Fractals and Cosmological Large-Scale Structure

Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal

Fractal algorithms for signal analysis are developed from geometric fractals and can be used to describe various complex signals in nature. A roughness scaling extraction algorithm with first-order flattening (RSE-f1) was shown in our previous studies to have a high accuracy, strong noise resistance, and a unique capacity to recognize the complexity of non-fractals that are common in signals. In this study, its disadvantage of a long calculation duration was addressed by using a dichotomy-binary strategy. The accelerated RSE-f1 algorithm (A-RSE-f1) retains the three above-mentioned advantages of the original algorithm according to theoretical analysis and artificial signal testing, while its calculation speed is significantly accelerated by 13 fold, which also makes it faster than the typical Higuchi algorithm. Afterwards, the vibration signals of the milling process are analyzed using the A-RSE-f1 algorithm, demonstrating the ability to distinguish different machining statuses (idle, stable, and chatter) effectively. The results of this study demonstrate that the RSE algorithm has been improved to meet the requirements of practical engineering with both a fast speed and a high performance.

Multiscaling Properties of Large-Scale Structure in the Universe

The large-scale distribution of galaxies and galaxy clusters in the universe can be described in the mathematical language of multifractal sets. A particularly significant aspect of this description is that it furnishes a natural explanation for the observed differences in clustering properties of objects of different density in terms of multiscaling, the generic consequence of the application of a local density threshold to a multifractal set. The multiscaling hypothesis suggests ways of improving upon the traditional statistical measures of clustering pattern (correlation functions) and exploring further the connection between clustering pattern and dynamics.