Based on the analyses of the structural feature and the function requirements of newstyle bottle cap, the two fundamental components, the lining washer and the outer body, are abstracted as a plate and a cylinder with...Based on the analyses of the structural feature and the function requirements of newstyle bottle cap, the two fundamental components, the lining washer and the outer body, are abstracted as a plate and a cylinder with thin wall respectively. For simulating the deformation of the lining washer under equiaxial pressure, the modified Lagrangian finite element analysis is applied on the 228 triangular elements. Under assembly pressure, the plastoelastic deformation of both the lining washer and the outer body are studied in terms of Tresca＇s yield criterion and the limitation of the plastic deformation is presented when the two components are assembled into one unit. For the production of this kind of bottle cover, experiments are carried out by carefully measuring the changes of the diameter of lining washer as well as that of the outer body. It is shown that results from the experiments have a good agreement with the theoretical calculation and the maximum value of the allowable pressure has successfully been used in the design of newly developed bottle cap production system.展开更多
Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable drivin...Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable driving road environments and needs post-processing to abstract the lane parameters.In this work,a series of lines are used to represent traffic lanes and a novel line deformation network(LDNet) is proposed to directly predict the coordinates of lane line points.Inspired by the dynamic behavior of classic snake algorithms,LDNet uses a neural network to iteratively deform an initial lane line to match the lane markings.To capture the long and discontinuous structures of lane lines,1 D convolution in LDNet is used for structured feature learning along the lane lines.Based on LDNet,a two-stage pipeline is developed for lane marking detection:(1) initial lane line proposal to predict a list of lane line candidates,and(2) lane line deformation to obtain the coordinates of lane line points.Experiments show that the proposed approach achieves competitive performances on the TuSimple dataset while being efficient for real-time applications on a GTX 1650 GPU.In particular,the accuracy of LDNet with the annotated starting and ending points is up to99.45%,which indicates the improved initial lane line proposal method can further enhance the performance of LDNet.展开更多
基金This project is supported by Provincial Natural Science Fundation of Hei-longjiang, China (No.E0311) and Provincial Key Project of Heilingjiang,China (No.G99A13-1).
文摘Based on the analyses of the structural feature and the function requirements of newstyle bottle cap, the two fundamental components, the lining washer and the outer body, are abstracted as a plate and a cylinder with thin wall respectively. For simulating the deformation of the lining washer under equiaxial pressure, the modified Lagrangian finite element analysis is applied on the 228 triangular elements. Under assembly pressure, the plastoelastic deformation of both the lining washer and the outer body are studied in terms of Tresca＇s yield criterion and the limitation of the plastic deformation is presented when the two components are assembled into one unit. For the production of this kind of bottle cover, experiments are carried out by carefully measuring the changes of the diameter of lining washer as well as that of the outer body. It is shown that results from the experiments have a good agreement with the theoretical calculation and the maximum value of the allowable pressure has successfully been used in the design of newly developed bottle cap production system.
基金Supported by the Science and Technology Research Project of Hubei Provincial Department of Education (No.Q20202604)。
文摘Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable driving road environments and needs post-processing to abstract the lane parameters.In this work,a series of lines are used to represent traffic lanes and a novel line deformation network(LDNet) is proposed to directly predict the coordinates of lane line points.Inspired by the dynamic behavior of classic snake algorithms,LDNet uses a neural network to iteratively deform an initial lane line to match the lane markings.To capture the long and discontinuous structures of lane lines,1 D convolution in LDNet is used for structured feature learning along the lane lines.Based on LDNet,a two-stage pipeline is developed for lane marking detection:(1) initial lane line proposal to predict a list of lane line candidates,and(2) lane line deformation to obtain the coordinates of lane line points.Experiments show that the proposed approach achieves competitive performances on the TuSimple dataset while being efficient for real-time applications on a GTX 1650 GPU.In particular,the accuracy of LDNet with the annotated starting and ending points is up to99.45%,which indicates the improved initial lane line proposal method can further enhance the performance of LDNet.
