Oceans and rivers contain abundant renewable low-velocity flow energy without large-scale utilization. Here, we present a novel energy harvester based on vortex resonance to collect the low-velocity flow energy. The harvester is mainly constituted by a cantilever beam, a hollow cylinder, a carrier sheet, and a Solid-Liquid Triboelectric Nanogenerator (SLTENG). Computational fluid dynamic (CFD) and finite element analysis are used to simulate the vortex shedding frequency and the natural frequency of the harvester. After optimization of the structure, the natural frequency is close to the vortex shedding frequency (2 Hz) and reaches resonance. Furthermore, the state of the liquid inside the cylinder is analyzed by CFD under different volumes and diameters of the cylinder.
Domain adaptation has aroused heated interest in medical image analysis due to the universality of cross-modality data, e.g., CT and MRI. Among that, unsupervised domain adaptation has become increasingly important because of the lack of high-quality manual annotations. Deep learning methods have demonstrated the state-of-the-art performance on the above tasks, especially the adversarial learning methods such as Synergistic Image and Feature Alignment (SIFA) network. Based on the elegant benchmark SIFA, this paper presents an improved unsupervised domain adaptation method by introducing a multi-task branch for target image reconstruction. The network is implicitly improved to learn domain-invariant features via the image-level alignment in image reconstruction space. We achieve 82.8 Dice and 4.7 ASD on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, demonstrating that our method is effective in improving segmentation performance on unlabeled target images.
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