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Cellular U-Shaped Steel Combination Column Seismic Performance Analysis
Yunfeng Li,Cheng Zhao,Qianqian Lu 한국강구조학회 2021 International Journal of Steel Structures Vol.21 No.5
In this paper, a new form of structural design of the combination column—Cellular U-shaped steel combination column, as vertical force components applied to steel structure buildings. By changing the diameter size of honeycomb holes, the thickness of U-shaped steel and embellished plates, and the steel strength, the seismic properties of this new combination steel column are studied by using ABAQUS fi nite element software. Results the setting of honeycomb hole is reduced by part, the delay of the test piece and its energy consumption capacity are improved to varying degrees, the stiff ness degradation rate is improved, and the setting of honeycomb steel plays a certain role in the seismic performance of the steel column. The experiment is used as a reference, and the destruction form and carrying capacity are compared, so that the numerical model has some credibility. Results the strength and stiff ness of the cell U-shaped steel grid composition column are analyzed to meet the standards, the seismic performance is good, and can be applied to engineering practice.
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Research on Pixel Unmixing based on Support Vector Machine
Yunfeng Liu,Laijun Lu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.7
Through the research of the application of support vector machine theory in the pixel unmixing, the advantages of the weighted posterior probability support vector machine theory in pixel unmixing is presented. Considering the difference of each support vector machine classifier, posterior probability pixel is used as weight coefficient of sub-pixel classification for pixels unmixing. This paper presents a weighted posterior probability support vector machine mixed pixel unmixing method. This method not only has the nonlinear model decomposition characteristics of high precision, but also reduces the standard support vector machine calculating the amount of multi classifier, and it has strong adaptability.
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Li, Wenting,Zhang, Mengmeng,Wang, Kejun,Lu, Yunfeng,Tang, Hui,Wu, Keliang Asian Australasian Association of Animal Productio 2020 Animal Bioscience Vol.33 No.1
Objective: The objective of a conservation program is to maintain maximum genetic diversity and preserve the viability of a breed. However, the efficiency of a program is influenced by the ability to accurately measure and predict genetic diversity. Methods: To examine this question, we conducted a simulation in which common measures (i.e. heterozygosity) and novel measures (identity-by-descent probabilities and parental genomic components) were used to estimate genetic diversity within a conserved population using double-labeled single nucleotide polymorphism markers. Results: The results showed that the accuracy and sensitivity of identity-by-state probabilities and heterozygosity were close to identity by descent (IBD) probabilities, which reflect the true genetic diversity. Expected heterozygosity most closely aligned with IBD. All common measures suggested that practices used in the current Chinese pig conservation program result in a ~5% loss in genetic diversity every 10 generations. Parental genomic components were also analyzed to monitor real-time changes in genomic components for each male and female ancestor. The analysis showed that ~7.5% of male families and ~30% of female families were lost every 5 generations. After 50 generations of simulated conservation, 4 male families lost ~50% of their initial genomic components, and the genomic components for 24.8% of the female families were lost entirely. Conclusion: In summary, compared with the true genetic diversity value obtained using double-labeled markers, expected heterozygosity appears to be the optimal indicator. Parental genomic components analysis provides a more detailed picture of genetic diversity and can be used to guide conservation management practices.
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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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Sohn, Hiesang,Xiao, Qiangfeng,Seubsai, Anusorn,Ye, Youngjin,Lee, Jinwoo,Han, Hyokyung,Park, Sehkyu,Chen, Gen,Lu, Yunfeng American Chemical Society 2019 ACS APPLIED MATERIALS & INTERFACES Vol.11 No.24
<P>Thermally stable porous bimetallic (Ni<SUB><I>x</I></SUB>Pt<SUB>1-<I>x</I></SUB>) alloy mesocrystals within a carbon framework are produced via an aerosol-assisted process for high-performance catalysts for the oxygen reduction reaction (ORR) and hydrogenation. The porous Ni<SUB><I>x</I></SUB>Pt<SUB>1-<I>x</I></SUB> alloy has a robust composite of alloy nanoparticles with an adjustable composition and a porous carbon skeleton. Porous Ni<SUB><I>x</I></SUB>Pt<SUB>1-<I>x</I></SUB> alloys exhibit high thermal stability, retaining their crystalline structure and morphology at 550 °C for 6 h, as observed in thermal treatment tests under various conditions (time, temperature, and atmosphere). The porous Ni<SUB><I>x</I></SUB>Pt<SUB>1-<I>x</I></SUB> alloy as a catalyst for the hydrogenation of propylene has high conversion efficiency (>80%) and low activation energy (<I>E</I><SUB>a</SUB> < 20 kJ/mol) at ≥80 °C through the suitable control of the element composition and a pore structure. As a catalyst for the ORR, the catalytic activity of the porous Ni<SUB><I>x</I></SUB>Pt<SUB>1-<I>x</I></SUB> alloy is superior to that of conventional Pt/C (0.115 mA) (0.853 mA/cm<SUB>Pt</SUB><SUP>2</SUP> at 0.9 V/cm<SUB>Pt</SUB><SUP>2</SUP>). This is attributed to the homogeneous alloying of the metal components (Ni and Pt) and the increased accessibility of the reactants to the catalyst, resulting from the unique morphology of the porous Ni<SUB><I>x</I></SUB>Pt<SUB>1-<I>x</I></SUB> alloy, i.e., hierarchical structure with high porosity.</P> [FIG OMISSION]</BR>
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