Because of the developed surface of the Triply PeriodicMinimumSurface(TPMS)structures,polylactide(PLA)products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted dru...Because of the developed surface of the Triply PeriodicMinimumSurface(TPMS)structures,polylactide(PLA)products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery.For implants,mechanical properties are key performance characteristics,so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure.The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research.Two TPMS topologies,the Schwarz Diamond and Gyroid architectures,were used for the sample construction by 3D printing.ANSYS software was utilized to simulate compressive deformation.It was found that under the same load,the vonMises stresses in the Gyroid structure are higher than those in the Schwartz Diamond structure,which was associated with the different orientations of the cells in the studied structures in relation to the direction of the loading axis.The deformation process occurs in the local regions of the studied TPMS structures.Maximum von Mises stresses were observed in the vertical parts of the structures oriented along the load direction.It was found that,unlike the Gyroid,the Schwartz Diamond structure contains a frame that forms unique stiffening ribs,which ensures the redistribution of the load under the vertical loading direction.An analysis of the mechanical characteristics of PLA samples with the Schwartz Diamond and Gyroid structures produced by the Fused Deposition Modeling(FDM)method was correlated with computer simulation.The Schwarz Diamond-type structure was shown to have a higher absorption energy than the Gyroid one.A study of the fracture in PLA samples with various cell sizes revealed a particular feature related to the samples’periodic surface topology and the 3D printing process.Scanning electron microscopic(SEM)studies of the samples deformed by compression showed thatwith an increase in the density of the samples,the failure mechanism changes from ductile to quasi-brittle due to the complex participation of both cell deformation and fiber deformation.展开更多
This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limita...This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.展开更多
Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback com...Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded.展开更多
文摘Because of the developed surface of the Triply PeriodicMinimumSurface(TPMS)structures,polylactide(PLA)products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery.For implants,mechanical properties are key performance characteristics,so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure.The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research.Two TPMS topologies,the Schwarz Diamond and Gyroid architectures,were used for the sample construction by 3D printing.ANSYS software was utilized to simulate compressive deformation.It was found that under the same load,the vonMises stresses in the Gyroid structure are higher than those in the Schwartz Diamond structure,which was associated with the different orientations of the cells in the studied structures in relation to the direction of the loading axis.The deformation process occurs in the local regions of the studied TPMS structures.Maximum von Mises stresses were observed in the vertical parts of the structures oriented along the load direction.It was found that,unlike the Gyroid,the Schwartz Diamond structure contains a frame that forms unique stiffening ribs,which ensures the redistribution of the load under the vertical loading direction.An analysis of the mechanical characteristics of PLA samples with the Schwartz Diamond and Gyroid structures produced by the Fused Deposition Modeling(FDM)method was correlated with computer simulation.The Schwarz Diamond-type structure was shown to have a higher absorption energy than the Gyroid one.A study of the fracture in PLA samples with various cell sizes revealed a particular feature related to the samples’periodic surface topology and the 3D printing process.Scanning electron microscopic(SEM)studies of the samples deformed by compression showed thatwith an increase in the density of the samples,the failure mechanism changes from ductile to quasi-brittle due to the complex participation of both cell deformation and fiber deformation.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00245084)by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00415938,HRD Program for Industrial Innovation)and Soonchunhyang University.
文摘This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.
基金supported by the National Science and Technology Innovation 2030-Major Program(2022ZD 0115403)the National Natural Science Foundation of China(61991414)+1 种基金Chongqing Natural Science Foundation(CSTB2023NSCQJQX0018)Beijing Natural Science Foundation(L221005)
文摘Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded.
