Texture and Magnetic Property of Fe-6.5 wt % Si Steel Strip with Cu-Rich Particles Modification.

Based on the ordered phase effectively suppressed by rapid solidification technology, the grain refinement concept using Cu is incorporated into the soft magnetic materials. Cu dosage not only could refine the grain size with an average grain size of 8.7 µm but also improve the continuity and consistency of Fe-6.5 wt % Si steel strip. It mainly attributes to the Cu-rich particles precipitating at the grain boundary, nailing the grain boundaries movement and inhibiting the grain growth, and then improving the magnetic properties and mechanical properties. The 1.5 wt % Cu sample exhibits an excellent magnetic property with the saturation magnetization of 236.54 emu/g, which mainly attributes to the strong η, λ, Goss texture formation and the band structure optimization of Si-Cu comodification. Furthermore, the mechanical properties of the steel strip are effectively improved, and the failure plastic deformation of 1.5 wt % Cu steel strip is about 11%. The rapid solidification with Cu-dosage refinement technology also has a remarkable reference on the mechanical properties and magnetic properties modification of other metal materials.

ImTooth: Neural Implicit Tooth for Dental Augmented Reality.

The combination of augmented reality (AR) and medicine is an important trend in current research. The powerful display and interaction capabilities of the AR system can assist doctors to perform more complex operations. Since the tooth itself is an exposed rigid body structure, dental AR is a relatively hot research direction with application potential. However, none of the existing dental AR solutions are designed for wearable AR devices such as AR glasses. At the same time, these methods rely on high-precision scanning equipment or auxiliary positioning markers, which greatly increases the operational complexity and cost of clinical AR. In this work, we propose a simple and accurate neural-implicit model-driven dental AR system, named ImTooth, and adapted for AR glasses. Based on the modeling capabilities and differentiable optimization properties of state-of-the-art neural implicit representations, our system fuses reconstruction and registration in a single network, greatly simplifying the existing dental AR solutions and enabling reconstruction, registration, and interaction. Specifically, our method learns a scale-preserving voxel-based neural implicit model from multi-view images captured from a textureless plaster model of the tooth. Apart from color and surface, we also learn the consistent edge feature inside our representation. By leveraging the depth and edge information, our system can register the model to real images without additional training. In practice, our system uses a single Microsoft HoloLens 2 as the only sensor and display device. Experiments show that our method can reconstruct high-precision models and accomplish accurate registration. It is also robust to weak, repeating and inconsistent textures. We also show that our system can be easily integrated into dental diagnostic and therapeutic procedures, such as bracket placement guidance.

Vox-Surf: Voxel-Based Implicit Surface Representation.

Virtual content creation and interaction play an important role in modern 3D applications. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. In this work, we propose Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides the space into finite sparse voxels, where each voxel is a basic geometry unit that stores geometry and appearance information on its corner vertices. Due to the sparsity inherited from the voxel representation, Vox-Surf is suitable for almost any scene and can be easily trained end-to-end from multiple view images. We utilize a progressive training process to gradually cull out empty voxels and keep only valid voxels for further optimization, which greatly reduces the number of sample points and improves inference speed. Experiments show that our Vox-Surf representation can learn fine surface details and accurate colors with less memory and faster rendering than previous methods. The resulting fine voxels can also be considered as the bounding volumes for collision detection, which is useful in 3D interactions. We also show the potential application of Vox-Surf in scene editing and augmented reality. The source code is publicly available at https://github.com/zju3dv/Vox-Surf.

Recursive identification for choke finger system in wind tunnel.

Choke finger system represents a vital piece of component within a wind tunnel system. Real time grasping the dynamic characteristics of the choke finger system accurately plays an important role in improving the controller design of a wind tunnel system. This research focuses on recursive identifying the choke finger system of the wind tunnel in China Aerodynamics Research and Development Center. According to the analysis results of experiment data, the choke finger system is expressed by a block-oriented model. Based on the model structure, a recursive algorithm is proposed to on-line estimate the unknown model parameters. The algorithm is modified according to the convergence analysis, which enlarges the convergence domain. Applying the proposed algorithm to the wind tunnel test, reasonable parameter estimates and accurate system output estimates are obtained from identification results.