Damage detection in bridges under moving loads based on subspace projection residuals

This paper proposes a data-driven method using subspace projection residual of the responses to identify the damage locations in bridges subjected to moving loads. In this method, a moving window with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses is used to cut out the acceleration responses of the bridge subjected to a moving vehicle. The characteristic subspaces of the windowed signals are subsequently extracted to calculate the local damage index using the subspace projection residual. When the window moves to the damage location, the orthogonality between the active subspace of the damaged state and the null subspace of the healthy state is invalid, which leads to a relatively large projection residual that can be used to localize the damage. To improve the reliability of the proposed approach, a one-side upper confidence limit is introduced. A simply supported beam bridge subjected to a moving mass is simulated to verify the effectiveness of the proposed method. Numerical results indicate that the proposed approach can accurately localize the single and multiple damages, even when the responses are smeared with a significant noise. Experimental tests conducted on a steel beam bridge model also demonstrate the performance and accuracy of the proposed approach. The results demonstrate that the proposed method can localize the damage even with a small number of sensors, indicating the method has a good and promising performance for practical engineering applications.

HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON MANIFOLD DATA ANALYSIS AND SPARSE SUBSPACE PROJECTION

Aiming at the problem of "dimension disaster" in hyperspectral image classification, a method of dimension reduction based on manifold data analysis and sparse subspace projection (MDASSP) is proposed. The sparse coefficient matrix is established by the new method, and the sparse subspace projection is carried out by the optimization method. To keep the geometric structure of the manifold, the objective function is regularized by the manifold learning method. The new method combines sparse coding and manifold learning to generate features with better classification ability. The experimental results show that the new method is better than other methods in the case of small samples.

A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

HPS meets AMPS: how soft hair dissolves the firewall

We build on the observation by Hawking, Perry and Strominger that a global black hole space-time supports a large number of soft hair degrees of freedom to shed new light on the firewall argument by Almheiri, Marolf, Polchinski, and Sully. We propose that the soft hair Goldstone mode is encoded in a classical transition function that connects the asymptotic and near horizon region. The entropy carried by the soft hair is part of the black hole entropy and encoded in the outside geometry. We argue that the infalling observer automatically measures the classical value of the soft mode before reaching the horizon and that this measurement implements a code subspace projection that enables the reconstruction of interior operators. We use the soft hair dynamics to introduce an observer dependent notion of the firewall and show that for an infalling observer it recedes inwards into the black hole interior: the observer never encounters a firewall before reaching the singularity. Our results indicate that the HPS black hole soft hair plays an essential role in dissolving the AMPS firewall.

Wideband RFI Cancellation for High-sensitivity Phased Array Receivers with True Time Delays and Truncated Hadamard Projection

<div>Radio frequency interference (RFI) is a significant challenge for high-sensitivity phased array instruments. RFI can be suppressed using digital signal processing, but to improve dynamic range for wideband RFI, it can be desirable to remove interference in the analog domain before sampling. In previous work, it has been shown that analog true time delay (TTD) stages with a truncated Hadamard transform can place a wide-band spatial null on RFI from a given direction of arrival. We show that TTD and Hadamard projection is mathematically equivalent to the classical, narrow-band subspace projection beamformer, but with a structure that allows efficient implementation in analog circuitry. Simulation results show that TTD and Hadamard projection can place deep nulls on wideband RFI signals and still achieve SNR performance comparable to the optimal digital maximum signal to interference and noise ratio beamformer.</div>

Wideband RFI Cancellation for High-sensitivity Phased Array Receivers with True Time Delays and Truncated Hadamard Projection

<div>Radio frequency interference (RFI) is a significant challenge for high-sensitivity phased array instruments. RFI can be suppressed using digital signal processing, but to improve dynamic range for wideband RFI, it can be desirable to remove interference in the analog domain before sampling. In previous work, it has been shown that analog true time delay (TTD) stages with a truncated Hadamard transform can place a wide-band spatial null on RFI from a given direction of arrival. We show that TTD and Hadamard projection is mathematically equivalent to the classical, narrow-band subspace projection beamformer, but with a structure that allows efficient implementation in analog circuitry. Simulation results show that TTD and Hadamard projection can place deep nulls on wideband RFI signals and still achieve SNR performance comparable to the optimal digital maximum signal to interference and noise ratio beamformer.</div>