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Ground-penetrating radar (GPR) has been widely applied to the nondestructive inspection of concrete structures such as tunnel lining, bridge deck, and retaining wall, which are usually reinforced by steel bars. The scattering of electromagnetic (EM) waves caused by the dense steel rebar embedded in the concrete structures has a severe influence on the penetration capacity of GPR waves. In this letter, the scattering and penetration characteristics of EM waves propagating through rebar net are investigated via both numerical and laboratory experiments, with an aim to select the antenna nominal frequency for a different reinforcement density. The results show that the rebar, which is perpendicular to the polarization direction of GPR waves and has a very small diameter compared with the wavelength, is almost transparent to the impinged GPR waves. The scattering and interaction of GPR waves caused by the rebar that is parallel to the polarization direction result in a shielding effect, which is manifested as a blind band in the low-frequency range in the transmitted spectrum. This result violates the rule of thumb commonly used in the GPR community, i.e., the lower frequency has a deeper GPR penetration depth. In the end, a low cutoff frequency is recommended for selecting a GPR antenna with an appropriate nominal frequency when it is used in the detection of an anomaly inside and behind a reinforced concrete structure, in which the spacing of the rebar net is known. [ABSTRACT FROM AUTHOR]

Lifelong topic model (LTM), an emerging paradigm for never-ending topic learning, aims to yield higher-quality topics as time passes through knowledge accumulated from the past yet learned for the future. In this paper, we propose a novel lifelong topic model based on non-negative matrix factorization (NMF), called Affinity Regularized NMF for LTM (NMF-LTM), which to our best knowledge is distinctive from the popular LDA-based LTMs. NMF-LTM achieves lifelong learning by introducing word-word graph Laplacian as semantic affinity regularization. Other priors such as sparsity, diversity, and between-class affinity are incorporated as well for better performance, and a theoretical guarantee is provided for the algorithmic convergence to a local minimum. Extensive experiments on various public corpora demonstrate the effectiveness of NMF-LTM, particularly its human-like behaviors in two carefully designed learning tasks and the ability in topic modeling of big data. A further exploration of semantic relatedness in knowledge graphs and a case study on a large-scale real-world corpus exhibit the strength of NMF-LTM in discovering high-quality topics in an efficient and robust way. [ABSTRACT FROM AUTHOR]