PROBLEMS OF CONSTRUCTION OF ASYNCHRONOUS ELECTRIC DRIVES WITH STATE OBSERVERS

This paper considers the problems of constructing asynchronous electric drives with state observers and the latest advances in the field of sensorless alternating current drives. The main areas of application of asynchronous electric drives with state observers are determined. A vector sensorless control system using coordinate converters from a natural coordinate system to a stationary and rotating one and a state observer based on a mathematical model of a motor in a two-phase stationary coordinate system was used as a basic one when considering the structures of modern asynchronous electric drives. The main types of flow and speed observers of asynchronous electric drives are considered for the tasks of constructing a high-quality asynchronous electric drive with vector control without using sensors. The problem was formulated for further modernization of control systems based on an electric drive with a flow and speed observer.

A Measure of Whole-Brain White Matter Topography

The unprecedentedly high-quality large-scale brain imaging datasets, from such as the Human Connectome Project (HCP) and UK-Biobank, provide a unique opportunity for measuring the white matter topography of the human brain. By leveraging the multi-shell diffusion MRI data from the original young adult HCP, we systematically develop a reliable measure of the whole-brain white matter topography, and we coin it topographic vector. As the main result, we find that the three most dominant dimensions of the topographic vectors strongly and linearly correlate with the coordinates of the corresponding streamlines of the whole-brain tractograms. Our results support the earlier prescient hypothesis that brain development follows a “base-plan” established by three (main) chemotactic gradients of early embryogenesis, and they implicate that the whole brain white matter tracts can be represented by vectors of a natural coordinate system.

Display and enhancement of volumetric fault images

Fault picking is a critical, but human-labor-intensive component of seismic interpretation. In a bid to improve fault imaging in seismic data, we have applied a directional Laplacian of a Gaussian operator to sharpen fault features within a coherence volume. We computed an [Formula: see text] matrix of the second moment tensor distance-weighted coherence values that fell within a 3D analysis window about each voxel. The eigenvectors of this matrix defined the orientation of planar discontinuities, whereas the corresponding eigenvalues determined whether these discontinuities were significant. The eigenvectors, which quantified the fault dip magnitude and dip azimuth, defined a natural coordinate system for smoothing of the planar discontinuity. We rotated the data to the new coordinate system and applied the sharpening operator. By comparing the vector dip of the discontinuity to the vector dip of the reflectors, we could apply a filter to either suppress or enhance discontinuities associated with unconformities or low-signal-to-noise-ratio shale-on-shale reflectors. We have revealed the value and robustness of the technique by application to two 3D data volumes from offshore New Zealand, which exhibited polygonal faulting, shale dewatering, and mass transport complexes.