Identification of cement manufacturing raw materials using machine vision

In the mining and manufacturing industry, there is a need for a non-extractive system to identify raw materials on conveying systems. Such a system would allow identification of raw materials on conveying systems preventing cross-contamination when the materials arrive at the final storage location. This project used machine vision techniques to identify cement manufacturing raw materials (clinker, gypsum and, limestone). Firstly, a representative sample (25 x 10kg samples of each material) was collected using a stratified random sampling procedure. This stratified random sampling procedure ensured the sample accurately represented the raw material in the stockpile. A dual purpose test bed and controlled lighting camera enclosure (for static model development and future dynamic system implementation) were constructed to minimise the effect of varying ambient light. This test bed and camera enclosure allowed the CMOS global shutter industrial camera to take twenty, 24bit colour images (8bit for each colour) of each sample. These images were catalogued and stored in a database for further model training and verification purposes. These images were pre-processed by a median filter which allowed any over saturated pixels (due to raw material surface moisture reflection) to have their intensity level reduced by replacing its value by the median value of its local neighbours. From the filtered image the individual red, green and blue (RGB) components were passed to a Histogram function which binned (255 bins for 8-bit colour) the various pixel intensities. The statistical features (weighted mean, skewness and kurtosis) of each colour's histogram were then stored in an array which then passed to the image feature database. A varying amount of feature arrays were used to train and verify the success of a probabilistic neural network (PNN) model. Initial optimisation of the PNN model was conducted using a local search algorithm which changed the smoothing parameter which achieved 94.83% accuracy. This model was then improved by implementing a Supervised Learning Probabilistic Neural Network (SLPNN). This model added data weight which changed the height of the Gaussian distribution function and input variable vector weight which changes the width of Gaussian distribution function. The implementation of the Supervised Learning Probabilistic Neural Network improved the models accuracy to 99.57%. Further model field testing will be required to verify the system in an operational environment where the camera enclosure will be subjected to dust, noise, varying temperatures and moisture. The Supervised Learning Probabilistic Neural Network outperforms the standard Probabilistic Neural Network which has been proven by this work. This work supports the claim that Machine Vision can be successfully be used to identify cement manufacturing raw materials with a high success rate. It also contributes to the literature by classifying clinker, gypsum and limestone in one body of work

Comparing Notes: Recording and Criticism

This chapter charts the ways in which recording has changed the nature of music criticism. It both provides an overview of the history of recording and music criticism, from the advent of Edison’s Phonograph to the present day, and examines the issues arising from this new technology and the consequent transformation of critical thought and practice

Wider Still and Wider: British Music Criticism since the Second World War

This chapter provides the first historical examination of music criticism in Britain since the Second World War. In the process, it also challenges the simplistic prevailing view of this being a period of decline from a golden age in music criticism

The Gender Paradox: Criticism of Women and Women as Critics

In May 2014 a storm erupted in the British classical music world when five established male critics fat-shamed Irish mezzo-soprano Tara Erraught, who was performing Octavian in Der Rosenkavalier at Covent Garden. Instead of focusing upon Erraught’s technique or interpretation, the critics ridiculed her physique. Writing in the Financial Times Andrew Clark referred to Erraught as ‘a chubby bundle of puppy-fat’; Michael Church in The Independent and Rupert Christiansen in The Telegraph both described her as ‘dumpy’; Andrew Clements in The Guardian called her ‘stocky’; and Richard Morrison in The Times characterised her as ‘unbelievable, unsightly and unappealing’. Although these sexist comments drew widespread condemnation, they are symptomatic of a centuries-old tendency for empowered male critics to fail to produce objective assessments of female musicians

Critiquing the Canon: The Role of Criticism in Canon Formation

Music critics wield great power. Their writing influences public opinion and contributes to how audiences receive works. They focus attention upon specific works and musicians, thus justifying these as most worthy of public recognition and debate. They help works to achieve repeat performances, and thereby to establish their places within the performing canon. In the age of recorded sound, they influence sales and affect charts. Although some claim that with the recent rise of ubiquitous digital critical commentary (much of it amateur) professional critics have lost their traditional authority, online criticism continues to exercise considerable sway. In a very real way, critics have been – and continue to be – the gatekeepers of the canon. As Roy Shuker has observed, ‘popular music critics … function as significant gatekeepers and as arbiters of taste’