calibrating pronlems with structure core

Tensile coupon tests on the investigated steels are modelled in ABAQUS with the explicit solver. Fracture of the tensile coupons is modelled using a critical damage variable. Damage initiation criterion is based on the Rice-Tracey model and damage evolution law is related to the calibrated damaged stress and the estimated undamaged stress. A combined linear and power expression is adopted to calibrate the post-necking damaged stress–strain relations of the investigated steels, upon which the undamaged stress–strain relations are estimated further. This paper presents a practical calibration for the ductile damage model of S355 and high-strength steel S690Q, S700MC, S960Q based on tensile coupon test results. Newell, A., Deng, J.Numerical analyses incorporating appropriate damage models provide an opportunity to predict the strength and deformation capacity of steel structures. Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)

Zhao, Y., Zhu, S.C.: Scene parsing by integrating function, geometry and appearance models (2013) Zhang, R., Lin, L., Wang, G., Wang, M., Zuo, W.: Hierarchical scene parsing by weakly supervised learning with image descriptions. Zhang, J., Shih, K., Tao, A., Catanzaro, B., Elgammal, A.: An interpretable model for scene graph generation (2018) Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context (2017) In: Computer Vision & Pattern Recognition (2016) Yatskar, M., Zettlemoyer, L., Farhadi, A.: Situation recognition: visual semantic role labeling for image understanding. Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing (2017) In: Leibe, B., Matas, J., Sebe, N., Welling, M. Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. Liu, Y., Liu, L., Rezatofighi, H., Do, T.T., Shi, Q., Reid, I.: Learning pairwise relationship for multi-object detection in crowded scenes (2019) Li, Y., Ouyang, W., Zhou, B., Wang, K., Wang, X.: Scene graph generation from objects, phrases and caption regions (2017) Kumar, S., Atreja, S., Singh, A., Jain, M.: Adversarial adaptation of scene graph models for understanding civic issues (2019) Herzig, R., Raboh, M., Chechik, G., Berant, J., Globerson, A.: Mapping images to scene graphs with permutation-invariant structured prediction (2018) KeywordsĪrjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)Īrmeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2D-3D-semantic data for indoor scene understanding (2017) In this paper, we introduce the Self-Calibrating Scene Understanding Model based on the combination between Motifnet and Wasserstein GAN, which emulates the methodologies people cognize the world and brings out an idea that scene understanding, recognized as one of the core technologies towards general artificial intelligence, should be fused with calibrating mechanism. However, as one of the basic concepts constructing physical hierarchies of our world, mutual promotion and calibration within different perceptual contexts are sparsely considered in current work. As one of the most important tools in scene understanding, scene graph generation inspires abundant researches focusing on the reasoning of different relationships among objects. Scene understanding has made significant progress recently that motivates the development of practical applications and accelerates the prosperity of general artificial intelligence.

Calibrating pronlems with structure core