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

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

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++

© 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

First-principles study of native point defects in Bi2Se3

Using first-principles method within the framework of the density functional theory, we study the influence of native point defect on the structural and electronic properties of Bi$_2$Se$_3$. Se vacancy in Bi$_2$Se$_3$ is a double donor, and Bi vacancy is a triple acceptor. Se antisite (Se$_{Bi}$) is always an active donor in the system because its donor level ($\varepsilon$(+1/0)) enters into the conduction band. Interestingly, Bi antisite(Bi$_{Se1}$) in Bi$_2$Se$_3$ is an amphoteric dopant, acting as a donor when $\mu$$_e$$<$0.119eV (the material is typical p-type) and as an acceptor when $\mu$$_e$$>$0.251eV (the material is typical n-type). The formation energies under different growth environments (such as Bi-rich or Se-rich) indicate that under Se-rich condition, Se$_{Bi}$ is the most stable native defect independent of electron chemical potential $\mu$$_e$. Under Bi-rich condition, Se vacancy is the most stable native defect except for under the growth window as $\mu$$_e$$>$0.262eV (the material is typical n-type) and $\Delta$$\mu$$_{Se}$$<$-0.459eV(Bi-rich), under such growth windows one negative charged Bi$_{Se1}$ is the most stable one.Comment: 7 pages, 4 figure

Progress of research on active noise radiation control with reflecting surfaces

© INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control Engineering. All Rights Reserved. The effect of reflecting surfaces on the peformance of active noise radiation control attracted attentions more than 20 years ago and there has been a lot of research on the area since then; however, successful applications are rarely reported. This paper first reviews the history of the research on active noise radiation control with reflecting surfaces, and then introduces recent progresses on this area at Nanjing University. The first progress is that the mechanism of noise reduction enhancement by introducing a refecting surface against the primary source in a multi-channel active sound radiation control system is analyzed. The second progress is that the noise reduction improvment by introducing an extra vertical reflecting surface to an active noise radiation control system near one existing horizontal surface is studied and the effects of the system orientation and the primary source location are discussed. The last progess is on increaseing the noise reduction by employing a finite size reflecting surface for the primary source on ground

Photometric identification of blue horizontal branch stars

We investigate the performance of some common machine learning techniques in identifying BHB stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from SDSS data. We investigate the performance of three different techniques, namely k nearest neighbour classification, kernel density estimation and a support vector machine (SVM). We discuss the performance of the methods in terms of both completeness and contamination. We discuss the prospect of trading off these values, achieving lower contamination at the expense of lower completeness, by adjusting probability thresholds for the classification. We also discuss the role of prior probabilities in the classification performance, and we assess via simulations the reliability of the dataset used for training. Overall it seems that no-prior gives the best completeness, but adopting a prior lowers the contamination. We find that the SVM generally delivers the lowest contamination for a given level of completeness, and so is our method of choice. Finally, we classify a large sample of SDSS DR7 photometry using the SVM trained on the spectroscopic sample. We identify 27,074 probable BHB stars out of a sample of 294,652 stars. We derive photometric parallaxes and demonstrate that our results are reasonable by comparing to known distances for a selection of globular clusters. We attach our classifications, including probabilities, as an electronic table, so that they can be used either directly as a BHB star catalogue, or as priors to a spectroscopic or other classification method. We also provide our final models so that they can be directly applied to new data.Comment: To appear in A&A. 19 pages, 22 figures. Tables 7, A3 and A4 available electronically onlin

Triaxially deformed relativistic point-coupling model for Λ\Lambda hypernuclei: a quantitative analysis of hyperon impurity effect on nuclear collective properties

The impurity effect of hyperon on atomic nuclei has received a renewed interest in nuclear physics since the first experimental observation of appreciable reduction of $E2$ transition strength in low-lying states of hypernucleus $^{7}_\Lambda$Li. Many more data on low-lying states of $\Lambda$ hypernuclei will be measured soon for $sd$-shell nuclei, providing good opportunities to study the $\Lambda$ impurity effect on nuclear low-energy excitations. We carry out a quantitative analysis of $\Lambda$ hyperon impurity effect on the low-lying states of $sd$-shell nuclei at the beyond-mean-field level based on a relativistic point-coupling energy density functional (EDF), considering that the $\Lambda$ hyperon is injected into the lowest positive-parity ($\Lambda_s$) and negative-parity ($\Lambda_p$) states. We adopt a triaxially deformed relativistic mean-field (RMF) approach for hypernuclei and calculate the $\Lambda$ binding energies of hypernuclei as well as the potential energy surfaces (PESs) in $(\beta, \gamma)$ deformation plane. We also calculate the PESs for the $\Lambda$ hypernuclei with good quantum numbers using a microscopic particle rotor model (PRM) with the same relativistic EDF. The triaxially deformed RMF approach is further applied in order to determine the parameters of a five-dimensional collective Hamiltonian (5DCH) for the collective excitations of triaxially deformed core nuclei. Taking $^{25,27}_{\Lambda}$Mg and $^{31}_{\Lambda}$Si as examples, we analyse the impurity effects of $\Lambda_s$ and $\Lambda_p$ on the low-lying states of the core nuclei...Comment: 15 pages with 18 figures and 1 table (version to be published in Physical Review C