An ELU Network with Total Variation for Image Denoising

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

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

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

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

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

The $Fermi$-LAT has revealed that rotation powered millisecond pulsars (MSPs) are a major contributor to the Galactic $\gamma$-ray source population. We discuss the $\gamma$-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 $\gamma$-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 $\gamma$-ray luminosity and the spin down power. Our Monte-Calro simulation implies that most of the $\gamma$-ray emitting MSPs are radio quiet in the present sensitivity of the radio survey, indicating that most of the $\gamma$-ray MSPs have been unidentified. We argue that the Galactic $Fermi$ 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

Multiwavelength properties of a new Geminga-like pulsar: PSR J2021+4026

In this paper, we report a detailed investigation of the multiwavelength properties of a newly detected gamma-ray pulsar, PSR J2021+4026, in both observational and theoretical aspects. We firstly identify an X-ray source in the XMM-Newton serendipitous source catalogue, 2XMM J202131.0+402645, located within the 95% confidence circle of PSR J2021+4026. With an archival Chandra observation, this identification provides an X-ray position with arcsecond accuracy which is helpful in facilitating further investigations. Searching for the pulsed radio emission at the position of 2XMM J202131.0+402645 with a 25-m telescope at Urumqi Astronomical Observatory resulted in null detection and places an upper-limit of 0.1~mJy for any pulsed signal at 18~cm. Together with the emission properties in X-ray and gamma-ray, the radio quietness suggests PSR J2021+4026 to be another member of Geminga-like pulsars. In the radio sky survey data, extended emission features have been identified in the gamma-ray error circle of PSR J2021+4026. We have also re-analyzed the gamma-ray data collected by FERMI's Large Area Telescope. We found that the X-ray position of 2XMM J202131.0+402645 is consistent with that of the optimal gamma-ray timing solution. We have further modeled the results in the context of outer gap model which provides us with constraints for the pulsar emission geometry such as magnetic inclination angle and the viewing angle. We have also discussed the possibility of whether PSR J2021+4026 has any physical association with the supernova remnant G78.2+2.1 (gamma-Cygni).Comment: 11 pages, 14 figure