Wavelet Denoising of Flight Flutter Testing Data for Improvement of Parameter Identification

AbstractThe accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequency domain identification algorithms. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A “clean” input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data

Predicting the epidemic threshold of the susceptible-infected-recovered model

Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method, and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues---relationships among differing results and levels of accuracy---by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. When compared to the 56 real-world networks, the epidemic threshold obtained by the DMP method is closer to the actual epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in some scenarios---such as networks with positive degree-degree correlations, with an eigenvector localized on the high $k$-core nodes, or with a high level of clustering---the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input parameter, performs better than the other two methods. We also find that the performances of the three predictions are irregular versus modularity

Hierarchical triple mergers: testing Hawking's area theorem with the inspiral signals

Hawking's area theorem is one of the fundamental laws of black holes (BHs), which has been tested at a confidence level of $\sim 95\%$ with gravitational wave (GW) observations by analyzing the inspiral and ringdown portions of GW signals independently. In this work, we propose to carry out the test in a new way with the hierarchical triple merger (i.e., two successive BH mergers occurred sequentially within the observation window of GW detectors), for which the properties of the progenitor BHs and the remnant BH of the first coalescence can be reliably inferred from the inspiral portions of the two mergers. As revealed in our simulation, a test of the BH area law can be achieved at the significance level of $\gtrsim 3\sigma$ for the hierarchical triple merger events detected in LIGO/Virgo/KAGRA's O4/O5 runs. If the hierarchical triple mergers contribute a $\gtrsim 0.1\%$ fraction to the detected BBHs, a precision test of the BH area law with such systems is achievable in the near future. Our method also provides an additional criterion to establish the hierarchical triple merger origin of some candidate events.Comment: 5 pages, 5 figures, 1 tabl

Electric Field Effect in Multilayer Cr2Ge2Te6: a Ferromagnetic Two-Dimensional Material

The emergence of two-dimensional (2D) materials has attracted a great deal of attention due to their fascinating physical properties and potential applications for future nanoelectronic devices. Since the first isolation of graphene, a Dirac material, a large family of new functional 2D materials have been discovered and characterized, including insulating 2D boron nitride, semiconducting 2D transition metal dichalcogenides and black phosphorus, and superconducting 2D bismuth strontium calcium copper oxide, molybdenum disulphide and niobium selenide, etc. Here, we report the identification of ferromagnetic thin flakes of Cr2Ge2Te6 (CGT) with thickness down to a few nanometers, which provides a very important piece to the van der Waals structures consisting of various 2D materials. We further demonstrate the giant modulation of the channel resistance of 2D CGT devices via electric field effect. Our results illustrate the gate voltage tunability of 2D CGT and the potential of CGT, a ferromagnetic 2D material, as a new functional quantum material for applications in future nanoelectronics and spintronics.Comment: To appear in 2D Material