(5) Suppose the value of b can be inferred empirically, and an optimal segment selected from the time sequence generated through formula (5), then the values for both a and c can be determined using linear regression, whereby forecast production Q t f can be obtained as Q_{t}^{f} = (Q_{1}^{f},Q_{2}^{f}, ldots,Q_{n}^{f} ),quad t = 1, 2, ldots,n.
The idea is to fuse one-by-one the pixels coming from different information sources (the input image expressed in six color spaces), in order to avoid the over-segmentation and to obtain an optimal segmented image.
The evidence theory is employed to merge the three primitive colors (R, G, and B) of the same image in order to increase the quality of the information and to obtain an optimal segmented image.
Most recent studies in color image segmentation [21, 24, 25] have used the DS evidence theory to fuse one-by-one the pixels coming from the three components (Red, Green, and Blue) of original image, in order to increase the quality of information and to obtain an optimal segmented image.
The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image.
Leaving out one fold at a time, we used n-1 of the n folds to learn a set of computationally optimal segment weights and used those to calculate the aROC on the left-out fold and finally report the average aROC over the n cycles.
Then, we extend our method to scalable media distribution to find optimal segment sizes for all media layers.
We need to choose not only the optimal segment for outboard train to start its trip and for inboard train to complete its trip, but also the optimal assignment between locomotive and carriage.
Furthermore, after value b is given then regression being made, randomness and subjectivity are implied when optimal segment selected from the time sequence generated on semi-logarithmic relations between historical production (Q) and time (t), which may in turn, affect the forecast result.
It also allows determination of whether the current VT is within the optimal segment of the V-P curve.
Figure 4 Optimal segmented beacon cluster.
