It is well known that the asymmetric vane spacing can result in decreased levels of the excitation at specific frequencies. In the previous paper, the resonant response reduction of the mistuned bladed disks due to the asymmetric vane spacing was studied by use of the equivalent spring-mass model. Although the mistuned bladed disk should be analyzed by FEA (Finite Element Analysis) to accurately evaluate the resonant response reduction effect of the asymmetric vane spacing, it is unrealistic due to enormous computational time. Therefore, in this study, the mistuned bladed disk is modeled by use of FMM (Fundamental Mistuning Model) to evaluate the resonant response reduction effect of the asymmetric vane spacing accurately and practically. First, the frequency response analysis of a simple mistuned bladed disk consisting of flat plate blades is carried out for the symmetric and asymmetric vane spacing, using both of FMM and the direct FEA, and the calculated results are compared to confirm the validity of FMM. Second, the frequency response analysis of a realistic bladed disk is carried out for the symmetric and asymmetric vane spacing, using FMM, to examine the effect of the resonant response reduction effect.

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Recently, we have faced on the problem such as increasing elderly people who have occurred accidents including solitary death and falling and so on at their houses. To deal with these problems, many researchers and companies are proposing and releasing the systems monitored by sensors for keeping them safety. However, conventional systems using sensors including cameras and microphones give little privacy. In order to solve the problems, we proposed and discussed the falling discrimination using sound information for watching elderly people. Proposed system expects the falling discriminations using acoustic features extracted from sound. For discrimination accurately, we focused on Spectrum gravity between subjects/things and flooring styles. The feature characterized and modeled as Spectrum ratio of delta power as dynamic parameter. Finally, experiments of two-class discriminations using feature parameters achieved to 96.83 % with dataset of unspecific conditions.

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