arXiv subjects

Honggang Chen

Publications and source records attributed to Honggang Chen.

26 records · Page 2Linked to original sources

arXiv2021

Reinforcing increase of ΔTC in MgB2 smart meta-superconductors by adjusting the concentration of inhomogeneous phases

Incorporating with inhomogeneous phases with high electroluminescence (EL) intensity to prepare smart meta-superconductors (SMSCs) is an effective method of increasing the superconducting transition temperature (Tc) and has been confirmed in both MgB2 and Bi(Pb)SrCaCuO systems. However, the increase of ΔTc has been quite small because of the low optimal concentrations of inhomogeneous phases. In this work, three kinds of MgB2 raw materials, namely, aMgB2, bMgB2, and cMgB2, were prepared with particle sizes decreasing in order. Inhomogeneous phases, Y2O3:Eu3+ and Y2O3:Eu3+/Ag, were also prepared and doped into MgB2 to study the influence of doping concentration on the ΔTc of MgB2 with different particle sizes. Results show that reducing the MgB2 particle size increases the optimal doping concentration of inhomogeneous phases, thereby increasing ΔTc. The optimal doping concentrations for aMgB2, bMgB2, and cMgB2 are 0.5%, 0.8%, and 1.2%, respectively. The corresponding ΔTc values are 0.4, 0.9, and 1.2 K, respectively. This work open a new approach to reinforcing increase of ΔTc in MgB2 SMSCs.

cond-mat.supr-con↗

arXiv2021

Real-World Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation, has been an active research topic in the area of image processing in recent decades. Particularly, deep learning-based super-resolution (SR) approaches have drawn much attention and have greatly improved the reconstruction performance on synthetic data. Recent studies show that simulation results on synthetic data usually overestimate the capacity to super-resolve real-world images. In this context, more and more researchers devote themselves to develop SR approaches for realistic images. This article aims to make a comprehensive review on real-world single image super-resolution (RSISR). More specifically, this review covers the critical publically available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR. Comparisons are also made among representative RSISR methods on benchmark datasets, in terms of both reconstruction quality and computational efficiency. Besides, we discuss challenges and promising research topics on RSISR.

eess.IV↗

arXiv2020

Smart metastructure method for increasing TC of Bi(Pb)SrCaCuO high-temperature superconductors

Improving the critical transition temperature (TC) of Bi(Pb)SrCaCuO (B(P)SCCO) high-temperature superconductors is important, however, considerable challenges exist. In this study, on the basis of the metamaterial structure and the idea that the injecting energy will promote the formation of Cooper pairs, a smart meta-superconductor B(P)SCCO consisting of B(P)SCCO microparticles and Y2O3:Eu3++Ag or Y2O3:Eu3+ luminophor was designed. In the applied electric field, the Y2O3:Eu3++Ag or Y2O3:Eu3+ luminophor generates an electroluminescence (EL), thereby promoting the TC via EL energy injection. A series of Y2O3:Eu3++Ag topological luminophor-doped B(P)SCCO samples was prepared. Results showed that Y2O3:Eu3++Ag was dispersed around B(P)SCCO particles, forming a metastructure. Accordingly, the onset transition temperature (T_(C,on)) and zero resistance transition temperature (T_(C,0)) of B(P)SCCO increased. Meanwhile, the B(P)SCCO sample doped with 0.2 wt% Y2O3 or Y2O3:Sm3+ nonluminous inhomogeneous phase was also prepared to further prove the influence of EL on the T_C rather than the rare earth effect. Results indicated that the TC of the Y2O3 or Y2O3:Sm3+ doping sample decreased. However, the TC of the 0.2 wt% Y2O3:Eu3++Ag or Y2O3:Eu3+ luminophor-doped sample improved. This outcome further demonstrated that the smart metastructure method can improve the TC of B(P)SCCO.

cond-mat.supr-con↗

arXiv2019

Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network

Porous media are ubiquitous in both nature and engineering applications, thus their modelling and understanding is of vital importance. In contrast to direct acquisition of three-dimensional (3D) images of such medium, obtaining its sub-region (s) like two-dimensional (2D) images or several small areas could be much feasible. Therefore, reconstructing whole images from the limited information is a primary technique in such cases. Specially, in practice the given data cannot generally be determined by users and may be incomplete or partially informed, thus making existing reconstruction methods inaccurate or even ineffective. To overcome this shortcoming, in this study we proposed a deep learning-based framework for reconstructing full image from its much smaller sub-area(s). Particularly, conditional generative adversarial network (CGAN) is utilized to learn the mapping between input (partial image) and output (full image). To preserve the reconstruction accuracy, two simple but effective objective functions are proposed and then coupled with the other two functions to jointly constrain the training procedure. Due to the inherent essence of this ill-posed problem, a Gaussian noise is introduced for producing reconstruction diversity, thus allowing for providing multiple candidate outputs. Extensively tested on a variety of porous materials and demonstrated by both visual inspection and quantitative comparison, the method is shown to be accurate, stable yet fast ($\sim0.08s$ for a $128 \times 128$ image reconstruction). We highlight that the proposed approach can be readily extended, such as incorporating any user-define conditional data and an arbitrary number of object functions into reconstruction, and being coupled with other reconstruction methods.

