Shijiazhuang Tiedao University

The performance of sandwich beams in high-speed railway systems experiences severe challenges because their dynamic stability and safety require material configuration tests under high dynamic loads. The research studies sandwich beam performance, which consists of 2D functionally graded face sheets and honeycomb core structures when exposed to moving loads that mimic high-speed train operations. The higher-order shear deformation theory (HSDT) enables accurate modeling of transverse shear effects in thick and layered sandwich structures without the need for shear correction factors. The researchers used Hamilton’s principle to create the governing equations of motion through a detailed process, which included both moving load kinematics and material gradation in face sheets. The researchers applied the differential quadrature method (DQM) to discretize the coupled partial differential equations, which achieved high precision using a minimal number of grid points, while the Newmark method was used for time integration to maintain numerical stability and operational efficiency. Researchers conduct parametric studies to study how different factors impact dynamic stability limits and vibration behavior of sandwich beams, which include load velocity and material gradation indices and honeycomb core characteristics and geometric parameters. The development of a hybrid machine learning system enables better verification of numerical results, which enhances the generated confidence level. The machine learning models achieve training success through the DQM-Newmark solutions, which function as both verification systems and quick assessment tools. Engineers can employ the combined theoretical-numerical-data-driven method as a robust framework to assess dynamic stability and safety for advanced sandwich beam structures in high-speed railway systems.

Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF) addresses these challenges through several innovations. It uses spatially optimized sampling to focus on points near object surfaces, reducing computation while improving precision. Leveraging the pre-trained Marigold model, it generates depth and normal maps as geometric priors. Sampled points are processed through a hybrid network combining an MLP and a multi-resolution feature grid (MRF), capturing fine details and large-scale structures. To handle varying illumination and complex materials, OptiNeRF introduces adaptive volume rendering (AVR), dynamically adjusting light transparency and scattering. A progressive sampling strategy further focuses computation on regions with high geometric complexity. The loss function incorporates RGB, normal, depth, boundary, and lighting optimization losses, with adaptive weight modulation for geometric priors, ensuring both visual fidelity and geometric consistency even with inaccurate depth/normal estimates. Experiments on dynamic scenes show strong performance, with a PSNR of 32.10 dB, SSIM of 0.936, Chamfer distance of 1.28×10−3, training time of 12 h, and rendering speed of 25 FPS, demonstrating high geometric accuracy, realistic rendering, and computational efficiency over conventional methods.

Jumping is a critical capability for quadruped robots, especially for navigating obstacles and gaps in complex environments. For successful jump, accurate trajectory tracking and robust feedback mechanism are essential, as cumulative deviations from the desired jumping trajectory can lead to instability or landing failure. Existing controllers often rely on fixed joint-level PD control or simplified inverse dynamics, which often fall short in tracking accuracy and robustness. In this paper, we propose a phase-aware iterative Linear Quadratic Regulator (iLQR) framework tailored for dynamic quadruped jumping tasks. By segmenting the jumping motion into distinct phases, we define phase-wise optimal control problem that respects the unique characteristics and requirements of each stage. Moreover, by leveraging a planar full-body dynamics of quadruped in each iLQR sub-problem, we derive a tracking controller consisting time-varying, full-state feedback gains, which shows better performance in tracking accuracy and disturbances rejection over traditional baseline controllers. Extensive simulation and hardware experiments on the Deeprobotics Lite3 quadruped validate the effectiveness and reliability of our proposed method in a number of dynamic jumping scenarios.