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 .     

PREACO: A fast ant colony optimization for codebook generation

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.     

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.     

Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors

Dispatching rules are important to the performance of a manufacturing system. Selective applications of different priority rules at different processing stages in a multiple workstation manufacturing system have a positive impact on shop performance. This type of problem is a combinatorial dispatching decision. However, no dispatching rule can consistently produce better performance than all other rules under a variety of operating conditions and criteria. It is the purpose of this study to provide a robust solution for a dispatching decision that will have ‘good’ performance under different operating scenarios. In this paper a simulation case of a flow shop with multiple processors is proposed, specifically a multi-layer ceramic capacitor manufacturing system. Two multiple criteria decision-making methods – techniques for order preference by similarity to ideal solution (TOPSIS) and an analytic hierarchy process (AHP) – in combination with Taguchi orthogonal array are used to find the most suitable dispatching rule for every workstation. The results show that for 15 production scenarios and 4 criteria this combinatorial dispatching rule is robust, in the sense that it outperforms other commonly employed strategies.     

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.     

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.     

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.