Modelling thermomechanical behaviour of Cr-Mo-V steel

This paper presents a mechanism-based approach for modelling the thermomechanical behaviour of a Cr-Mo-V steel. A set of unified viscoplastic constitutive equations were employed to model dislocation density, recrystallisation and grain size during deformation. The evolution of dislocation density accounts for the build-up of dislocations due to plastic strain, the static and dynamic recovery and the effect of recrystallisation. Recrystallisation occurs when a critical dislocation density is reached after an incubation time, and grain size becomes smaller after such event. Gleeble compression tests were used to obtain Stress-strain curves and evaluate the microstructural evolution at different temperature and strain rate, and the material constants for the model were determined from the experimental data. Copyright © 2010 MS&T10®

A Single Self-Supervised Model for Many Speech Modalities Enables Zero-Shot Modality Transfer

While audio-visual speech models can yield superior performance and robustness compared to audio-only models, their development and adoption are hindered by the lack of labeled and unlabeled audio-visual data and the cost to deploy one model per modality. In this paper, we present u-HuBERT, a self-supervised pre-training framework that can leverage both multimodal and unimodal speech with a unified masked cluster prediction objective. By utilizing modality dropout during pre-training, we demonstrate that a single fine-tuned model can achieve performance on par or better than the state-of-the-art modality-specific models. Moreover, our model fine-tuned only on audio can perform well with audio-visual and visual speech input, achieving zero-shot modality generalization for speech recognition and speaker verification. In particular, our single model yields 1.2%/1.4%/27.2% speech recognition word error rate on LRS3 with audio-visual/audio/visual input