Robust kernel distance multivariate control chart using support vector principles

It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false- negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process mo

How does it feel to be a narcissist? : narcissism and emotions

Emotional processes are of key importance for the understanding of narcissism, in both its grandiose and its vulnerable forms. The current chapter provides an overview on the links between narcissism and emotionality. The two forms of narcissism differ distinctly in their hedonic tone, with vulnerable narcissism being characterized by negative emotionality and low well-being and grandiose narcissism being linked to positive emotionality and high well-being. Both forms are related to strong mood variability that is thought to stem from contingent self-esteem. Both forms are related to hubristic pride, but only vulnerable narcissism is linked to shame-proneness, envy, and schadenfreude. Both forms are characterized by outbursts of anger, but the underlying causes and the expression of anger differ between the two forms. Specifically, vulnerable narcissism is linked to uncontrollable narcissistic rage that stems from a fragile sense of self and results in disproportionate and dysfunctional aggression. Grandiose narcissism, in contrast, goes along with instrumental aggression that serves the purpose of asserting one's dominance in the face of strong direct status threats. Vulnerable narcissism is related to deficits in emotion regulation, yet research has just begun to shed light on the regulation processes of grandiose narcissists. The chapter concludes with reflections on how recent theoretical and methodological developments might be employed to gain a fuller understanding of narcissists' emotional lives