307,018 research outputs found

    An ELU Network with Total Variation for Image Denoising

    Full text link
    In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function. We investigate the suitability by analyzing ELU's connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On the other hand, batch normalization (BN) is indispensable for residual denoising and convergence purpose. However, direct stacking of BN and ELU degrades the performance of CNN. To mitigate this issue, we design an innovative combination of activation layer and normalization layer to exploit and leverage the ELU network, and discuss the corresponding rationale. Moreover, inspired by the fact that minimizing total variation (TV) can be applied to image denoising, we propose a TV regularized L2 loss to evaluate the training effect during the iterations. Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images.Comment: 10 pages, Accepted by the 24th International Conference on Neural Information Processing (2017

    Improved thermal performance of a large laminated lithium-ion power battery by reciprocating air flow

    Get PDF
    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Thermal safety issues are increasingly critical for large-size laminated Lithium-Ion Batteries (LIBs). Despite a number of investigations conducted on the Battery Thermal Management System (BTMS) with reciprocating air-flow cooling, large laminated power LIBs are still not sufficiently investigated, particularly in the view of battery thermal characteristics. The present study investigates the thermal behaviors of an air-cooled NCM-type LIB (LiNi1−x−yCoxMnyO2 as cathode) from an experimental and systematic approach. The temperature distribution was acquired from different Depth of Discharge (DOD) by the infrared imaging (IR) technology. A reciprocating air-flow cooling method was proposed to restrict the temperature fluctuation and homogenize temperature distribution. Results showed that there was a remarkable temperature distribution phenomenon during the discharge process, the temperature distribution was affected by direction of air-flow. Forward air-flow (from current collector side to lower part of battery) was always recommended at the beginning of the discharge due to the thermal characteristics of the battery. After comprehensive consideration on battery temperature limit and cooling effect, the desired initial reversing timing was about 50% DOD at 3 C discharge rate. Different reversing strategies were investigated including isochronous cycles and aperiodic cycles. It was found that the temperature non-uniformity caused by heat accumulation and concentration was mitigated by reciprocating air-flow with optimized reversing strategy

    Bidirectional optimization of the melting spinning process

    Get PDF
    This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

    Long-term X-ray emission from Swift J1644+57

    Get PDF
    The X-ray emission from Swift J1644+57 is not steadily decreasing instead it shows multiple pulses with declining amplitudes. We model the pulses as reverse shocks from collisions between the late ejected shells and the externally shocked material, which is decelerated while sweeping the ambient medium. The peak of each pulse is taken as the maximum emission of each reverse shock. With a proper set of parameters, the envelope of peaks in the light curve as well as the spectrum can be modelled nicely.Comment: 6 pages, 2 figures, accepted for publication in MNRA

    Figure of Merit for Dark Energy Constraints from Current Observational Data

    Full text link
    Choosing the appropriate figure of merit (FoM) for dark energy (DE) constraints is key in comparing different DE experiments. Here we show that for a set of DE parameters {f_i}, it is most intuitive to define FoM = 1/\sqrt{Cov(f1,f2,f3,...)}, where Cov(f1,f2,f3,...) is the covariance matrix of {f_i}. The {f_i} should be minimally correlated. We demonstrate two useful choices of {f_i} using 182 SNe Ia (compiled by Riess et al. 2007), [R(z_*), l_a(z_*), \Omega_b h^2] from the five year Wilkinson Microwave Anisotropy Probe (WMAP) observations, and SDSS measurement of the baryon acoustic oscillation (BAO) scale, assuming the HST prior of H_0=72+/-8 km/s Mpc^{-1} and without assuming spatial flatness. We find that the correlation of (w_0,w_{0.5}) [w_0=w_X(z=0), w_{0.5}=w_X(z=0.5), w_X(a) = 3w_{0.5}-2w_0+3(w_0-w_{0.5})a] is significantly smaller than that of (w_0,w_a) [w_X(a)=w_0+(1-a)w_a]. In order to obtain model-independent constraints on DE, we parametrize the DE density function X(z)=\rho_X(z)/\rho_X(0) as a free function with X_{0.5}, X_{1.0}, and X_{1.5} [values of X(z) at z=0.5, 1.0, and 1.5] as free parameters estimated from data. If one assumes a linear DE equation of state, current data are consistent with a cosmological constant at 68% C.L. If one assumes X(z) to be a free function parametrized by (X_{0.5}, X_{1.0}, X_{1.5}), current data deviate from a cosmological constant at z=1 at 68% C.L., but are consistent with a cosmological constant at 95% C.L.. Future DE experiments will allow us to dramatically increase the FoM of constraints on (w_0,w_{0.5}) and of (X_{0.5}, X_{1.0}, X_{1.5}). This will significantly shrink the DE parameter space to enable the discovery of DE evolution, or the conclusive evidence for a cosmological constant.Comment: 7 pages, 3 color figures. Submitte

    Population study for γ\gamma-ray emitting Millisecond Pulsars and FermiFermi unidentified sources

    Full text link
    The FermiFermi-LAT has revealed that rotation powered millisecond pulsars (MSPs) are a major contributor to the Galactic γ\gamma-ray source population. We discuss the γ\gamma-ray emission process within the context of the outer gap accelerator model, and use a Monte-Calro method to simulate the Galactic population of the γ\gamma-ray emitting MSPs. We find that the outer gap accelerator controlled by the magnetic pair-creation process is preferable in explaining the possible correlation between the γ\gamma-ray luminosity and the spin down power. Our Monte-Calro simulation implies that most of the γ\gamma-ray emitting MSPs are radio quiet in the present sensitivity of the radio survey, indicating that most of the γ\gamma-ray MSPs have been unidentified. We argue that the Galactic FermiFermi unidentified sources located at high latitudes should be dominated by MSPs, whereas the sources in the galactic plane are dominated by radio-quiet canonical pulsars.Comment: 2011 Fermi Symposium proceedings - eConf C11050