Science and Engineering is an international, peer-reviewed open access journal dedicated to advancing research the field of science and engineering. The journal provides a rapid publication process to ensure wide dissemination of high-quality articles to scientists, professionals, and interested individuals worldwide. Our goal is to serve as an efficient, reliable, and trusted platform for scholars and readers, publishing cutting-edge research in the field.
Abstract: Addressing the difficulty in balancing detection accuracy and model lightweighting in vehicle detection tasks under foggy conditions, this paper proposes an improved foggy vehicle detection algorithm based on RT-DETR-r18. This algorithm overcomes the computational bottleneck of the model. First, an improved backbone network (C2F_DYNCB) module is designed, combining a dynamic convolution weight (DKW) mechanism and parameter sharing (DyInConv) to construct a novel dynamic convolution unit (DAIN Mixer). This module enables adaptive depth convolution and feature fusion, significantly reducing the number of parameters and computations while still maintaining detailed processing of input feature maps. Second, a PEMD module is proposed, introducing the linear attention mechanism Pola Attention and EDFFN, which significantly improves the discriminative power of the attention map and model performance while maintaining linear complexity. Then, a lightweight adaptation block, the MOEN module, is designed to further integrate local information and stabilize feature distribution. Finally, WIoU-v3 is used as the regression loss, adaptively adjusting the weights of positive and negative samples during training to increase attention to subtle bounding boxes. Experimental results show that on the REFYG foggy dataset, the improved algorithm's mAP50 is 3.05 percentage points higher than the original algorithm, and the model's computational complexity and number of parameters are reduced by 39.4% and 32.8%, respectively, achieving significant lightweighting.Abstract: Addressing the difficulty in balancing detection accuracy and model lightweighting in vehicle detection tasks under foggy conditions, this paper proposes an improved foggy vehicle detection algorithm based on RT-DETR-r18. This algorithm overcomes the computational bottleneck of the model. First, an improved backbone network (C2F_DYNCB) module is de...Learn More
Abstract: This study focuses on solving the Allen-Cahn (AC) equation with consideration of varying phase boundary thickness using Physics-Informed Neural Networks (PINN). Traditional numerical methods often exhibit declining computational efficiency and accuracy as problem complexity increases when handling complex phase boundary conditions; PINNs offer a novel approach to addressing such challenges. The study first conducts a detailed derivation of the AC equation and constructs a PINN-based solution model, including three key steps: designing a specific neural network architecture; building a loss function composed of partial differential equation (PDE) residual terms, boundary condition residual terms, and initial condition residual terms; and performing model training. The computational results demonstrate that the established PINN model can effectively solve the AC equation with varying phase boundary thicknesses, accurately revealing the distribution patterns of the order parameter under different phase boundary thicknesses, and adapt to the challenges brought by changes in phase boundary thickness by adjusting the number of iterations to achieve stable and efficient numerical solutions. The phase boundary thickness parameter has a significant impact on order parameter distribution, computational stability, and convergence. Additionally, a discussion is conducted between PINN and the finite difference method. This research provides support for in-depth understanding of the physical connotation of the AC equation and optimization of numerical calculation methods, and also lays a foundation for the application of PINN in solving similar complex problems in fields such as materials science and condensed matter physics. Future work can further expand the application of PINN in high-dimensional phase-field models and multi-physics coupling problems, combine other technologies to improve the accuracy and efficiency of phase-field simulations, and carry out more studies on practical application cases.Abstract: This study focuses on solving the Allen-Cahn (AC) equation with consideration of varying phase boundary thickness using Physics-Informed Neural Networks (PINN). Traditional numerical methods often exhibit declining computational efficiency and accuracy as problem complexity increases when handling complex phase boundary conditions; PINNs offer a no...Learn More
