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Defence Technology（防务技术） 2024年第3期33卷 55-65页

作者： Zhiyuan Tang yu Wang Khalil I.Elkhodary zefeng yu Shan Tang Dan Peng State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of Technology116023DalianPR China The Department of Mechanical Engineering The American University in CairoNew Cairo11835Egypt Department of Neurology The Second Hospital of Dalian Medical UniversityDalian116023PR China

Brain tissue is one of the softest parts of the human body,composed of white matter and grey *** mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain ***,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical *** white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical *** such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional *** a constitutive law for multiscale brain tissue using traditional function-based approaches can be very ***,this paper proposes a data-driven approach to establish the desired mechanical model of brain *** focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our *** matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading *** generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal ***,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body *** with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling *** predictions made by our trained model demonstrate sufficient ***,we find that the level of internal blood pressur

关键词： Data driven Constitutive law Anisotropy Brain tissue Internal pressure

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极端热环境下驻人月球科研站形态选型研究

华中科技大学学报（自然科学版） 2024年第8期52卷 146-152页

作者：潘文特于泽峰王继先哈尔滨工业大学建筑与设计学院哈尔滨150001 哈尔滨工业大学寒地城乡人居环境科学与技术工业和信息化部重点实验室哈尔滨150001

为了探索由月球表面极端热环境导致的驻人月球科研站不同形态选型之间防隔热效果的差异性,基于月球表面太阳辐射模型,利用ANSYS WORKBENCH有限元分析平台,建立驻人月球科研站多形态的仿真模型,对月昼和月夜典型时刻的内外表面温度和热... 详细信息

为了探索由月球表面极端热环境导致的驻人月球科研站不同形态选型之间防隔热效果的差异性,基于月球表面太阳辐射模型,利用ANSYS WORKBENCH有限元分析平台,建立驻人月球科研站多形态的仿真模型,对月昼和月夜典型时刻的内外表面温度和热通量等数据进行模拟分析.结果表明:当所有形态的驻人月球科研站均选择10mm HAYNES214金属蜂窝外板、30mm LI900纤维隔热毡层、30mm SY1000气凝胶、10mm HAYNES214金属蜂窝内板和10 mm钛合金内支撑板作为外围护结构时,月昼正午时刻,立式圆柱体形态的驻人月球科研站内表面平均温度和单位时间热获取更小,是面对此热环境时更为适合的防隔热形态选型;月夜午夜时刻,六棱柱体形态的驻人月球科研站内表面平均温度更大,单位时间热损失更小,是面对此热环境时更为适合的防隔热形态选型.

关键词：月球热环境驻人月球科研站建筑形态选型有限元仿真稳态热分析

Elastoplastic constitutive modeling under the complex loading driven by GRU and small-amount data

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Theoretical & Applied Mechanics Letters 2022年第6期12卷 389-394页

作者： zefeng yu Chenghang Han Hang Yang yu Wang Shan Tang Xu Guo The State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian116023China International Research Center for Computational Mechanics Dalian University of TechnologyDalian116023China Ningbo Institute of Dalian University of Technology Ningbo315016China

In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is *** this method,one-dimensional stress-strain data obtained under uniaxial load and different loading history is learned offline by gate recurrent unit(GRU)*** learned constitutive model is embedded into the general finite element framework through data expansion from one dimension to three dimensions,which can perform stress updates under the three-dimensional *** proposed method is then adopted to drive numerical solutions of boundary value problems for engineering *** with direct numerical simulations using the J2 plasticity model,the stress-strain response of beam structure with elastoplastic materials under forward loading,reverse loading and cyclic loading were predicted *** path dependent response of structure was captured and the effectiveness of the proposed method is *** shortcomings of the proposed method are also discussed.

