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Construction and analysis of mathematical models of hydrodynamic forces and moment on the ship's hull using multivariate regression analysis

Kryvyi O., Miyusov M. V.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2021

Vol. 15 No. 4

854--864

To analyse the existing mathematical models of hydrodynamic forces and moment on the ship's hull and build new effective ones, an approach based on multivariate regression analysis is suggested. As factors (regressors), various dimensionless ratios of the geometric parameters of the vessel, such as length, breadth, draught, and block coefficient, were taken. When analysing existing mathematical models of hydrodynamic derivatives and building new ones, the value of the multiple correlation coefficient R and the value of standard errors were estimated. The significance of the models and the significance of all factors (regressors) included in the model were assessed using Fisher's and Student's criteria. As a result, new adequate mathematical models have been obtained for hydrodynamic constants with a high degree of correlation and an excellent level of significance of regressors.

Comparison of information on sleep apnoea contained in two symmetric EEG recordings

Prucnal Monika A., Polak Adam G.

Metrology and Measurement Systems

2019

Vol. 26, nr 2

229--239

Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4-A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apneas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.