Identification of Factors Affecting Disk Drive’s Performance in Data Server by Use of Decision Tree Learning Method

To identify factors affecting magnetic disk drive’s data recording performance in data server, decision tree learning method is proposed and validated in this paper. Aiming at improving classification efficiency of various causes of HDD performance degradation, the ID3 algorithm of decision tree was first used showing the training set model would be able to achieve 100% accuracy. The maximum information entropy and information gain theory of ID3 algorithm were then adopted, from which accuracy range of 0.5–0.6 can be further achieved. The proposed method was validated to be effective for leveraging the data sever into Industry 4.0 ready smart machine.

Modeling biomass hydrothermal carbonization by the maximum information entropy criterion

The kinetics of biomass hydrothermal carbonization is modeled by the MaxEnt principle, without assuming a reaction network. Modeling is in good accordance with the experimental data concerning a broad range of biomass and reaction conditions.

Динамический хаос в нелинейной системе с 1/f-спектром

In the system of two nonlinear differential equations, proposed to explain the physical nature of the 1/f spectra, chaotization of the trajectories is revealed under periodic external action on one of the equations. External noise effects lead to stochastic resonance and low-frequency 1/f behavior of power spectra. Stochastic resonance and 1/f behavior of power spectra corresponds to the maximum information entropy, which indicates the stability of a random process.