Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

This paper studies the problem of adaptive observer‐based radial basis function neural network tracking control for a class of strict‐feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non‐smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so‐called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system. Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design.     

Novel efficient full adder and full subtractor designs in quantum cellular automata

In this study, two new full adder/full subtractor designs based on quantum-dot cellular automata technology have been proposed. By means of the presented equation for SUM and SUBTRACT operations, the new high-speed, low power, and cost efficient designs have been achieved. Even if the three-level design has a lower cell count, occupies less area, and operates at a higher speed, the one-layer design is far more feasible. Analysis of the temperature and energy consumption of the proposed design indicates that the proposed approaches are superior to those of previous works.

     

New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing

The Journal of Supercomputing - Utilizing from energy-aware solutions along with maintaining service-level agreements is one of the most important research issues in cloud computing. In the...     

Mutual neighbors and diagonal loading-based sparse locally linear embedding

In this study, a new Locally Linear Embedding (LLE) algorithm is proposed. Common LLE includes three steps. First, neighbors of each data point are determined. Second, each data point is linearly modeled using its neighbors and a similarity graph matrix is constructed. Third, embedded data are extracted using the graph matrix. In this study, for each data point mutual neighborhood conception and loading its covariance matrix diagonally are used to calculate the linear modeling coefficients. Two data points will be named mutual neighbors, if each of them is in the neighborhood of the other. Diagonal loading of the neighboring covariance matrix is applied to avoid its singularity and also to diminish the effect of noise in the reconstruction coefficients. Simulation results demonstrate the performance of applying mutual neighborhood conception and diagonal loading and their combination. Also, the results of applying the mutual neighborhood on Laplacian Eigenmap (LEM) demonstrate the good performance of the proposed neighbor selection method. Our proposed method improves recognition rate on Persian handwritten digits and face image databases.     

Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search

An artificial neural network (ANN) is used to model the frequency of the first mode, using the beam length, the moment of inertia, and the load applied on the beam as input parameters on a database of 100 samples. Three different heuristic optimization methods are used to train the ANN: genetic algorithm (GA), particle swarm optimization algorithm and imperialist competitive algorithm. The suitability of these algorithms in training ANN is determined based on accuracy and runtime performance. Results show that, in determining the natural frequency of cantilever beams, the ANN model trained using GA outperforms the other models in terms of accuracy.     

Assignment of cells to switches in cellular mobile network: a learning automata-based memetic algorithm

Handoff and cabling costs management plays an important role in the design of cellular mobile networks. Efficient assigning of cells to switches can have a significant impact on handoff and cabling cost. Assignment of cells to switches problem (ACTSP) in cellular mobile network is NP-hard problem and consequently cannot be solved by exact methods. In this paper a new memetic algorithm which is obtained from the combination of learning automata (LA) and local search is proposed for solving the ACTSP in which the learning automata keeps the history of the local search process and manages the problem’s constraints. The proposed algorithm represents chromosome as object migration automata (OMAs), whose states represent the history of the local search process. Each state in an OMA has two attributes: the value of the gene (allele), and the degree of association with those values. The local search changes the degree of association between genes and their values. To show the superiority of the proposed algorithm several computer experiments have been conducted. The obtained results confirm the efficiency of proposed algorithm in comparison with the existing algorithms such as genetic algorithm, memetic algorithm, and a hybrid Hopfield network-genetic algorithm.     

Effect of size on the dynamic behaviors of atomic force microscopes

Microsystem Technologies - Accurate mathematical modeling and simulation of cantilever dynamics are crucial to design and fabrication of the atomic force microscope (AFM). Thickness of AFM...     

A finite difference/finite element technique with error estimate for space fractional tempered diffusion-wave equation

An efficient numerical technique is proposed to solve one- and two-dimensional space fractional tempered fractional diffusion-wave equations. The space fractional is based on the Riemann–Liouville fractional derivative. At first, the temporal direction is discretized using a second-order accurate difference scheme. Then a classic Galerkin finite element is employed to obtain a full-discrete scheme. Furthermore, for the time-discrete and the full-discrete schemes error estimate has been presented to show the unconditional stability and convergence of the developed numerical method. Finally, two test problems have been illustrated to verify the efficiency and simplicity of the proposed technique.     

Nonlocal vibration and instability analysis of carbon nanotubes conveying fluid considering the influences of nanoflow and non-uniform velocity profile

In this article, the influences of non-uniform velocity profile attributable to slip boundary condition and viscosity of fluid on the dynamic instability of carbon nanotubes (CNTs) conveying fluid are investigated. The nonlocal elasticity theory and the Euler–Bernoulli beam theory are employed to derive partial differential equation of nanotubes conveying fluid. Furthermore, a dimensionless momentum correction factor (MCF) is obtained as a function of Knudsen number (Kn) so as to insert the effects of non-uniform velocity profile into the equation of motion. In continuation, complex eigen-frequencies of the system are attained with respect to different boundary conditions, the momentum correction factor, slip boundary condition and nonlocal parameter. The results delineate that considering the effects of non-uniform velocity profile could diminish predicted critical velocity of flow. Therefore, the divergence instability occurs in the lower values of flow velocity. In addition, the MCF decreases through enhancement of Kn; hence, the effects of non-uniform velocity profile are more noticeable for liquid fluid than gas fluid.     

Advanced Bioresponsive Multitasking Hydrogels in the New Era of Biomedicine

Advanced forms of hydrogels have many inherently desirable properties and can be designed with different structures and functions. In particular, bioresponsive multifunctional hydrogels can carry out sophisticated biological functions. These include in situ single-cell approaches, capturing, analysis, and release of living cells, biomimetics of cell, tissue, and tumor-specific niches. They can allow in vivo cell manipulation and act as novel drug delivery systems, allowing diagnostic, therapeutic, vaccination, and immunotherapy methods. In the present review of multitasking hydrogels, new approaches and devices classified into point-of-care testing (POCT), microarrays, single-cell/rare cell approaches, artificial membranes, biomimetic modeling systems, nanodoctors, and microneedle patches are summarized. The potentials and application of each format are critically discussed, and some limitations are highlighted. Finally, how hydrogels can enable an “all-in-one platform” to play a key role in cancer therapy, regenerative medicine, and the treatment of inflammatory, degenerative, genetic, and metabolic diseases is being looked forward to.