Figure 2.7 Results of image segmentation by k-means method (hard ciustering approach) for a Raman kidney caicuius. (A) segmentation schemes (B) ciass centroids. |

In a first step the sensitivity of the method towards small clusters was studied, by monitoring the change in LSPR centroid wavelength for both a blank IMPS chip (reference) and a chip with 0.005 e/nm unselected Ptn>S3 supported on SisN as a function of temperature. The samples were heated in an A r flow (200mL/min) from 325 K in steps of 15 K and held at one temperature for 45 min (after 15 min, [Pg.155]

The best-known relocation method is the k-means method, for which there exist many variants and different algorithms for its implementation. The k-means algorithm minimizes the sum of the squared Euclidean distances between each item in a cluster and the cluster centroid. The basic method used most frequently in chemical applications proceeds as follows [Pg.11]

Furthermore, the situation becomes even worse for an asymmetric potential like that in (3.18), because at low temperature nearly the entire period p is spent on dwelling in the potential well (see appendix A), so that lim -oo < >ins = 0- In other words, unless the potential is strictly symmetric, the transition state position x tends to the minimum of the initial state It is natural to expect that the centroid approximation will work well when x does not deviate too far from x. To summarize, the centroid method is an instructive way to describe in a unique TST-like manner both the high [T > T ) and fairly low [T < T ) temperature regions, but it does not give a reliable estimate for k. [Pg.50]

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