A multi-population state optimization algorithm for rail crack fault diagnosis

The rails usually work in complex environments, which makes them more prone to mechanical failures. In order to better diagnose the crack faults, a multi-population state optimization algorithm (MPVHGA) is proposed in this paper, which is used to solve the problems of low efficiency, easy precocity, and easy convergence of local optimal solutions in traditional genetic algorithms. The detection results of fault signals show that MPVHGA has the advantages of fast convergence rate, high stability, no stagnation, and no limitation of fixed iterations number. The average iterations number of MPVHGA in 100 independent iterations is about 1/5 of the traditional genetic algorithm (SGA for short) and about 1/3 of the population state optimization algorithm (VHGA for short), and the total convergence number of MPVHGA converges to 55 and 10 more than SGA and VHGA respectively, and the accuracy of fault diagnosis can reach 95.04%. On the basis of improving the performance of simple genetic algorithm, this paper provides a new detection method for rail crack fault diagnosis, which has important engineering practical value.

An Adaptive Operational Modal Analysis Method Using Encoder LSTM with Random Decrement Technique

A new parameter identification method under non-white noise excitation using transformer encoder and long short-term memory networks (LSTMs) is proposed in the paper. In this work, the random decrement technique (RDT) processing of the data is equivalent to eliminating the noise of the raw data. In general, the addition of the gate in LSTM allows the network to selectively store data, which avoids gradient disappearance and gradient explosion to a certain extent. It is worthwhile mentioning that the encoder can learn the essence of data, which reduces the burden for the LSTM. More specifically, establish as simple LSTM structure as possible to learn the data of this essence to achieve the best training effect. Finally, the proposed method is used for simulation and experimental verification, and the results show that the method has the advantages of high recognition accuracy, strong anti-noise ability, and fast convergence rate. Specially, the results indicated appropriate accuracy proposed by deep learning combined with traditional method for parameter identification as well as proper performance of the proposed method.

A Variable Step-Size Leaky LMS Algorithm

The leaky LMS algorithm has been extensively studied because of its control of parameter drift. This unexpected parameter drift is linked to the inadequacy of excitation in the input sequence. And generally leaky LMS algorithms use fixed step size to force the performance of compromise between the fast convergence rate and small steady-state misalignment. In this paper, variable step-size (VSS) leaky LMS algorithm is proposed. And the variable step-size method combines the time average estimation of the error and the time average estimation of the normalized quantity. Variable step-size method proposed incorporating with leaky LMS algorithm can effectively eliminate noise interference and make the early convergence, and final small misalignments are obtained together. Simulation results demonstrate that the proposed algorithm has better performance than the existing variable step-size algorithms in the unexcited environment. Furthermore, the proposed algorithm is comparable in performance to other variable step-size algorithms under the adequacy of excitation.

On the Trade-Off between Energy Efficiency and Spectral Efficiency in RIS-Aided Multi-User MISO Downlink

As reconfigurable intelligent surfaces (RISs) have been gradually brought to reality, a large amount of research has been conducted to investigate the immense benefits of RISs. That is because RISs enable us to artificially direct the radio wave propagating through the environment at a relatively low cost. This paper investigates the trade-off between spectral efficiency (SE) and energy efficiency (EE) in the RIS-aided multi-user multiple-input single-output downlink. We develop an optimization framework for designing the transmitting precoding at the base station and the phase shift values at the RIS to balance the EE-SE trade-off. The proposed iterative optimization framework for the design includes quadratic transform, alternating optimization, and weighted minimization mean-square error conversion. Simulation results illustrate our optimization framework algorithm exhibits effectiveness and a fast convergence rate.

Method of lines for analysis of plane wave scattering by periodic arrays of magnetically-biased graphene strips

In this paper, efficient analysis of the plane wave scattering by periodic arrays of magnetically-biased graphene strips (PAMGS) is performed using the semi-numerical, semi-analytical method of lines (MoL). In MoL, all but one independent variable is discretized to reduce a system of partial differential equations to a system of ordinary differential equations. Since the solution in one coordinate direction is obtained analytically, this method is time effective with a fast convergence rate. In the case of a multi-layered PAMGS, the governing equations of the problem are discretized concerning periodic boundary conditions (PBCs) in the transverse direction. The reflection coefficient transformation approach is then used to obtain an analytical solution in the longitudinal direction. Here, magnetically-biased graphene strips are modeled as conductive strips with a tensor surface conductivity which is electromagnetically characterized with tensor graphene boundary condition (TGBC). The reflectance and transmittance of different multi-layered PAMGS are carefully obtained and compared with those of other methods reported in the literature. Very good accordance between the results is observed which confirms the accuracy and efficiency of the proposed method.

Leader-Following Consensus Over Acyclic Switching Digraphs

The existing results on the leader-following consensus problem for linear continuous-time multi-agent systems over jointly connected switching digraphs rely on the assumption that the system matrices do not have eigenvalues with positive real parts. In this paper, to remove this assumption, we first establish a stability result for a class of linear switched systems. Based on this, we show that the leader-following consensus problem for linear multi-agent systems with general system modes over jointly connected switching digraphs is solvable if the digraphs are acyclic. Moreover, the leader-following consensus can be achieved at a preassigned but arbitrarily fast convergence rate. A numerical example is provided to illustrate our design.

New approximate analytical technique for the solution of time fractional fluid flow models

In this note, we broaden the utilization of an efficient computational scheme called the approximate analytical method to obtain the solutions of fractional-order Navier–Stokes model. The approximate analytical solution is obtained within Liouville–Caputo operator. The analytical strategy generates the series form solution, with less computational work and fast convergence rate to the exact solutions. The obtained results have shown a simple and useful procedure to analyze complex problems in related areas of science and technology.

Constrained Ensemble Langevin Monte Carlo

<p style='text-indent:20px;'>The classical Langevin Monte Carlo method looks for samples from a target distribution by descending the samples along the gradient of the target distribution. The method enjoys a fast convergence rate. However, the numerical cost is sometimes high because each iteration requires the computation of a gradient. One approach to eliminate the gradient computation is to employ the concept of "ensemble." A large number of particles are evolved together so the neighboring particles provide gradient information to each other. In this article, we discuss two algorithms that integrate the ensemble feature into LMC, and the associated properties.</p><p style='text-indent:20px;'>In particular, we find that if one directly surrogates the gradient using the ensemble approximation, the algorithm, termed Ensemble Langevin Monte Carlo, is unstable due to a high variance term. If the gradients are replaced by the ensemble approximations only in a constrained manner, to protect from the unstable points, the algorithm, termed Constrained Ensemble Langevin Monte Carlo, resembles the classical LMC up to an ensemble error but removes most of the gradient computation.</p>

A Decomposition Method for a Fractional-Order Multi-Dimensional Telegraph Equation via the Elzaki Transform

In this article, the Elzaki decomposition method is used to evaluate the solution of fractional-order telegraph equations. The approximate analytical solution is obtained within the Caputo derivative operator. The examples are provided as a solution to illustrate the feasibility of the proposed methodology. The result of the proposed method and the exact solution is shown and analyzed with figures help. The analytical strategy generates the series form solution, with less computational work and a fast convergence rate to the exact solutions. The obtained results have shown a useful and straightforward procedure to analyze the problems in related areas of science and technology.