Ming Han

Effective low-frequency vibration isolation is crucial for the precise functioning of advanced equipment. To address the degradation of low-frequency vibration isolation in linear control methods, which is a result of accumulated inertial-sensor noise, a disturbance-weighted multi-mode composite H[ ] precision vibration isolation method is proposed. This method combines the absolute velocity and displacement of the involved payload in an H[ ] control framework using specially designed weighting functions to reduce base vibration and sensor noise. The resulting optimal controller minimizes the impact of sensor noise while providing enhanced broadband vibration isolation. Experimental results demonstrate that the proposed method maintains vibration transmissibility below 0[ ]dB for frequencies above 0.2[ ]Hz and achieves approximately [ ][ ]dB transmissibility in the 0.2–5[ ]Hz range. Compared to linear control methods, the proposed method exhibits 8[ ]dB lower transmissibility at 0.125[ ]Hz, effectively mitigating the noise accumulation problem associated with linear control methods and providing enhanced low-frequency vibration isolation. This improved performance shows the method’s potential for application in semiconductor manufacturing equipment, high-performance microscopes, and precision space systems.

Change detection (CD) is a technique widely used in remote sensing for identifying the differences between data acquired at different times. Most existing 3D CD approaches voxelize point clouds into 3D grids, project them into 2D images, or rasterize them into digital surface models due to the irregular format of point clouds and the variety of changes in three-dimensional (3D) objects. However, the details of the geometric structure and spatiotemporal sequence information may not be fully utilized. In this article, we propose PBFormer, a transformer network with Siamese architecture, for directly inferring pointwise changes in bi-temporal 3D point clouds. First, we extract point sequences from irregular 3D point clouds using the k-nearest neighbor method. Second, we uniquely use a point transformer network as an encoder to extract point feature information from bitemporal 3D point clouds. Then, we design a module for fusing the spatiotemporal features of bi-temporal point clouds to effectively detect change features. Finally, multilayer perceptrons are used to obtain the CD results. Extensive experiments conducted on the Urb3DCD benchmark show that PBFormer outperforms other excellent approaches for 3D point cloud CD tasks.