Summary of discussion on ‘Sciences related to pitch and coke usage in carbon manufacture’

This article is a brief summary of the Discussion session held after the presentation of the preceding papers at the conference organized by the Industrial Carbon and Graphite Group of the Society of Chemical Industry, London, March 1984.     

Introduction of a nonlinearity measure for principal component models

Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process.     

Linear demosaicing inspired by the human visual system.

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.     

Multi-functional biochip for medical diagnostics and pathogen detection

We describe a multi-functional biochip (MFB), which uses two different types of bioreceptors, including nucleic acid and antibody probes, on a single platform. The multi-functional capability of the MFB device for biomedical diagnostics is illustrated by measurements of DNA probes specific to gene fragments of Bacillus anthracis and antibody probes targeted to Escherichia coli. Calibration curves for monitoring pathogenic species using antibody probes against E. coli and DNA probes for B. anthracis illustrate the capability of the device for medical diagnostics and for quantitative detection of pathogenic agents.     

Conditionals and conditional probability.

The authors report 3 experiments in which participants were invited to judge the probability of statements of the form if p then q given frequency information about the cases pq, p?q, ?pq, and ?p?q (where ? = not). Three hypotheses were compared: (a) that people equate the probability with that of the material conditional, 1 - P(p?q); (b) that people assign the conditional probability, P(q/p); and (c) that people assign the conjunctive probability P(pq). The experimental evidence allowed rejection of the 1st hypothesis but provided some support for the 2nd and 3rd hypotheses. Individual difference analyses showed that half of the participants used conditional probability and that most of the remaining participants used conjunctive probability as the basis of their judgments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)