An introduction to the Information Display Measurement Standard 1, the first display measurement standard from the International Committee for Display Metrology

On June 1, 2012, the Information Display Measurement Standard version 1 was released. This is the first standard from the worldwide standard group, the International Committee for Display Metrology, a standards committee within SID, and it culminates several years worth of development from many contributors at various levels. It has 26 top‐level chapters and a total over 450 sections of all parts, including over 200 measurement sections and 8 appendices that cover 133 topics of display metrology, tutorials, technical discussions, and references. Herein is an overview of the Information Display Measurement Standard version 1, including the structure of the document, tips, tricks, and hidden features, how it is maintained, support material, a breakdown of its contents, and finally, a sampling of four of the over 200 measurements. The standard is available as a free download and can be found on the International Committee for Display Metrology home page — http://www.icdm‐sid.org .     

Spectral preconditioning of Krylov spaces: Combining PLS and PC regression

In regularized regression the vectors that lie in Krylov and eigen subspaces, formed in PLS and PC regressions respectively, provide useful low dimensional approximations for the LS regression coefficient vector. By preconditioning the LS normal equations we provide a framework in which to combine these methods, and so exploit both of their respective advantages. The link between the proposed method to orthogonal signal correction and to cyclic subspace regression is made. The performance of the proposed solution in reducing the dimension of the regression problem, and the shrinkage properties of the resulting coefficient vector, are both examined.     

On the forced vibration of the fiber partially immersed in fluid

In this paper an experimental and theoretical investigation of the forced vibration of a partially immersed fiber has been carried out. An optical method utilizing a forward light scattering pattern has been used to detect a small (<1.0 μm) vibration amplitude of the fiber. The physical and mathematical model of the partially immersed fiber vibration has been put forward. Based on an analytical solution of the model, natural frequencies of the partially immersed fiber vibration have been found; they are consecutive positive roots of the transcendental equation. An “effective” speed of the wave propagation over the fiber has been introduced, which allows one to find the physical meaning of normal modes of the partially immersed fiber vibration. Theoretical predictions agree well with experimental data. The sensor exhibits an excellent sensitivity and could be used for measuring physical properties of fluids and liquid level.     

Nano-Engineered Carbon Fibre-Based Piezoelectric Smart Composites for Energy Harvesting and Self-Powered Sensing

The integration of piezoelectric materials onto carbon fiber (CF) can add energy harvesting and self-power sensing capabilities enabling great potential for “Internet of Things” (IoT) applications in motion tracking, environmental sensing, and personal portable electronics. Herein, a CF-based smart composite is developed by integrating piezoelectric poly(3,4-ethylenedioxythiophene) (PEDOT)/CuSCN-coated ZnO nanorods onto the CF surfaces with no detrimental effect on the mechanical properties of the composite, forming composites using two different polymer matrices: highly flexible polydimethylsiloxane (PDMS) and more rigid epoxy. The PDMS-coated piezoelectric smart composite can serve as an energy harvester and a self-powered sensor for detecting variations in impact acceleration with increasing output voltage from 1.4 to 7.6 V under impact acceleration from 0.1 to 0.4 m s⁻². Using epoxy as the matrix for a CF-reinforced plastic (CFRP) device with sensing and detection functions produces a voltage varying from 0.27 to 3.53 V when impacted at acceleration from 0.1 to 0.4 m s⁻², with a lower output compared to the PDMS-coated device attributed to the greater stiffness of the matrix. Finally, spatially sensitive detection is demonstrated by positioning two piezoelectric structures at different locations, which can identify the location as well as the level of the impacting force from the fabricated device.     

Sparse CCA using a Lasso with positivity constraints

Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. A sparse version of CCA is proposed that reduces the chance of including unimportant variables in the canonical variates and thus improves their interpretation. A version of the Lasso algorithm incorporating positivity constraints is implemented in tandem with alternating least squares (ALS), to obtain sparse canonical variates. The proposed method is demonstrated on simulation studies and a data set from market basket analysis.     

Deciding k-Colorability of P 5-Free Graphs in Polynomial Time

The problem of computing the chromatic number of a P ₅-free graph (a graph which contains no path on 5 vertices as an induced subgraph) is known to be NP-hard. However, we show that for every fixed integer k, there exists a polynomial-time algorithm determining whether or not a P ₅-free graph admits a k-coloring, and finding one, if it does.     

Discriminating between disturbance and process model mismatch in model predictive control

A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process.