关键词： Data driven Recurrent neural network Path dependence Small-amount data

Research on the training mode of innovative and entrepreneurial talents of higher education based on ＂Internet ＋＂

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International English Education Research 2018年第4期 76-77页

作者： yu zefeng DU Qian DAI Lilan School of Management Shanghai University of Engineering Science shanghai 201620

The talent training mode under the traditional educational form has hardly met the needs of modem enterprises for the innovation and Entrepreneurship of college graduates. Using the modem information technology and relying on the ＂Interact ＋＂ platform to study the training mode of innovative and entrepreneurial talents of higher education, in order to adapt to the present situation of the demand of modem enterprises for college graduates. This paper makes a survey and Analysis on the present situation of the cultivation of innovative and entrepreneurial talents in higher education, and deeply grasps the prospect of the human resource demand of the enterprise, in order to improve the students＇ innovative and entrepreneurial ability as the core, to serve the enterprise as the purpose, to make use of the information and communication technology and the Interact platform to innovate the educational model, so as to improve the training of higher education. The theoretical basis is provided for innovative and entrepreneurial talents.

关键词： Training mode "Intemet +" Innovative and entrepreneurial talents

Research on Talent Cultivation Model and Application Based on Mobile Cloud Learning Technology-Taking Innovative Talent Cultivation as an Example

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International English Education Research 2018年第2期 17-19页

作者： DAI Lilan DU Qian yu zefeng School of Management Shanghai University of Engineering Science Shanghai 201620

Mobile cloud learning and innovation and entrepreneurship education are undoubtedly one of the hot issues in the current development of human resources and education. At present, domestic colleges and universities still lack the industrialization model and platform that apply theory to practice areas, and apply the new mobile cloud learning technology to the research and practice of innovation and entrepreneurship training.

关键词： Mobile cloud learning Innovation and entrepreneurship education Blended learning Talents training mode

基于投影数据全广义变分最小化的低剂量CT重建

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南方医科大学学报 2017年第12期37卷 1585-1591页

作者：牛善洲吴恒喻泽峰郑子君喻高航赣南师范大学数学与计算机科学学院江西赣州341000

目的提出基于投影数据全广义变分最小化的低剂量CT重建方法。方法首先,通过非线性Anscombe变换将满足Poisson分布的投影数据转化为近似Gaussian分布,然后基于全广义变分正则化模型对变换后的Gaussian型数据进行噪声去除。最后,对去噪的... 详细信息

目的提出基于投影数据全广义变分最小化的低剂量CT重建方法。方法首先,通过非线性Anscombe变换将满足Poisson分布的投影数据转化为近似Gaussian分布,然后基于全广义变分正则化模型对变换后的Gaussian型数据进行噪声去除。最后,对去噪的结果进行Anscombe逆变换后实现传统的滤波反投影(FBP)CT重建。结果数值体膜实验结果表明本文提出的方法可以大大地改进重建图像的质量。FBP方法重建的Clock和Shepp-Logan体膜图像的信噪比分别为17.752 dB和19.379 dB,本文方法重建的图像的信噪比提高到24.0352 dB和23.4181 dB。FBP方法重建方法重建的Clock和Shepp-Logan体膜图像的均方误差分别为0.86%和0.58%,本文方法重建的图像的均方误差降低到到0.2%和0.23%。结论本文方法可以在投影数据不满足分段常数假设的前提下去除噪声和条形伪影,从而提高低剂量CT图像重建质量。

关键词：低剂量CT重建全变分全广义变分正则化 Gaussian分布滤波反投影算法

Improved hydrogen storage kinetics of MgH_(2) using TiFe_(0.92)Mn_(0.04)Co_(0.04) with in-situ generated α-Fe as catalyst

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Materials Reports(Energy) 2024年第1期4卷 95-103页

作者： zefeng Li Yangfan Lu Jingfeng Wang yu'an Chen Qian Li Fushen Pan College of Materials Science and Engineering National Engineering Research Center for Magnesium AlloysChongqing UniversityChongqing400044PR China National Key Laboratory of Advanced Casting Technologies Chongqing UniversityChongqing400044PR China Chongqing Institute of New Energy Storage Materials and Equipment Chongqing401135PR China

