Search

This study investigates a mechanism to generate the wavenumber-3 longitude variation in the ionosphere, using the simulations with the Global Coupled Ionosphere Thermosphere Electrodynamics Model, developed by the Institute of Geology and Geophysics, Chinese Academy of Sciences (GCITEM-IGGCAS). Due to the asymmetry of geomagnetic field, the asymmetric Hough mode of diurnal eastward wavenumber-3 (DE3) also produces the WN3 structure in the ionosphere by coupling with the magnetic line. The densities of the neutral mass and the plasmas in the ionosphere are studied in detail. The results show a clear WN3 pattern driven by tide's electro-dynamical coupling. We then conclude that the asymmetric component of the DE3 can also cause the WN3 structure in the ionosphere, which confirms the assumption that more than one source could generate WN3 structure in previous studies. [ABSTRACT FROM AUTHOR]

Underwater wet laser cladding technology has become one of the research hotspots in the marine engineering, but the existence of the water environment inevitably leads to process instability and defects. In this study, the nano-ZrO 2 protective layer was used to increase the viscosity of the molten pool surface to resist the impact force generated by the high-speed water jet, and the underwater wet laser Fe-based cladding layer with good forming quality and desirable performance was successfully prepared. The nano-ZrO 2 protective layer improved the formability of the cladding layer and guaranteed the depth of the melt pool, while reducing the depth of the heat affected zone. The addition of nano-ZrO 2 particles reduced the average grain size of the cladding layer from 4.90 μm to 3.66 μm. In addition,the corrosion resistance of the cladding layer was greatly improved with the application of the nano-ZrO 2 protective layer, with the evident passivation region of 0.74 V. The wear rate of the cladding layer with the nano-ZrO 2 protective layer (0.64×10-15m3N-1m-1) was significantly lower than that of the substrate (9.01×10-15m3N-1m-1), and the excellent wear resistance of the cladding layer with nano-ZrO 2 protective layer benefited from the good carrying capacities and high microhardness of the microstructure with ZrO 2 particles. This study reveals the strengthening mechanism of nano-ZrO 2 protective layer on the Fe-based cladding layer, which can provide theoretical guidance for the development of the underwater laser cladding. • The Fe-based cladding layer with good formability was successfully prepared underwater. • The protective mechanism of the nano-ZrO 2 protective layer is studied in detail. • The nano-ZrO 2 protective layer can refine grains and decline heat effect. • The corrosion and wear performances of Fe-based cladding layer were improved by nano-ZrO 2 protective layer. • The nano-ZrO 2 induced grain refinement enabled Fe-based cladding layer to be further wear resistant. [ABSTRACT FROM AUTHOR]

• In view of the necessity and research value, we are the first to specialize in studying fine-grained classification under the 3D point clouds representation, providing a new perspective for 3D shape classification. • Through in-depth analysis of the characteristics of the target object (3D point clouds) and the key to fine-grained classification tasks, design a feature extraction model to effectively learn the discriminative features. • To highlight discriminative local regions and captures spatial differences between 3D point clouds from different sub-categories, propose a module to capture spatial structure feature and aggerate local features. 3D point clouds classification has been a hot research topic and received great progress in recent years. However, due to the similar data distributions and subtle differences among various sub-categories in a meta-category, the 3D point clouds classification at a fine-grained level is still very challenging, especially without the annotations of part locations or attributes. In this paper, we propose a novel weakly supervised network for fine-grained 3D point clouds classification, namely FGPNet. Different from the previous supervised fine-grained classification methods that use class labels and other manual annotation information, FGPNet develops a unified framework to address both local geometric details and global spatial structures only using the class labels as input. Specifically, FGPNet firstly employs a context-aware discriminative feature extraction (CDFE) module, which extract contextual contrasted information across differential receptive fields hierarchically, and further capture discriminative local details from point clouds. Subsequently, an SimAM-Capsule Aggregation (SCA) module is introduced to highlight the significant local features and capture their spatial relationships. Quantitative and qualitative experimental results on fine-grained dataset including three categories Airplane, Chair and Car demonstrate that FGPNet outperforms the state-of-the-art methods on fine-grained 3D point clouds classification tasks. [ABSTRACT FROM AUTHOR]