eess.IV↗

arXiv2018

Smart meta-superconductor MgB2 constructed by inhomogeneous phase of luminescent nanocomposite

On the basis of the idea that the injecting energy will improve the conditions for the formation of Cooper pairs, a smart meta-superconductor (SMSC) was prepared by doping inhomogeneous phase of luminescent nanocomposite Y2O3:Eu3+/Ag, which has the strong luminescence characteristic, in MgB2 to improve the superconducting transition temperature (TC) of the MgB2-based superconductor. Two types of Y2O3:Eu3+/Ag with different sizes were prepared and marked as m-Y2O3:Eu3+/Ag and nY2O3:Eu3+/Ag. MgB2 SMSC was prepared through an ex situ process. Results show that when the inhomogeneous phase content was fixed at 2.0 wt.%, the TC of MgB2 SMSC increased initially then decreased with the increase in the Ag content in the dopant. When the Ag content accounted for 5 wt.% of the inhomogeneous phase weight, the TC of MgB2 SMSC was 37.2-38.0 K, which was similar to that of pure MgB2. Meanwhile, the TC of MgB2 SMSC doped with n-Y2O3:Eu3+/Ag increased initially then decreased basically with the increase in the content of n-Y2O3:Eu3+/Ag, in which Ag accounted for 5 wt.% of the inhomogeneous phase. The TC of MgB2 SMSC doped with 0.5 wt.% n-Y2O3:Eu3+/Ag was 37.6-38.4 K, which was 0.4 K higher than that of pure MgB2. It is thought that the doping inhomogeneous phase of luminescent nanocomposite into the superconductor is a new means to improve the TC of SMSC.

cond-mat.supr-con↗

arXiv2018

DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images

JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates. Soft decoding is an effective solution to improve the quality of compressed images without changing codec or introducing extra coding bits. Inspired by the excellent performance of the deep convolutional neural networks (CNNs) on both low-level and high-level computer vision problems, we develop a dual pixel-wavelet domain deep CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet. The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction. The pixel domain and wavelet domain estimates are combined to generate the final soft decoded result. Experimental results demonstrate the superiority of the proposed DPW-SDNet over several state-of-the-art compression artifacts reduction algorithms.

cs.CV↗

arXiv2017

The effect of inhomogeneous phase on the critical temperature of smart meta-superconductor MgB2

The critical temperature (TC) of MgB2, one of the key factors limiting its application, is highly desired to be improved. On the basis of the meta-material structure, we prepared a smart meta-superconductor structure consisting of MgB2 micro-particles and inhomogeneous phases by an ex situ process. The effect of inhomogeneous phase on the TC of smart meta-superconductor MgB2 was investigated. Results showed that the onset temperature (Ton C) of doping samples was lower than those of pure MgB2. However, the offset temperature (Toff C) of the sample doped with Y2O3:Eu3+ nanosheets with a thickness of 2~3 nm which is much less than the coherence length of MgB2 is 1.2 K higher than that of pure MgB2. The effect of the applied electric field on the TC of sample was also studied. Results indicated that with the increase of current, Ton C is slightly increased in the samples doping with different inhomogeneous phases. When increasing current, the Toff C of the samples doped with nonluminous inhomogeneous phases was decreased. However, the Toff C of the luminescent inhomogeneous phase doping samples increased and then decreased as increasing current.

cond-mat.supr-con↗

arXiv2017

CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks

In recent years, much research has been conducted on image super-resolution (SR). To the best of our knowledge, however, few SR methods were concerned with compressed images. The SR of compressed images is a challenging task due to the complicated compression artifacts, while many images suffer from them in practice. The intuitive solution for this difficult task is to decouple it into two sequential but independent subproblems, i.e., compression artifacts reduction (CAR) and SR. Nevertheless, some useful details may be removed in CAR stage, which is contrary to the goal of SR and makes the SR stage more challenging. In this paper, an end-to-end trainable deep convolutional neural network is designed to perform SR on compressed images (CISRDCNN), which reduces compression artifacts and improves image resolution jointly. Experiments on compressed images produced by JPEG (we take the JPEG as an example in this paper) demonstrate that the proposed CISRDCNN yields state-of-the-art SR performance on commonly used test images and imagesets. The results of CISRDCNN on real low quality web images are also very impressive, with obvious quality enhancement. Further, we explore the application of the proposed SR method in low bit-rate image coding, leading to better rate-distortion performance than JPEG.

cs.CV↗