where t is a threshold value associated to the particular feature descriptor or measurement and b is a boolean function defined as: b ( x, t ) = 1 x ≤ t, 0 x > t. (2).
In particular, given two descriptors or measurements v p and v q, then d(v p, v q ) is represented by a real positive value that states how different the two descriptors or measurements are.
For each type of local descriptor or points pair measurement, we define a similarity difference function d that is invariant under rigid transformation of each single N-points base.
In this paper, under a matching condition between the descriptor matrix and the measurement output matrix (or the control input matrix), we propose to set the Lyapunov matrix in the BMI as block diagonal appropriately so that the BMI is reduced to LMIs.
The descriptors and their indicators, measurement endpoints, and potential data sources are given in Table 1 and discussed in more detail below.
The question arises how to decide which descriptors in different measurements represent the same peak (and hence potentially the same VOC).
To implement this system, we also propose a data distribution approximation model, Polygon descriptor, and a shape difference measurement, called Deforming distance method.
Such a procedure is especially useful when the descriptor values are derived from experimental measurement or require expensive calculation (for example, those derived from QM calculations).
The descriptor value was the modified response measurement.
