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The internal wave travels beneath the sea surface and modulate the roughness of the sea surface through the wave-current interaction. This makes some dark and bright bands can be observed in the Synthetic Aperture Radar (SAR) images. In this paper, we first establish the profile of the internal wave based on the KdV equations; then, the action balance equation and the wave-current interaction source function are used to modify the sea spectrum; finally, the two-scale theory based facet model is combined with the modified sea spectrum to calculate the scattering characteristics of the sea. We have simulated the scattering coefficient distribution of the sea with an internal wave traveling through. The influence on the scattering coefficients and the Doppler spectra under different internal wave parameters and sea state parameters are analyzed.

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction. [ABSTRACT FROM AUTHOR]

For reducing the adjacent segment degeneration of the lumbar spine, the interspinous process device as a kind of flexible non-fusion device was designed to overcome the deficiencies associated with rigid fusion devices. However, it was not clear how the interspinous process device influenced the human spine system, especially the lumbar spine under a vibration environment. This study was designed to evaluate the effect of StenoFix under the vibration condition and also to optimize the structure of the device to obtain better biomechanical performance. A finite element model of the intact lumbar spine was developed and validated. The surgical finite element model was constructed by implanting the interspinous process device StenoFix. Using topology optimization, a new device StenoFix-new was redesigned. The results showed that the interspinous process device decreased vibration amplitudes of annulus stress and intradiscal pressure under vibration at the surgical level. The redesigned StenoFix-new with the smaller stiffness exhibited a better dynamic flexibility performance than StenoFix. In addition, the range of motions of StenoFix-new was closer to the intact model than StenoFix at the surgical level. These results might encourage the designers to give more consideration to the dynamic characteristics of the human spine on the premise of ensuring the safety and strength of implanted devices. [ABSTRACT FROM AUTHOR]