16,999 research outputs found
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 BiSe. Se vacancy in BiSe is a double
donor, and Bi vacancy is a triple acceptor. Se antisite (Se) is always
an active donor in the system because its donor level ((+1/0))
enters into the conduction band. Interestingly, Bi antisite(Bi) in
BiSe is an amphoteric dopant, acting as a donor when
0.119eV (the material is typical p-type) and as an acceptor when
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 is the most stable native defect independent
of electron chemical potential . Under Bi-rich condition, Se vacancy
is the most stable native defect except for under the growth window as
0.262eV (the material is typical n-type) and
-0.459eV(Bi-rich), under such growth windows one
negative charged Bi 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 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 transition strength in low-lying states of
hypernucleus Li. Many more data on low-lying states of
hypernuclei will be measured soon for -shell nuclei, providing good
opportunities to study the impurity effect on nuclear low-energy
excitations. We carry out a quantitative analysis of hyperon impurity
effect on the low-lying states of -shell nuclei at the beyond-mean-field
level based on a relativistic point-coupling energy density functional (EDF),
considering that the hyperon is injected into the lowest
positive-parity () and negative-parity () states. We
adopt a triaxially deformed relativistic mean-field (RMF) approach for
hypernuclei and calculate the binding energies of hypernuclei as well
as the potential energy surfaces (PESs) in deformation plane.
We also calculate the PESs for the 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 Mg and Si as examples, we analyse
the impurity effects of and 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
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