An Integrated Flow-Curing Model for Predicting Residual Stresses in Textile Composites

Fiber-reinforced composites have been widely employed in structural applications due to their high stiffness-to-weight ratio and easily-tailored mechanical properties. However, manufacturing of these lightweight materials involves an infusion and curing process, and flow-induced defects and residual stresses can easily occur, which can compromise the mechanical performance of the composite structures. The purpose of this work is to develop an integrated flow-curing processing model to accurately predict the resin flow front and the residual stresses in the Liquid Composite Molding (LCM) process. In the infusion model, the fiber fabrics are treated as a homogeneous and porous material, and the resin flow movement is governed by the Navier-Stokes equations. The resin flow can be solved using the Volume of Fluid (VOF) method. Due to the pressure equilibrium, the resin flow movement and the compaction of the fiber preform are two-way coupled, and the coupling between the flow and compaction models can be captured in the proposed model. After the infusion simulation, the residual stresses are predicted through a curing model. Since the thermal and chemical strains are major contributing factors to the residual stresses, the temperature and cure progression inside the composite are first predicted through a thermal-chemical analysis. Based on the predicted temperature and cure progression, the residual stress development can be captured through a thermo-viscoelastic model. Based on the elastic moduli of the fiber and viscoelastic properties of the resin, the effective relaxation moduli of the composite can be predicted through the correspondence principle and micromechanics models. The effective composite properties are then included in a 3D anisotropic viscoelastic constitutive law, and the differential form of the constitutive law is developed for numerical implementation to improve the computational efficiency. The accuracy of the processing model is assessed by comparing the simulation results against experiments through a set of benchmark examples. The proposed coupled flow-curing processing model is physics-based and experimentally-validated, which can be employed to understand the variability in composite manufacturing and identify the root causes of processing-induced defects. The integrated model shows great promise as a modeling toolkit to guide the design of optimal manufacturing procedures with minimized defects

Embedding Uncertain Knowledge Graphs

Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different learning objectives. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results on these tasks, and consistently outperforms baselines on these tasks

Evolutionary dynamics under periodic switching of update rules on regular networks

Microscopic strategy update rules play an important role in the evolutionary dynamics of cooperation among interacting agents on complex networks. Many previous related works only consider one \emph{fixed} rule, while in the real world, individuals may switch, sometimes periodically, between rules. It is of particular theoretical interest to investigate under what conditions the periodic switching of strategy update rules facilitates the emergence of cooperation. To answer this question, we study the evolutionary prisoner's dilemma game on regular networks where agents can periodically switch their strategy update rules. We accordingly develop a theoretical framework of this periodically switched system, where the replicator equation corresponding to each specific microscopic update rule is used for describing the subsystem, and all the subsystems are activated in sequence. By utilizing switched system theory, we identify the theoretical condition for the emergence of cooperative behavior. Under this condition, we have proved that the periodically switched system with different switching rules can converge to the full cooperation state. Finally, we consider an example where two strategy update rules, that is, the imitation and pairwise-comparison updating, are periodically switched, and find that our numerical calculations validate our theoretical results

LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network

In this technical report, we present the 1st place solution for the 2023 Waymo Open Dataset Pose Estimation challenge. Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods have commonly relied on 2D image features and 2D sequential annotations for 3D human pose estimation. In contrast, our proposed method, named LPFormer, uses only LiDAR as its input along with its corresponding 3D annotations. LPFormer consists of two stages: the first stage detects the human bounding box and extracts multi-level feature representations, while the second stage employs a transformer-based network to regress the human keypoints using these features. Experimental results on the Waymo Open Dataset demonstrate the top performance, and improvements even compared to previous multi-modal solutions.Comment: Technical report of the top solution for the Waymo Open Dataset Challenges 2023 - Pose Estimation. CVPR 2023 Workshop on Autonomous Drivin