MMH-Net: A novel multi-modal hybrid learning network for accurate mass estimation of acoustic levitated objects

• Published in: Engineering applications of artificial intelligence 2024-10, Vol.136, p.108965, Article 108965
• Main Authors: Wang, Yingwei, Jiang, Liangxu, Chen, Ziyi, Sun, Meiqi, Zhang, Han, Li, Xinbo
• Format: Article
• Language: English
• Subjects: Acoustic levitated; Multi-modal hybrid network; Object mass estimation; Oscillating signal; Variational mode decomposition
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.108965

The acoustic levitation technology expands the possibility of non-contact mass measurements, avoiding contact contamination and loss, particularly for tiny objects. Current acoustic levitated object mass estimation methods focus on the mechanism models between object mass and oscillating frequency. However, the same object exhibits different oscillating behaviors in the acoustic field, especially for non-spherical objects, resulting in obvious variability and non-stationarity in oscillating signals, which limits the estimation accuracy and versatility of mass. To address these challenges, this paper proposes a novel multi-modal hybrid network (MMH-Net) method for accurate mass estimation of levitated objects. This method directly correlates the object’s actual mass with the oscillatory signal. Firstly, to reveal the inherent patterns of the oscillating signals, the variational mode decomposition (VMD) is introduced to decompose the variable oscillating signals into different frequency mode sub-windows. Secondly, a global module and a feature refinement module are constructed to extract the long-term dependencies of the original oscillatory signals and refine the local regularity patterns of the sub-modal signals, respectively. In addition, a weighted fusion strategy is adopted to integrate global and refinement features better. The specially designed dual spectrum self-attention module can assign reasonable weights to information from different sources, facilitating the extraction of representative features and effectively suppressing negative migration. Experimental results demonstrate that the proposed network model performs outstandingly in acoustic levitated object mass estimation. The proposed method is suitable to mass measurements of spherical and non-spherical objects, further promoting the development of acoustic levitation systems in research and application fields.