservation properties that are intrinsic to discontinuous Galerkin models are defined with respect to physically meaningful Riemann fluxes on the element boundaries. We present numerical examples that demonstrate the effectiveness of the proposed model, and we explore the distinct features of hyperbolic advection–diffusion response in subcritical and supercritical flows. Copyright © 2015 John Wiley & Sons, Ltd.