Reframing Pinterest: Information Literacy for Interior Design Students

This article discusses providing information literacy to interior design students, specifically how over a few years, and several assignment iterations, we moved from a more traditional one-shot library instruction session to a creative Association of College and Research Libraries Framework-inspired assignment using Pinterest as the medium for the students' professionally relevant research project. The outcome of our instruction relied on flexible assignment planning, collaboration as faculty and librarians changed roles, and a focus on information literacy skills needed by professional designers.     

Dynamics of a fifth-order iterative method

In this paper, we study the dynamical behaviour of a two-point iterative method with order of convergence five to solve nonlinear equations in the complex plane. In fact, we complement the dynamical study started in previous works with a more systematic analysis for polynomials with at most two different roots and different multiplicities. In addition, we characterize some polynomials of degree greater or equal to 4, such that the related methods are not generally convergent. We also analyse the degrees of the rational functions associated with two-point methods when they are applied to polynomials of degree n, showing their dependence on n ² and how this fact considerably complicates the dynamical study.     

Iterative solvers of Ax = b derived from ode's numerical integration methods

Although iterative methods for solving linear systems has been the subject of study for a long time, the acceleration of such methods is still object of interest, research focusing in improvements of already known methods as well as on new, faster ones. In this sense we can cite among several other authors, for example, the works of Martins [8], Hadjidimos [9] and Evans [7]. The purpose of this paper is twofold. First we show that the numerical integration methods for Ordinary Differential Equations, obtained by Taylor expansions, result in a B-extrapolation method [3] for iteratively solving linear algebraic systems. Second, we compare the best rates of convergence of the algorithms developed here, with the best rate of convergence of the Jacobi Over-Relaxation method, (JOR), proving that depending on the choice of the step of integration and the behavior of the spectrum of the matrix A of the original system Ax = b the third order methods derived from the Taylor expansions can be better than the JOR. The procedure used here with the splitting A – I – J can also be easely applied to other splittings, resulting in a comparison of the convergence of the present method with the SOR or iterations methods or any other linear iterative methods of degree one.     

A numerical algorithm for image sequence inpainting that preserves fine textures

We describe a fast, reliable and automatic algorithm for image sequence inpainting that combines spatio-temporal interpolation with fine texture preservation inside missing areas. The algorithm provides an estimate of the inpainting error by using an automatic geometric recognition of missing regions. Computational kernels are sparse linear systems solved using Generalized Minimum RESidual iterative method equipped with AMG multigrid preconditioner. Experiments on synthetic and real data are discussed.     

H∞ model reduction for negative imaginary systems

This paper is concerned with the H_∞ model reduction for negative imaginary (NI) systems. For a given linear time-invariant system that is stable and NI, our goal is to find a stable reduced-order NI system satisfying a pre-specified H_∞ approximation error bound. Sufficient conditions in terms of matrix inequalities are derived for the existence and construction of an H_∞ reduced-order NI system. Iterative algorithms are provided to solve the matrix inequalities and to minimise the H_∞ approximation error. Finally, a numerical example is used to demonstrate the effectiveness of the proposed model reduction method.     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Generalized F-discrepancy-based point selection strategy for dependent random variables in uncertainty quantification of nonlinear structures

In the performance evaluation of structures under disastrous actions, for example, earthquakes, it is important to take into account the randomness of structural parameters. Generally, these random parameters are treated either as independent or perfectly dependent, but practically they are partly dependent. This article aims at developing a point selection strategy for uncertainty quantification of nonlinear structures involving probabilistically dependent random parameters characterized by copula function. For this purpose, the point selection strategy for structures involving independent basic variables is first revisited. As an improvement, a generalized F-discrepancy diminishing oriented iterative screening algorithm is proposed. Then, combining with the conditional sampling method, a conditional point set rearrangement method and a conditional iterative screening-rearrangement method are proposed for probabilistically dependent variables. These new point selection strategies are readily incorporated into the probability density evolution method for uncertainty quantification of nonlinear structures involving dependent random parameters, which is characterized by copula function. The proposed methods are illustrated by two examples including a shear frame with hysteretic restoring forces and a reinforced concrete frame structure with the damage constitutive model of concrete, where the material parameters are probabilistically dependent. The results demonstrate the effectiveness of the proposed method. Problems to be studied are discussed.     

基于变步长差分迭代方法的河道水面线计算

在计算天然河道水面线时，由于河道的不规则性，导致计算断面间距往往是不相等的。为此，依据水力学能量方程，建立了变步长差分迭代的水面曲线模型。该数学模型简单易行，适应性强，便于程序化实现。通过对河南省南部某河道治理工程水面线的推求验证，证明该方法可有效解决非等间距断面的水面线推求问题，且计算效率和精度较高，可为计算类似工程问题提供参考。     

Rhombellation: a builder for multi-shell clusters

Rhombellanes are mathematical structures, proposed by us in 2017; they may appear both in periodic crystals or in finite structures. The simplest rhombellane is rbl.5 or K_2.3, the complete bipartite graph. In this paper, rhombellation operation is iteratively applied on three classes of structures: cube, tori and cubic pcu-network, respectively. The structural topological parameters are detailed.     

机器人系统有限时间自适应迭代学习控制

研究任意初态下，机器人系统的有限时间自适应迭代学习控制方法。引入初始修正吸引子的概念，构造一个含有初始修正项的误差变量。针对定常机器人系统和时变机器人系统，采用Lyapunov-like方法，分别设计迭代学习控制器处理系统中不确定性。并且，采用未含/含限幅学习机制，保证闭环系统各变量的一致有界性和误差变量在整个作业区间一致收敛性。藉以实现跟踪误差在预先指定区间的完全跟踪。仿真结果验证所设计控制方法的有效性。