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RISS 인기검색어
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- 저자 : Yunfeng Jiang (검색결과 4 건)
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A Double-channel Four-band True Color Night Vision System
Yunfeng Jiang,Dongsheng Wu,Jie Liu,Kuo Tian,Dan Wang 한국광학회 2022 Current Optics and Photonics Vol.6 No.6
By analyzing the signal-to-noise ratio (SNR) theory of the conventional true color night vision system, we found that the output image SNR is limited by the wavelength range of the system response λ 1and λ 2 . Therefore, we built a double-channel four-band true color night vision system to expand the system response to improve the output image SNR. In the meantime, we proposed an image fusion method based on principal component analysis (PCA) and nonsubsampled shearlet transform (NSST) to obtain the true color night vision images. Through experiments, a method based on edge extraction of the targets and spatial dimension decorrelation was proposed to calculate the SNR of the obtained images and we calculated the correlation coefficient (CC) between the edge graphs of obtained and reference images. The results showed that the SNR of the images of four scenes obtained by our system were 125.0%, 145.8%, 86.0% and 51.8% higher, respectively, than that of the conventional tri-band system and CC was also higher, which demonstrated that our system can get true color images with better quality.
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LI HOU,Yunfeng Niu,Yan Wang,YANG JIANG,Rongna Chen,Tianran Ma,FAMING GAO 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2016 NANO Vol.11 No.6
Here, we present a simple and novel method based on using insulin amyloid fibrils (INSAFs) as sacrificial templates for the construction of Pt–Pd nanoparticle chains (Pt–PdNPCs) under mild conditions. By incubating INSAFs in metal salt solution and then reduction, Pt–Pd nanoparticles with an uniform particle size of around 2.5 nm nucleated and grew along the axial direction of INSAFs, and thus formed a long chain structure with length up to several micrometers. The as-prepared Pt–PdNPCs exhibit an improved catalytic activity for lowtemperature CO and methanol oxidation and possess greater CO tolerance compared with the commercial Pt/C catalyst, which makes them promising for a variety of possible catalytic applications.
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Jing Ji,Zhichao Xu,Liangqin Jiang,Chaoqing Yuan,Yunfeng Zhang,Lijian Zhou,Shilong Zhang 한국강구조학회 2018 International Journal of Steel Structures Vol.18 No.4
This paper was concerned with the nonlinear analysis on the overall stability of H-type honeycombed composite column with rectangular concrete-fi lled steel tube fl anges (STHCC). The nonlinear analysis was performed using ABAQUS, a commercially available fi nite element (FE) program. Nonlinear buckling analysis was carried out by inducing the fi rst buckling mode shape of the hinged column to the model as the initial imperfection with imperfection amplitude value of L /1000 and importing the simplifi ed constitutive model of steel and nonlinear constitutive model of concrete considering hoop eff ect. Close agreement was shown between the experimental results of 17 concrete-fi lled steel tube (CFST) specimens and 4 I-beams with top fl anges of rectangular concrete-fi lled steel tube (CFSFB) specimens conducted by former researchers and the predicted results, verifying the correctness of the method of FE analysis. Then, the FE models of 30 STHCC columns were established to investigate the infl uences of the concrete strength grade, the nominal slenderness ratio, the hoop coeffi cient and the fl ange width on the nonlinear stability capacity of SHTCC column. It was found that the hoop coeffi cient and the nominal slenderness ratio aff ected the nonlinear stability capacity more signifi cantly. Based on the results of parameter analysis, a formula was proposed to predict the nonlinear stability capacity of STHCC column which laid the foundation of the application of STHCC column in practical engineering.
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Wang Yu,Yao Qingxu,Zhang Quanhu,Zhang He,Lu Yunfeng,Fan Qimeng,Jiang Nan,Yu Wangtao 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.12
Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gammaray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers’ confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.
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