19,957 research outputs found

    Material modelling and springback analysis for multi-stage rotary draw bending of thin-walled tube using homogeneous anisotropic hardening model

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    The aim of this paper is to compare several hardening models and to show their relevance for the prediction of springback and deformation of an asymmetric aluminium alloy tube in multi-stage rotary draw bending process. A three-dimensional finite-element model of the process is developed using the ABAQUS code. For material modelling, the newly developed homogeneous anisotropic hardening model is adopted to capture the Bauschinger effect and transient hardening behaviour of the aluminium alloy tube subjected to non-proportional loading. The material parameters of the hardening model are obtained from uniaxial tension and forward-reverse shear test results of tube specimens. This work shows that this approach reproduces the transient Bauschinger behaviour of the material reasonably well. However, a curve-crossing phenomenon observed for this material cannot be captured by the homogeneous anisotropic hardening model. For comparison purpose, the isotropic and combined isotropic-kinematic hardening models are also adopted for the analysis of the same problem. The predictions of springback and cross-section deformation based on these models are discussed. (C) 2014 The Authors. Published by Elsevier Ltd.open1134Nsciescopu

    Deep Learning for Single Image Super-Resolution: A Brief Review

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    Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

    Boosting the precision of virtual call integrity protection with partial pointer analysis for C++

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    © 2017 Association for Computing Machinery. We present, Vip, an approach to boosting the precision of Virtual call Integrity Protection for large-scale real-world C++ programs (e.g., Chrome) by using pointer analysis for the first time. Vip introduces two new techniques: (1) a sound and scalable partial pointer analysis for discovering statically the sets of legitimate targets at virtual callsites from separately compiled C++ modules and (2) a lightweight instrumentation technique for performing (virtual call) integrity checks at runtime. Vip raises the bar against vtable hijacking attacks by providing stronger security guarantees than the CHA-based approach with comparable performance overhead. Vip is implemented in LLVM-3.8.0 and evaluated using SPEC programs and Chrome. Statically, Vip protects virtual calls more effectively than CHA by significantly reducing the sets of legitimate targets permitted at 20.3% of the virtual callsites per program, on average. Dynamically, Vip incurs an average (maximum) instrumentation overhead of 0.7% (3.3%), making it practically deployable as part of a compiler tool chain

    Robust Preview Control for a Class of Uncertain Discrete-Time Lipschitz Nonlinear Systems

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    © 2018 Xiao Yu et al. This paper considers the design of the robust preview controller for a class of uncertain discrete-time Lipschitz nonlinear systems. According to the preview control theory, an augmented error system including the tracking error and the known future information on the reference signal is constructed. To avoid static error, a discrete integrator is introduced. Using the linear matrix inequality (LMI) approach, a state feedback controller is developed to guarantee that the closed-loop system of the augmented error system is asymptotically stable with H∞ performance. Based on this, the robust preview tracking controller of the original system is obtained. Finally, two numerical examples are included to show the effectiveness of the proposed controller

    Finite-Time Bounded Tracking Control for Linear Discrete-Time Systems

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    © 2018 Fucheng Liao et al. A finite-time bounded tracking control problem for a class of linear discrete-time systems subject to disturbances is investigated. Firstly, by applying a difference method to constructing the error system, the problem is transformed into a finite-time boundedness problem of the output vector of the error system. In fact, this is a finite-time boundedness problem with respect to the partial variables. Secondly, based on the partial stability theory and the research methods of finite-time boundedness problem, a state feedback controller formulated in form of linear matrix inequality is proposed. Based on this, a finite-time bounded tracking controller of the original system is obtained. Finally, a numerical example is presented to illustrate the effectiveness of the controller
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