The quality of the parameter estimates is measured by the root mean square (RMS) of the discrepancy vector (19) R M S = 1 N ∑ a = 1 N g ∑ i = 1 N c ∑ j = 0 N t α j a (g i a (T j, θ ) m o d e l − g i a (T j ) d a t a ) 2, where N t = 8 is the number of time classes, N c is the number of nuclei and α j a is equal to zero for Tll at j = 0,1, 2 and for Cad at j = 7, 8, and is equal to one otherwise.
Following, by back-projecting the reconstructed 3-d model onto the single image on screen (CAD design session) and applying a minimization function on the discrepancy vectors between the 3-d model and the single photography image, the final r value is estimated.
Then, the discrepancy distance vector, δ d, is calculated as the difference between the mean distance from the ToF camera after angle correction, z ̄, and the ground truth vector L: δ d = Z ̄ − L (7).
Then, the discrepancy distance vector δ v for all the j nodes is calculated for each pixel k=1,…,n as the difference between the mean distance from the ToF camera after angle correction, V ̄, and the ground truth distance vector L ′′ obtained using a laser distance meter: δ v = V ̄ − L ′′ (15).
The Green's function uncertainty may contribute to the discrepancy in the slip vector distribution between the teleseismic results and the field and InSAR data.
From the very first measurements made by the magnetometers onboard Swarm satellites launched by European Space Agency ESAA) in late 2013, it emerged a discrepancy between scalar and vector measurements.
In order to execute an LM optimization step, the vector of discrepancies Y, the matrix J and its SVD have to be evaluated for each new estimate of θ.
We denote the vector of discrepancies between the theoretical values and the measured values by Y. Then the least squares estimate θ ^ of the parameters is the value of θ that minimizes the sum of squares [ 14, 15] (2) S = ∑ i = 1 N (y c i (t i, θ ) − y ˜ i ) 2 = Y T Y.
The spatial likelihood at a point (x) in the medium can be calculated from the discrepancy between an optimum slowness vector (s mx ) from MUSIC analysis and a slowness vector (s(x)) of the seismic ray from the point to the array.
Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs.
This paper focuses on: (1) purely exploring the classification performance discrepancy between matrix- and vector-pattern representations; more importantly, (2) developing a new classifier design directly for matrix pattern.
