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The inner dependence matrix of the customer needs is shown in Table 4.
The selection matrix J can be viewed as the linear dependence matrix in PARALIND model.
The inner dependence matrix of the design requirements is shown in Table 5.
The statistical features were extracted using gray-level co-occurrence matrix (GLC M, also known gray-level spatial dependence matrix (GLSDM).
Step 2: Establish the inner dependence matrix W 2 of the CNs with respect to each CN.
Furthermore, [20] has shown that the dependence matrix J can be uniquely obtained in the PARALIND model by trilinear decomposition method.
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Second-order statistics are co-occurrence measurements calculated using spatial gray-level dependence matrices.
Figure 1 is the visualization of the six dependence matrices of a randomly selected sample.
Spatial gray-level dependence matrices (SGLDMs), or co-occurrence matrices, are a well-known method for texture analysis [ 28, 29].
More sophisticated approaches propose the employment of other texture analysis procedures, specifically the spatial gray-level dependence matrices (SGLDM) [ 12, 14] and the center-symmetric auto-correlation method (CSAC) [ 3].
In our method, the Haralick co-occurrence features were extracted from each of the gray level spatial-dependence matrices [ 13].
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