6.13Indicatornoisegainestimate.
.................. 92

6.14Indicatortrendslope........................ 92

6.15Indicatornoisegainslope.
....................
92

6.16Sigmoidprototype.
........................ 93

6.17
Calibration
set
with
approximated
using
evolutionary
opti-
mization............................... 96
6.18
Calibration
set
with
approximated
using
Trust
Region
with
simpleinitialguess......................... 96
6.19
Calibration
set
with
Trust
Region
residual
spectrum
validation
model.
............................... 98
6.20
Calibration
set
with
iteratively
approximated
sigmoid
model.
.
99
6.21
Calibration
set
with
sigmoid
approximate.
. . . . . . . . . . . 100
6.22Indicatordecomposition.
.....................102

6.23Indicatornoisegainestimate.
..................102

6.24
Indicatortrendslope..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.102

6.25Indicatornoisegainslope.
....................102

6.26Sourcesplitteroverview.
.....................107

6.27Indicatordecomposition.
.....................108

6.28Indicatornoisegainestimate.
..................108

6.29
Indicatortrendslope..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.108

6.30Indicatornoisegainslope.
....................108

6.31
Decomposition
of
synthetically
generated
dataset.
.
.
.
.
.
.
.
110

6.32
Decomposition
of
synthetically
generated
dataset.
.
.
.
.
.
.
.
111

6.33
Decomposition
of
synthetically
generated
dataset.
.
.
.
.
.
.
.
112

7.1
Indicator
parametrization
overview.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
116

7.2
Componentstateclassi.cation.
.................118

7.3
Case2MODrawanddecomposed.
　
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