Multi-Stage Prediction of Feed System Time Series

In order to reduce the tracking error of the computer numerical control (CNC) feed system and improve the CNC machining accuracy, a novel prediction model is proposed based on fuzzy C-means robust variational echo state network. Firstly, the feed speed time series is clustered, and then reconstructed for different categories. The multi-stage robust prediction models are established to realize the multi-state robust prediction of the CNC machining feed velocity to reduce the tracking error of the feed system. Finally, the reference and actual time series with different feed speed are used to verify the established models. The results show that the proposed method can reduce the tracking error and realize the effective prediction of the time series of the feed system.

Combining LOFAR and Apertif Data for Understanding the Life Cycle of Radio Galaxies

Active galactic nuclei (AGN) at the centres of galaxies can cycle between periods of activity and of quiescence. Characterising the duty-cycle of AGN is crucial for understanding their impact on the evolution of the host galaxy. For radio AGN, their evolutionary stage can be identified from a combination of morphological and spectral properties. We summarise the results we have obtained in the last few years by studying radio galaxies in various crucial phases of their lives, such as remnant and restarted sources. We used morphological information derived from LOw Frequency ARray (LOFAR) images at 150 MHz, combined with resolved spectral indices maps, obtained using recently released images at 1400 MHz from the APERture Tile In Focus (Apertif) phased-array feed system installed on the Westerbork Synthesis Radio Telescope. Our study, limited so far to the Lockman Hole region, has identified radio galaxies in the dying and restarted phases. We found large varieties in their properties, relevant for understanding their evolutionary stage. We started by quantifying their occurrences, the duration of the ‘on’ (active) and ‘off’ (dying) phase, and we compared the results with models of the evolution of radio galaxies. In addition to these extreme phases, the resolved spectral index images can also reveal interesting secrets about the evolution of apparently normal radio galaxies. The spectral information can be connected with, and used to improve, the Fanaroff–Riley classification, and we present one example of this, illustrating what the combination of the LOFAR and Apertif surveys now allow us to do routinely.

Research on the peak prediction and intelligent computing of reversal error for the tilt feed system installed on the precision machine tools

To effectively predict the peak of reversal error of tilt feed system and reduce reversal error caused by friction and gravity components, a peak prediction method of reversal error for tilt feed system on the precision NC machine tool is proposed. According to the load, tilt angle, motion trajectory, maximum static friction torque and relevant dynamic characteristic information, the peak prediction formula of the reversal error for the tilt feed system is established by mathematical derivation based on the kinematics, dynamics and torque balance during the process of reversal. Thus, the peak of reversal error for the tilt feed system can be obtained. The experimental results show that this method can achieve a good prediction effect, and can predict the peak of reversal error before the machining. It provides a theoretical basis for the reversal error suppression.