Quasi-B-mode generated by high-frequency gravitational waves and corresponding perturbative photon fluxes

Interaction of very low-frequency primordial(relic) gravitational waves(GWs) to cosmic microwave background(CMB) can generate B-mode polarization. Here, for the first time we point out that the electromagnetic(EM) response to high-frequency GWs(HFGWs) would produce quasi-B-mode distribution of the perturbative photon fluxes, and study the duality and high complementarity between such two B-modes. Based on this quasi-B-mode in HFGWs, it is shown that the distinguishing and observing of HFGWs from the braneworld would be quite possible due to their large amplitude, higher frequency and very different physical behaviors between the perturbative photon fluxes and background photons, and the measurement of relic HFGWs may also be possible though face to enormous challenge.Comment: 22 pages, 6 figures, research articl

SEISMIC DATA MULTI-SPECTRAL ANALYSIS, ATTENUATION ESTIMATION AND SEISMIC SEQUENCE STRATIGRAPHY ENHANCEMENT APPLIED TO CONVENTIONAL AND UNCONVENTIONAL RESERVOIRS

Seismic data are an essential resource for interpretation, providing abundant information about geological structures, sedimentation, stratigraphy and reservoir quality. Geophysicists have dedicated tremendous efforts in fully utilizing the information content in seismic data. Time series analysis and frequency (spectral) analysis are the two most common tools used to characterize seismic data. Multi-spectral analysis highlights geological features at different scales. The spectral sensitivity is not only from the tuning effects, but also from the geological structures and rock properties, including attenuation. To analyze the additional information in the spectral components rather than in the broad-band data, I begin by examining the spectrally limited coherence responses of multiple stages of incised valleys of Red Fork formation, Anadarko Basin, Oklahoma. Later, I combine covariance matrices for each spectral component, add them together, and compute multi-spectral coherence images. Spectral ratio and frequency shift methods are traditional attenuation estimation methods. However, the assumptions of each method introduce errors and instabilities into the results. I propose a modified frequency shift method to estimate attenuation (the reciprocal of the quality factor, Q), that relaxes some of these assumptions. Synthetic and field applications show robust and accurate results. Thin-bed layering also modifies the spectra, causing simple attenuation estimation to be inaccurate. To address this limitation, I use well logs based impedance inversion results to calculate a spectral correction for elastic variability in the spectra prior to estimating the inelastic attenuation contribution. The spectral correction can be viewed as a pre-conditioning step, following which both spectral ratio and frequency shift methods can produce better results. xix Traditional attenuation estimation methods work well in high porosity and high permeability gas sands. However, the well accepted squirt model does not apply to low permeability shale reservoirs. Rather, micro-cracks generate strong geometric or scattering attenuation, which combined with the intrinsic attenuation produced by TOC (total organic carbon) result in complicated spectral responses. Rather than estimating Q, I evaluate a suite of attenuation attributes. Even though the mechanism underlying may be unknown, these attenuation attributes can be statistically linked to the production and geology. Using the classic Fourier transform, the available spectral band often falls between 10 and 80 Hz. Nevertheless, interpreters observe lower frequency patterns in the data, for example, a 200 ms thick (5 Hz) pattern of low reflectivity sandstone and a 400 ms thick (2.5 Hz) pattern of high reflectivity responses (e.g. sabkhas or cyclothems). I introduce an adaptive intrinsic mode decomposition method called variational mode decomposition to analyze the “rhythm” in the seismic data. The intrinsic modes are defined as combinations of AM modulated signals, which are analyzed in the frequency domain with carrier frequencies (that fall within the 10-80 Hz limit), to characterize the buried stratigraphy information seen in the longer wavelength patterns. Because intrinsic modes are able to model seismic signals, but unable to model the noise component, the random noise lies within the residual of the intrinsic mode decomposition. Unlike filtering methods with predefined parameters, I develop a fully data-driven denoising method to suppress random noise, thereby enhancing the data quality

Cybersecurity Strategy against Cyber Attacks towards Smart Grids with PVs

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data structure, our approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. For comparison, we have evaluated classic data-driven methods, including -nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN). Comparison results verify the performance of the proposed method for detection and diagnosis of various cyber attacks on PV systems

HAL: Improved Text-Image Matching by Mitigating Visual Semantic Hubs

The hubness problem widely exists in high-dimensional embedding space and is a fundamental source of error for cross-modal matching tasks. In this work, we study the emergence of hubs in Visual Semantic Embeddings (VSE) with application to text-image matching. We analyze the pros and cons of two widely adopted optimization objectives for training VSE and propose a novel hubness-aware loss function (HAL) that addresses previous methods' defects. Unlike (Faghri et al.2018) which simply takes the hardest sample within a mini-batch, HAL takes all samples into account, using both local and global statistics to scale up the weights of "hubs". We experiment our method with various configurations of model architectures and datasets. The method exhibits exceptionally good robustness and brings consistent improvement on the task of text-image matching across all settings. Specifically, under the same model architectures as (Faghri et al. 2018) and (Lee at al. 2018), by switching only the learning objective, we report a maximum R@1improvement of 7.4% on MS-COCO and 8.3% on Flickr30k.Comment: AAAI-20 (to appear

Mask-guided modality difference reduction network for RGB-T semantic segmentation

By exploiting the complementary information of RGB modality and thermal modality, RGB-thermal (RGB-T) semantic segmentation is robust to adverse lighting conditions. When fusing features from RGB images and thermal images, the existing methods design different feature fusion strategies, but most of these methods overlook the modality differences caused by different imaging mechanisms. This may result in insufficient usage of complementary information. To address this issue, we propose a novel Mask-guided Modality Difference Reduction Network (MMDRNet), where the mask is utilized in the image reconstruction to ensure that the modality discrepancy within foreground regions is minimized. Doing so enables the generation of more discriminative representations for foreground pixels, thus facilitating the segmentation task. On top of this, we present a Dynamic Task Balance (DTB) method to balance the modality difference reduction task and semantic segmentation task dynamically. The experimental results on the MFNet dataset and the PST900 dataset demonstrate the superiority of the proposed mask-guided modality difference reduction strategy and the effectiveness of the DTB method