Based on the literature [ 16], this paper proposes the Sparse Group Lasso method for channel selection and feature selection of the EEG fused feature and estimates model parameters using a combination of the blockwise coordinate descent method and the coordinate gradient descent method.
The solution has been obtained by means of the Galerkin orthogonalisation method combined with the proposed method of the coordinate system transformation.
Also note that all methods, except the coordinate descent methods, ℓ 1-CD and L-CD, and the ℓ 1-OMP method, do not need to explicitly form the sensing matrix Φ but only need functions that implement the matrix-vector multiplication by Φ and Φ T at each iteration.
We show both qualitatively and quantitatively that the presented method aligns the coordinate systems accurately and, furthermore, considerably outperforms the baseline method.
Then, by using graphic correction method, the coordinates of common points on laser tracker will be corrected.
Based on the proposed method, the coordinates of detectors can be measured with very high accuracy and the coordinate of the center of the gantry (which is normally the same as the center of field of view) can be defined correctly.
The method of sequential updates corresponds to the coordinate descent method where multivariate optimization problem is solved by solving a sequence of scalar subproblems, each operating on a selected coordinate (scheduler) while all other coordinates are fixed.
An alternative method that could be considered is the coordinate descent method.
The analytical compliance matrix of the TFM is obtained by using the coordinate transformation method (CT-method) of compliance matrices.
The results show that the proposed method can coordinate the scheduling of the three types of handling equipment and can realize the optimal trade-off between time-saving and energy-saving.
