Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain

Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract

Fatigue reliability and service life prediction of aluminum naval ship details based on monitoring data

The evolution of naval vessels towards high-speed crafts subjected to severe sea conditions has promoted an increasing interest in lightweight high-strength materials. Due to its strength and weight characteristics, aluminum has been proven especially suitable as construction material for hull structures, as well as other vessel parts. However, fatigue in aluminum naval crafts needs to be effectively addressed for the proper life-cycle assessment. Structural health monitoring (SHM) systems constitute effective tools for measuring the structural response and assessing the structural performance under actual operational conditions. In this paper, an approach for using SHM information in the fatigue reliability analysis and service life prediction of aluminum naval vessels is presented. The accumulated fatigue damage and the fatigue reliability are quantified based on SHM data acquired under different operational conditions, specified by the ship speeds, sea states, and heading angles. Additionally, an approach for estimating the reliability-based fatigue life under a given operational profile is presented. Seakeeping trial data of an aluminium high-speed naval vessel are used to illustrate the proposed approach