The energy loss may predict rupture risks of anterior communicating aneurysms: A preliminary result

Anterior communicating artery (ACoA) aneurysms are well documented to have a higher rupture risk compared with aneurysms at other locations. However, the risk predicting factors for these aneurysms still remain unclear due to the complex arteries geometries and flow patterns involved. The authors introduce a comprehensive method to quantitatively illustrate the development of ACoA aneurysms using a computational fluid dynamics (CFD) approach. Seven ACoA aneurysms, which included 2 ruptured and 5 unruptured aneurysms, were employed. Patient-specific whole anterior circulation geometries were segmented to simulate the real circumstances in vivo. The energy losses (EL) and flow architectures of these 7 aneurysms were evaluated using an algorithm modality. Overall, the 2 ruptured aneurysms, along with 1 unruptured aneurysm that was defined as highly likely to rupture due to ACoA location and a bleb sitting at the top of the dome, had a significantly larger EL and more complex and unstable flow architecture than the others. Two aneurysms had a negative value of EL indicating that the geometries with aneurysms of the anterior communicating complex (ACC) had a smaller loss of energy than the geometries without aneurysms. Despite a small sample size resulting in a low statistical significance, EL may serve as a development predictor of ACoA aneurysms

Performance analysis of the generalised projection identification for time-varying systems

© The Institution of Engineering and Technology 2016. The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the timevarying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided