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We present the calculations using this method for cases corresponding to both quiet and disturbed conditions.
First, we have formulated the method for cases in which the airfoil motion is given.
A simple algorithm has been developed to optimize the method for cases where the number of sources differs significantly from the number of target points.
In this section, we will show the effectiveness of the proposed method for cases 1 and 3: gradual degradation considering CIT and degradation with shock occurring at an unknown time.
Thus, we do not recommend using the CIM-NPCI method for cases when marker density is low, and conclude that the SI method actually is appropriate for these cases.
However, SSA is an effective unmixing method for cases in which one gene has an approximately regular structure, and this differs from the structure of the other gene.
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Fig. 9 a-1 Warning area and prediction result for the warning issuance by the IPF method for case No. 5. a-2 Final prediction result by the IPF method for case No. 5. b-1 5 b-1 5ng areas and prediction results for the initial to fifth Warning issuareas by the PLUM method for cand No. 5.
Fig. 8 a-1 Warning area and prediction result for the warning issuance by the IPF method for case No. 1. a-2 Final prediction result by the IPF method for case No. 1. b-1, 2 Warning areas and prediction results at the initial (b-1) and second (b-2) warning issuances by the PLUM method for case No. 1.
Tables 4 and 5 show the average PSNR results of the video reconstructed from multiple video inputs using the proposed method for Case 1 and Case 2, respectively.
Through comparing the adaptation accuracies with those provided by other classical neuro-adaptation methods, the modularized adaptation is proved to be a feasible method for case adaptation.
b-6 Final prediction result by the PLUM method for case No. 5. Definitions of colors, shading, and symbols are the same as in Fig. 4.
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