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Petr Krysl - One of the best experts on this subject based on the ideXlab platform.
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mean strain 10 node tetrahedron with energy sampling stabilization for nonlinear deformation
International Journal for Numerical Methods in Engineering, 2017Co-Authors: Alireza Pakravan, Petr KryslAbstract:Summary A mean-strain 10-node tetrahedral element is developed for the solution of geometrically nonlinear solid mechanics problems using the concept of energy-sampling stabilization. A uniform-strain tetrahedron for applications to linear elasticity was recently described. The formulation as extended here is able to solve large-strain hyperelasticity. The present 10-node tetrahedron is composed of several four-node linear tetrahedral elements, four tetrahedra in the corners, and four tetrahedra that tile the central octahedron in three possible sets of four-node tetrahedra, corresponding to three different choices for the internal diagonal. We formulate a mean-strain element with stabilization energy evaluated on the four corner tetrahedra, which is shown to Guarantee Consistency and stability. The stabilization energy is expressed through a stored-energy function, and contact with input parameters in the small-strain regime is made. The neo-Hookean model is used to formulate the stabilization energy. As for small-strain elasticity, the stabilization parameters are determined by actual material properties and geometry of a tetrahedra without any user input. The numerical tests demonstrate that the present element performs well for solid, shell, and nearly incompressible structures. Copyright © 2016 John Wiley & Sons, Ltd.
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mean strain 8 node hexahedron with optimized energy sampling stabilization
Finite Elements in Analysis and Design, 2016Co-Authors: Petr KryslAbstract:A method for stabilizing the mean-strain hexahedron was described by Krysl (in IJNME 2014). The technique relied on a sampling of the stabilization energy using the mean-strain quadrature and the full Gaussian integration rule, which was shown to Guarantee Consistency and stability. The stabilization energy was assumed to be generated by a modified constitutive matrix based on the spectral decomposition. The stabilization required user-selected values of the stabilization parameters. In the present work we eliminate the arbitrariness of the stabilization parameters. We formulate the technique more precisely as an assumed-strain method, and we express the stabilization energy in terms of input parameters of the real material. Finally, we fix the value of the stabilization parameters in a quasi-optimal manner by linking the stabilization to the bending behavior of the hexahedral element. For simplicity the developments are limited to linear elasticity, but with an arbitrarily anisotropic elasticity matrix. The accuracy and convergence characteristics of the present formulations compare favorably with the capabilities of mean-strain and other high-performance hexahedral elements as implemented in Abaqus and with a number of successful hexahedral and shell elements and we demonstrate that the present element performs very well when used with large aspect ratios for thin structures such as plates or shells.
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mean strain eight node hexahedron with optimized energy sampling stabilization for large strain deformation
International Journal for Numerical Methods in Engineering, 2015Co-Authors: Petr KryslAbstract:Summary A method for stabilizing the mean-strain hexahedron for applications to anisotropic elasticity was described by Krysl (2015). The technique relied on a sampling of the stabilization energy using the mean-strain quadrature and the full Gaussian integration rule. This combination was shown to Guarantee Consistency and stability. The stabilization energy was expressed in terms of input parameters of the real material, and the value of the stabilization parameter was fixed in a quasi-optimal manner by linking the stabilization to the bending behavior of the hexahedral element (Krysl, submitted). Here, the formulation is extended to large-strain hyperelasticity (as an example, the formulation allows for inelastic behavior to be modeled). The stabilization energy is expressed through a stored-energy function, and contact with input parameters in the small-strain regime is made. As for small-strain elasticity, the stabilization parameter is determined to optimize bending performance. The accuracy and convergence characteristics of the present formulations for both solid and thin-walled structures (shells) compare favorably with the capabilities of mean-strain and other high-performance hexahedral elements described in the open literature and also with a number of successful shell elements. Copyright © 2015 John Wiley & Sons, Ltd.
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mean strain eight node hexahedron with stabilization by energy sampling
International Journal for Numerical Methods in Engineering, 2015Co-Authors: Petr KryslAbstract:Summary A method for stabilizing the mean-strain hexahedron is described that differs from the currently known approaches. For simplicity, the developments are limited to linear elasticity but with an arbitrarily anisotropic elasticity matrix. The technique relies on a sampling of the stabilization energy using two quadrature rules, the mean-strain quadrature and the full Gaussian integration rule. The use of two quadrature rules is shown to Guarantee Consistency and stability. The stabilization energy is assumed to be generated by a modified constitutive matrix based on the spectral decomposition. The spectral decomposition of the constitutive matrix identifies the stiff and flexible modes of deformation. The stiff modes of deformation are only sampled by the mean-strain integration, which eliminates volumetric locking for isotropic materials as well as locking due to strongly anisotropic material properties. The accuracy and convergence characteristics of the present formulations compare favorably with the capabilities of mean-strain and other high-performance hexahedral elements as implemented in ABAQUS. Copyright © 2014 John Wiley & Sons, Ltd.
