MAT

Schematic diagram to illustrate the separation and identification of nonlinear flow characteristics for the proposed method. Due to a fact that steam turbines and generators have different dynamic characteristics at different operating points, the static nonlinear flow characteristic (e.g., yellow solid lines in (a)) is overwhelmed by input and output data with dynamic characteristics, such as the blue dots in (a) and their corresponding time series are shown in (b). The key to identifying the nonlinear flow characteristic is to separate it from the linear dynamic submodels at different ope

The complete description of the dataset can be found at: https://arxiv.org/abs/2305.03170

7200 .csv files, each containing a 10 kHz recording of a 1 ms lasting 100 hz sound, recorded centimeterwise in a 20 cm x 60 cm locating range on a table. 3600 files (3 at each of the 1200 different positions) are without an obstacle between the loudspeaker and the microphone, 3600 RIR recordings are affected by the changes of the object (a book). The OOLA is initially trained offline in batch mode by the first instance of the RIR recordings without the book. Then it learns online in an incremental mode how the RIR changes by the book.

These datasets are of the hydraulically actuated robot HyQ’s proprioceptive sensors. They include absolute and relative encoders, force and torque sensors, and MEMS-based and fibre optic-based inertial measurement units (IMUs). Additionally, a motion capture system recorded the ground truth data with millimetre accuracy. In the datasets HyQ was manually controlled to trot in place or move around the laboratory. The sequence includes: forward and backwards motion, side-to-side motion, zig-zags, yaw motion, and a mix of linear and yaw motion.