Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Decentralized PID self-tuning control of industrial robots

A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

Projections of when temperature change will exceed 2°C above pre-industrial levels

Climate change projections are usually presented as 'snapshots' of change at a particular time in the future. Instead, we consider the key question 'when will specific temperature thresholds will be exceeded?'. Framing the question as "when might something happen (either permanently or temporarily)?" rather than "what might happen?" demonstrates that lowering future emissions will delay the crossing of temperature thresholds and buy valuable time for planning adaptation. For example, in higher greenhouse gas emission scenarios, a global average 2°C warming threshold is likely to be crossed by 2060, whereas in a lower emissions scenario, the crossing of this threshold is delayed up to several decades. On regional scales, however, the 2°C threshold will probably be exceeded over large parts of Eurasia, North Africa and Canada by 2040 if emissions continue to increase- well within the lifetime of many people living now.

Breaking free from the industrial age paradigm of branding

The paper aims to inform readers of the themes that emerged at the 2007 Thought Leaders International Conference on Brand Management and challenges academics and practitioners to rethink the basics of branding. The paper encourages academics and practitioners to escape from the continued confines of industrial age branding and the ‘influencing’ mindset and embrace the age of openness and co-creation. It is argued that we need to evolve from the industrial age paradigm of branding that informed brand management for decades and adjust practice and research accordingly.