Accurate 3D human pose reconstruction from a single RGB image remains challenging due to scale ambiguity and perspective distortions. Current single-view methods primarily rely on learned priors or kinematic constraints, but they often struggle to maintain geometric consistency with the physical scene. This results in horizon alignment drift and instability when rendered in metric environments. To overcome these limitations, this study introduces a vanishing-point-driven framework that integrates scene geometry into the pose correction process. Under the Manhattan-world assumption, dominant vanishing points are detected to estimate the ground plane and recover the camera orientation with high precision. A lightweight 3D pose estimation network generates initial joint coordinates in camera-centric space. These coordinates are then refined through a VP-based ground-alignment transformation, which resolves scale ambiguity and minimizes geometric drift. The corrected poses are normalized to physical scale and streamed to NVIDIA Omniverse^TM for real-time digital-twin visualization. Experiments conducted on indoor scenes from the NYU Depth V2 dataset demonstrate sub-pixel accuracy in vanishing-point localization and significant improvements in geometric alignment between the reconstructed poses and the true scene layout. This confirms the effectiveness of the proposed approach for single-view digital-twin human modeling.

Facility Layout Problem (FLP) aims to optimize arrangement of facilities to enhance productivity and minimize costs. Traditional methods face challenges in dealing with the complexity and non-linearity of modern manufacturing environments. This study introduced an approach combining Reinforcement Learning (RL) and simulation to optimize manufacturing line layouts. Deep Q-Network (DQN) learns to reduce unused space, improve path efficiency, and maximize space utilization by optimizing facility placement and material flow. Simulations were used to validate layouts and evaluate performance based on production output, path length, and bending frequency. This RL-based method offers a more adaptable and efficient solution for FLP than traditional techniques, addressing both physical and operational optimization.

In this study, kinematic analysis of forward kinematic, inverse kinematic and jacobian for 6-bar parallel robot was analyzed. In order to analyze the maximum workspace of 6-bar parallel robot, maximum revolution range of active joint was calculated. Also, to analyze forward dynamics and inverse dynamics of 6-bar parallel robot, recurdyn and simmechanics was utilized. Using a PI controller and Feedforward controller make an experiment with square motion of end_effector. The reference value of active joint and trace of end_effector were compared with actual experimental value.

This work focuses on the effects of crack free friction on Mode Ⅱ stress intensity factors, K_Ⅱ, for a vertical surface crack in a two-dimensional finite element model of TiN/steel subject to rolling contact. Results indicate that maximum K_Ⅱ values, which occur when the load is adjacent to the crack, may be significantly reduced in the presence of crack face friction. The reduction is more significant for thick coatings than for thin. Crack extension and increased layer thickness result in increased K_Ⅱ values. The effect of crack face friction on compressive K_Ⅰ values appears negligible. Comparative results are presented for MoS₂/steel and diamond-like carbon(DLC)/Ti systems.