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Interface evolution mechanism and model of atomic diffusion during Al-Au ultrasonic bonding
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作者 ZHANG Wei-xi LUO Jiao +2 位作者 CHEN Xiao-hong WANG Bo-zhe YUAN Hai 《Journal of Central South University》 2025年第3期806-819,共14页
Effects of ultrasonic bonding parameters on atomic diffusion, microstructure at the Al-Au interface, and shear strength of Al-Au ultrasonic bonding were investigated by the combining experiments and finite element (FE... Effects of ultrasonic bonding parameters on atomic diffusion, microstructure at the Al-Au interface, and shear strength of Al-Au ultrasonic bonding were investigated by the combining experiments and finite element (FE) simulation. The quantitative model of atomic diffusion, which is related to the ultrasonic bonding parameters, time and distance, is established to calculate the atomic diffusion of the Al-Au interface. The maximum relative error between the calculated and experimental fraction of Al atom is 7.35%, indicating high prediction accuracy of this model. During the process of ultrasonic bonding, Au8Al3 is the main intermetallic compound (IMC) at the Al-Au interface. With larger bonding forces, higher ultrasonic powers and longer bonding time, it is more difficult to remove the oxide particles from the Al-Au interface, which hinders the atomic diffusion. Therefore, the complicated stress state and the existence of oxide particles both promotes the formation of holes. The shear strength of Al-Au ultrasonic bonding increases with increasing bonding force, ultrasonic power and bonding time. However, combined with the presence of holes at especial parameters, the optimal ultrasonic bonding parameter is confirmed to be a bonding force of 23 gf, ultrasonic power of 75 mW and bonding time of 21 ms. 展开更多
关键词 Al-Au ultrasonic bonding model of atomic diffusion Au_(8)Al_(3) shear strength ultrasonic power
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Dynamics of the HBV model with diffusion and time delay 被引量:2
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作者 QIAO mei-hong,QI huan(Department of Control Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074) 《医用生物力学》 EI CAS CSCD 2009年第S1期117-118,共2页
Chronic hepatitis B infection is a major health problem,with approximately 350 million virus carriers worldwide.In Africa,about 30%-60% of children and 60%-100% of adults have
关键词 HBV TIME Dynamics of the HBV model with diffusion and time delay
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基于扩散模型图像增强与多类特征融合的火焰燃烧状态智能识别
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作者 汤健 杨薇薇 +2 位作者 夏恒 崔璨麟 乔俊飞 《北京工业大学学报》 北大核心 2025年第12期1502-1514,共13页
针对领域专家依据经验判断城市固废焚烧(municipal solid waste incineration,MSWI)过程中的火焰燃烧状态具有随意性、主观性和差异性,以及高质量火焰图像稀少等问题,提出基于去噪扩散概率模型(denoising diffusion probabilistic model... 针对领域专家依据经验判断城市固废焚烧(municipal solid waste incineration,MSWI)过程中的火焰燃烧状态具有随意性、主观性和差异性,以及高质量火焰图像稀少等问题,提出基于去噪扩散概率模型(denoising diffusion probabilistic model,DDPM)的图像增强与多类特征融合的火焰燃烧状态识别方法。首先,利用DDPM生成虚拟火焰图像以弥补高质量建模图像稀缺问题;然后,对由真实和虚拟图像混`合得到的建模数据采用LeNet-5模型提取深度特征,同时提取火焰图像的亮度、范围和颜色等物理特征;最后,面向上述混合特征构建基于深度森林分类(deep forest classification,DFC)的火焰燃烧状态识别模型。基于实际MSWI过程火焰图像验证了该方法的有效性和优越性。 展开更多
关键词 城市固废焚烧(municipal solid waste incineration MSWI) 火焰燃烧状态识别 去噪扩散概率模型(denoising diffusion probabilistic model DDPM) 深度特征 物理特征 深度森林分类(deep forest classification DFC)
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Construction of Clonal Growth Models in Trifolium repens
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作者 HUBaozhong LI Fenglan HU Xiaomei HU Guofu YUAN Qiang 《Journal of Northeast Agricultural University(English Edition)》 CAS 2009年第4期14-18,共5页
The suitability of models for describing the clonal growth of Trifolium repens population was discussed. The results showed that deterministic models were inadequate for describing its clonal growth, but the diffusion... The suitability of models for describing the clonal growth of Trifolium repens population was discussed. The results showed that deterministic models were inadequate for describing its clonal growth, but the diffusion models and the randomwalk models suited for the clonal growth characteristics of the population. And it was found that random-walk models were better than diffusion models for describing a population in an environment with rich natural resources, and the latter was better in a poor environment. 展开更多
关键词 Trifolium repens clonal growth model diffusion model random-walk model
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