Abdelrahman H Tawil - One of the best experts on this subject based on the ideXlab platform.
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scalable service oriented replication with flexible Consistency Guarantee in the cloud
Information Sciences, 2014Co-Authors: Tao Chen, Rami Bahsoon, Abdelrahman H TawilAbstract:Replication techniques are widely applied in and for cloud to improve scalability and availability. In such context, the well-understood problem is how to Guarantee Consistency amongst different replicas and govern the trade-off between Consistency and scalability requirements. Such requirements are often related to specific services and can vary considerably in the cloud. However, a major drawback of existing service-oriented replication approaches is that they only allow either restricted Consistency or none at all. Consequently, service-oriented systems based on such replication techniques may violate Consistency requirements or not scale well. In this paper, we present a Scalable Service Oriented Replication (SSOR) solution, a middleware that is capable of satisfying applications' Consistency requirements when replicating cloud-based services. We introduce new formalism for describing services in service-oriented replication. We propose the notion of Consistency regions and relevant service oriented requirements policies, by which trading between Consistency and scalability requirements can be handled within regions. We solve the associated sub-problem of atomic broadcasting by introducing a Multi-fixed Sequencers Protocol (MSP), which is a requirements aware variation of the traditional fixed sequencer approach. We also present a Region-based Election Protocol (REP) that elastically balances the workload amongst sequencers. Finally, we experimentally evaluate our approach under different loads, to show that the proposed approach achieves better scalability with more flexible Consistency constraints when compared with the state-of-the-art replication technique.
Tao Chen - One of the best experts on this subject based on the ideXlab platform.
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scalable service oriented replication with flexible Consistency Guarantee in the cloud
Information Sciences, 2014Co-Authors: Tao Chen, Rami Bahsoon, Abdelrahman H TawilAbstract:Replication techniques are widely applied in and for cloud to improve scalability and availability. In such context, the well-understood problem is how to Guarantee Consistency amongst different replicas and govern the trade-off between Consistency and scalability requirements. Such requirements are often related to specific services and can vary considerably in the cloud. However, a major drawback of existing service-oriented replication approaches is that they only allow either restricted Consistency or none at all. Consequently, service-oriented systems based on such replication techniques may violate Consistency requirements or not scale well. In this paper, we present a Scalable Service Oriented Replication (SSOR) solution, a middleware that is capable of satisfying applications' Consistency requirements when replicating cloud-based services. We introduce new formalism for describing services in service-oriented replication. We propose the notion of Consistency regions and relevant service oriented requirements policies, by which trading between Consistency and scalability requirements can be handled within regions. We solve the associated sub-problem of atomic broadcasting by introducing a Multi-fixed Sequencers Protocol (MSP), which is a requirements aware variation of the traditional fixed sequencer approach. We also present a Region-based Election Protocol (REP) that elastically balances the workload amongst sequencers. Finally, we experimentally evaluate our approach under different loads, to show that the proposed approach achieves better scalability with more flexible Consistency constraints when compared with the state-of-the-art replication technique.
Honghui Shi - One of the best experts on this subject based on the ideXlab platform.
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alleviating semantic level shift a semi supervised domain adaptation method for semantic segmentation
Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for semantic segmentation can significantly relieve human efforts in labelling pixel-level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot Guarantee Consistency from a local view (i.e. semantic-level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-level Shift (ASS), which can promote the distribution Consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
Frank Pfenning - One of the best experts on this subject based on the ideXlab platform.
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Intensionality, Extensionality, and Proof Irrelevance in Modal Type Theory
2018Co-Authors: Frank PfenningAbstract:We develop a uniform type theory that integrates intensionality, extensionality and proof irrelevance as judgmental concepts. Any object may be treated intensionally (subject only to α-conversion), extensionally (subject also to βη-conversion), or as irrelevant (equal to any other object at the same type), depending on where it occurs. Modal restrictions developed by R. Harper et al. (2000) for single types are generalized and employed to Guarantee Consistency between these views of objects. Potential applications are in logical frameworks, functional programming and the foundations of first-order modal logics. Our type theory contrasts with previous approaches that, a priori, distinguished propositions (whose proofs are all identified - only their existence is important) from specifications (whose implementations are subject to some definitional equalities
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intensionality extensionality and proof irrelevance in modal type theory
Logic in Computer Science, 2001Co-Authors: Frank PfenningAbstract:We develop a uniform type theory that integrates intensionality, extensionality and proof irrelevance as judgmental concepts. Any object may be treated intensionally (subject only to /spl alpha/-conversion), extensionally (subject also to /spl beta//spl eta/-conversion), or as irrelevant (equal to any other object at the same type), depending on where it occurs. Modal restrictions developed by R. Harper et al. (2000) for single types are generalized and employed to Guarantee Consistency between these views of objects. Potential applications are in logical frameworks, functional programming and the foundations of first-order modal logics. Our type theory contrasts with previous approaches that, a priori, distinguished propositions (whose proofs are all identified - only their existence is important) from specifications (whose implementations are subject to some definitional equalities).