User\u27s Guide to MBC3: Multi-Blade Coordinate Transformation Code for 3-Bladed Wind Turbine

The dynamics of wind turbine rotor blades are conventionally expressed in rotating frames attached to the individual blades. The tower-nacelle subsystem though, sees the combined effect of all rotor blades, not the individual blades. Also, the rotor responds as a whole to excitations such as aerodynamic gusts, control inputs, and tower-nacelle motion—all of which occur in a nonrotating frame. Multi-blade coordinate transformation (MBC) helps integrate the dynamics of individual blades and express them in a fixed (nonrotating) frame. MBC involves two steps: transforming the rotating degrees of freedom and transforming the equations of motion. Reference 1 details the MBC operation. This guide summarizes the MBC concept and underlying transformations

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches