Finite element modelling and biodynamic response prediction of the seated human body exposed to whole-body vibration.

Biodynamic modelling of seat-occupant systems can assist in seat comfort design. A finite element (FE) model of the seated human body, including detailed modelling of the lumbar spine, was established to reflect the human response to vibration and biodynamic response of the lumbar spine under whole-body vibration (WBV). The lumbar spine model was established and validated against the in-vitro results and calculated data. The posture of the lumbar spine was adjusted according to the radiological research results, and the adjusted model was combined to establish a FE model of the seated human body. The present seated human model with backrest inclination angles of 10, 20, and 30°, validated by comparing the measured apparent mass and seat-to-lumbar spine transmissibility, was used to calculate the biodynamic response of the lumbar spine with three inclined backrests under WBV. The results showed that the model could characterise the apparent mass, seat-to-lumbar spine transmissibility, and the biodynamic response of the lumbar spine. Practitioner summary: Biodynamic models can represent dynamic characteristics of the human body exposed to vibration and assist in seat comfort design. The three-dimensional FE model of the human body can be used to explore the human response to vibration and the biodynamic response of the lumbar spine under WBV.

Robust Acoustic Imaging Based on Bregman Iteration and Fast Iterative Shrinkage-Thresholding Algorithm.

The acoustic imaging (AI) technique could map the position and the strength of the sound source via the signal processing of the microphone array. Conventional methods, including far-field beamforming (BF) and near-field acoustic holography (NAH), are limited to the frequency range of measured objects. A method called Bregman iteration based acoustic imaging (BI-AI) is proposed to enhance the performance of the two-dimensional acoustic imaging in the far-field and near-field measurements. For the large-scale ℓ1 norm problem, Bregman iteration (BI) acquires the sparse solution; the fast iterative shrinkage-thresholding algorithm (FISTA) solves each sub-problem. The interpolating wavelet method extracts the information about sources and refines the computational grid to underpin BI-AI in the low-frequency range. The capabilities of the proposed method were validated by the comparison between some tried-and-tested methods processing simulated and experimental data. The results showed that BI-AI separates the coherent sources well in the low-frequency range compared with wideband acoustical holography (WBH); BI-AI estimates better strength and reduces the width of main lobe compared with ℓ1 generalized inverse beamforming (ℓ1-GIB).

A Ratio Model of L1/L2 Norm for Sound Source Identification.

In the field of sound source identification, robust and accurate identification of the targeted source could be a challenging task. Most of the existing methods select the regularization parameters whose value could directly affect the accuracy of sound source identification during the solving processing. In this paper, we introduced the ratio model ℓ1/ℓ2 norm to identify the sound source(s) in the engineering field. Using the alternating direction method of multipliers solver, the proposed approach could avoid the selection of the regularization parameter and localize sound source(s) with robustness at low and medium frequencies. Compared with other three methods employing classical penalty functions, including the Tikhonov regularization method, the iterative zoom-out-thresholding algorithm and the fast iterative shrinkage-thresholding algorithm, the Monte Carlo Analysis shows that the proposed approach with ℓ1/ℓ2 model leads to stable sound pressure reconstruction results at low and medium frequencies. The proposed method demonstrates beneficial distance-adaptability and signal-to-noise ratio (SNR)-adaptability for sound source identification inverse problems.