Lattice strain evolution in polycrystalline materials and comparison to advanced diffraction measurements

Because of the inhomogeneity in the polycrystalline materials, the nontrivial grain-to-grain and phase-to-phase interaction effects change the deformation behavior of individual grain. By using slip-based crystal plasticity theory, the lattice strain evolution of specific orientation can be evaluated and compared with the experimental observation by advanced diffraction measurements. In a recent in-situ two-dimensional X-ray diffraction test of Ni-based super alloy, the distribution of lattice strain near a round edge notch has been mapped out in a fatigue test. A continuum model is developed to observe the stress distribution near the crack tip and provide a stress/strain distribution and history near the crack tip, which is used as the load input profile in the RVE polycrystalline model. By comparing the lattice strain evolution with diffraction measurements, the intergranular strain effects can be clearly indicated with the strain partition observed in the simulation results. Another simulation based on multiple phases high strength steel is performed to further evaluate the intergranular interaction effects. The multiple phases steel have been investigated to improve the stress-elongation balance and the lattice strain evolution under a tensile loading is tested by in-situ neutron diffraction measurements. The lattice strain partition in three stages is observed in both measurement and simulation and indicates an intergranular interaction existing in the multiple phases steel model

Failure Simulations at Multiple Length Scales in High Temperature Structural Alloys

A number of computational methodologies have been developed to investigate the deformation and damage mechanism of various structural materials at different length scale and under extreme loading conditions, and also to provide insights in the development of high-performance materials. In microscopic material behavior and failure modes, polycrystalline metals of interest include heterogeneous deformation field due to crystalline anisotropy, inter/intra grain or phase and grain boundary interactions. Crystal plasticity model is utilized to simulate microstructure based polycrystalline materials, and micro-deformation information, such as lattice strain evolution, can be captured based on crystal plasticity finite element modeling (CPFEM) in ABAQUS. The comparison of advanced experimental measurement and numerical simulation facilitates the understanding of the deformation and stress partitioning mechanisms in dual phase steel (DP980) and multilayered steel. For corrosion or oxidation induced failure in high temperature alloys, a cohesive zone model (CZM) is introduced to describe the interfacial traction and separation behavior. By coupling diffusion process with CZM, impurity degradation effect at grain boundary can be studied to predict intergranular failure mechanism under corrosive environments. On the other hand, microscopic numerical methods are not efficient or applicable in the damage predictions for structural components. To this end, elastic perfect plastic (EPP) model has been proposed as an efficient tool to evaluate creep and fatigue damage for structural material (nickel based superalloy A617, SS316 etc.) at elevated temperatures. This methodology will be applied in numerous finite element simulations. By comparing with simplified method test data, the feasibility of EPP methodology at elevated temperatures can be verified

Collaborative Spatio-temporal Feature Learning for Video Action Recognition

Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independently (C2D) or jointly with unconstrained parameters (C3D). In this paper, we propose a novel neural operation which encodes spatio-temporal features collaboratively by imposing a weight-sharing constraint on the learnable parameters. In particular, we perform 2D convolution along three orthogonal views of volumetric video data,which learns spatial appearance and temporal motion cues respectively. By sharing the convolution kernels of different views, spatial and temporal features are collaboratively learned and thus benefit from each other. The complementary features are subsequently fused by a weighted summation whose coefficients are learned end-to-end. Our approach achieves state-of-the-art performance on large-scale benchmarks and won the 1st place in the Moments in Time Challenge 2018. Moreover, based on the learned coefficients of different views, we are able to quantify the contributions of spatial and temporal features. This analysis sheds light on interpretability of the model and may also guide the future design of algorithm for video recognition.Comment: CVPR 201

Personalized Phrase Dictionary for Voice Dictation

Users can use voice dictation capabilities of their devices to provide text input (or commands) and/or to edit text by using their voice. Speech recognition mechanisms are imperfect, leading to personalized names and phrases not being correctly transcribed, and requiring cumbersome manual corrections. This disclosure describes personalized automatic corrections to the transcribed text generated from voice dictation using a user-editable personal dictionary of correction pairs. With user permission, the personal dictionary can be generated automatically based on manual corrections to the transcribed text and shared across devices and applications associated with the user. Entries are added to the dictionary only after the user performs the corresponding corrections at least a threshold number of times. With user permission, user-specific and/or contextually relevant terms can be inferred from data sources such as the user\u27s contacts, calendar, interaction data, locations, etc. The techniques reduce the effort to provide manual corrections and enhance the user experience of voice dictation

REST: Robust lEarned Shrinkage-Thresholding Network Taming Inverse Problems with Model Mismatch

We consider compressive sensing problems with model mismatch where one wishes to recover a sparse high-dimensional vector from low-dimensional observations subject to uncertainty in the measurement operator. In particular, we design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. Our proposed network –named Robust lErned Shrinkage-Thresholding (REST) –exhibits additional features including enlarged number of parameters and normalization processing compared to state-of-the-art deep architecture Learned Iterative Shrinkage-Thresholding Algorithm (LISTA), leading to the reliable recovery of the signal under sample-wise varying model mismatch. Our proposed network is also shown to outperform LISTA in compressive sensing problems under sample-wise varying model mismatch