| Project/Area Number |
20740227
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Mathematical physics/Fundamental condensed matter physics
|
|---|
| Research Institution | Nihon University |
|---|
Principal Investigator |
KIYONO Ken Nihon University, 工学部, 准教授 (40434071)
|
|---|
| Project Period (FY) |
2008 – 2010
|
| Project Status |
Completed (Fiscal Year 2010)
|
| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2010: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2009: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2008: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
|
|---|
| Keywords | 非平衡 / 非線形物理学 / 間欠性ゆらぎ / 非対称間欠性 / 心拍変動 / 心臓突然死 / 時系列析 / リスク解析 / 間欠性 / 非ガウス分布 / 極値統計 / 確率過程 / 対数振幅 / 発達乱流 |
|---|
| Research Abstract |
In studies of hydrodynamic turbulence and nonequilibrium systems, it has been demonstrated that the observed non-Gaussian probability density functions are often described effectively by a superposition of Gaussian distributions with fluctuating variances. Based on this framework, we propose a general method to characterize intermittent and non-Gaussian time series. In our approach, an observed time series is assumed to be described by the multiplication of Gaussian and amplitude random variables, where the amplitude variable describes the variance fluctuation. It is shown analytically that statistical properties of the log-amplitude fluctuations can be estimated using the logarithmic absolute moments of the observed time series. This method is applicable to a wide variety of symmetric unimodal distributions with heavy tails. By analyzing random cascade-type processes and superstatistical non-Gaussian models, we demonstrate that our method can provide detailed characterization in a wide range of non-Gaussian fluctuations. Using this method, we study healthy heart rate variability, which show the slow convergence to a Gaussian distribution through coarse graining procedure.
Moreover, to study asymmetric intermittent fluctuations observed in complex systems, we propose positive- or negative-directional non-Gaussian statistics. As a numerical example of asymmetric intermittent fluctuations, we heuristically introduce a random cascade-type model. Using our method, it is demonstrated that the asymmetric properties of heart rate variability depend on aging and autonomic disorder.
|
