3-dimensional (orthogonal) structural complexity of time-series data using low-order moment analysis
Victor John Law, Feidhlim T. O’Neill, Denis Pius Dowling · AIP conference proceedings · 2012
The recording of atmospheric pressure plasmas (APP) electro-acoustic emission data has been developed as a plasma metrology tool in the last couple of years. The industrial applications include automotive and aerospace industry for surface activation of polymers prior to bonding [1, 2, and 3]. It has been shown that as the APP jets proceeds over a treatment surface, at a various fixed heights, two contrasting acoustic signatures are produced which correspond to two very different plasma-surface entropy states (blow arc ∼ 1700 ± 100 K; and; afterglow ∼ 300−400 K) [4]. The metrology challenge is now to capture deterministic data points within data clusters. For this to be achieved new real-time data cluster measurement techniques needs to be developed [5]. The cluster information must be extracted within the allotted process time period if real-time process control is to be achieved. This abstract describes a theoretical structural complexity analysis (in terms crossing points) of 2 and 3-dimentional line-graphs that contain time-series data. In addition LabVIEW implementation of the 3-dimensional data analysis is performed. It is also shown the cluster analysis technique can be transfer to other (non-acoustic) datasets.