While TiFe alloy has recently attracted attention as the efficient catalyst to enhance de/hydrogenation rates of Mg/MgH_(2),the difficulty of its activation characteristics has hindered further improvement of reaction ***,we report that the TiFe_(0.92)Mn_(0.04)Co_(0.04) catalyst can overcome the abovementioned *** synthesized MgH_(2)-30 wt% TiFe_(0.92)Mn_(0.04)Co_(0.04) can release 4.5 wt%of hydrogen in 16 min at 250℃,three times as fast as MgH_(2).The activation energy of dehydrogenation was as low as 84.6 kJ mol^(-1),which is 46.8%reduced from pure MgH_(2).No clear degradation of reaction rates and hydrogen storage capacity was observed for at least 30 *** studies reveal that TiFe_(0.92)Mn_(0.04)Co_(0.04) partially decomposes to in-situ generatedα-Fe particles dispersed on TiFe_(0.92)Mn_(0.04)Co_(0.04).The presence ofα-Fe reduces the formation of an oxide layer on TiFe_(0.92)Mn_(0.04)Co_(0.04),enabling the activation *** the same time,the hydrogen incorporation capabilities of TiFe_(0.92)Mn_(0.04)Co_(0.04) can provide more hydrogen diffusion paths,which promote hydrogen dissociation and *** discoveries demonstrate the advanced nature and importance of combining the in-situ generatedα-Fe with TiFe_(0.92)Mn_(0.04)Co_(0.04).It provides a new strategy for designing highly efficient and stable catalysts for Mg-based hydrogen storage materials.

关键词： Hydrogen storage materials Magnesium De/hydrogenation kinetics Catalyst

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改进YOLOv5的遥感图像目标检测算法

计算机工程与应用 2023年第9期59卷 207-214页

作者：李坤亚欧鸥刘广滨于泽峰李林成都理工大学计算机与网络安全学院(牛津布鲁克斯学院) 成都610051

针对遥感图像中背景复杂度高、目标尺寸多样和小目标存在过多所导致的目标检测精度较低的问题,提出一种改进YOLOv5的遥感图像目标检测算法。该算法在主干网络引入通道-全局注意力机制(CGAM)以增强对不同尺度目标的特征提取能力和抑制冗... 详细信息

针对遥感图像中背景复杂度高、目标尺寸多样和小目标存在过多所导致的目标检测精度较低的问题,提出一种改进YOLOv5的遥感图像目标检测算法。该算法在主干网络引入通道-全局注意力机制(CGAM)以增强对不同尺度目标的特征提取能力和抑制冗余信息的干扰。引入密集上采样卷积(DUC)模块扩张低分辨率卷积特征图,有效增强不同卷积特征图的融合效果。将改进算法应用于公开遥感数据集RSOD中,改进YOLOv5算法平均精度AP值达到78.5%,较原算法提升了3.1个百分点。实验结果证明,改进后的算法能有效提高遥感图像目标检测精度。

关键词：遥感图像目标检测 YOLOv5 注意力机制上采样

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基于复值损失函数的并行MRI的深度重建

南方医科大学学报 2022年第12期42卷 1755-1764页

作者：黄进红周根娇喻泽峰胡文玉赣南师范大学数学与计算机科学学院//江西省数值模拟与仿真技术重点实验室//赣州市计算成像重点实验室江西赣州341000

目的研究一种新的以幅值和相位均方误差和为损失函数的深度重建方法,用以加快磁共振图像重建速度及提高重建精度。方法考虑到磁共振不同线圈之间的数据噪声并不完全独立,本文首先将多线圈数据合并成单线圈幅值数据,以消除不同线圈噪声... 详细信息

目的研究一种新的以幅值和相位均方误差和为损失函数的深度重建方法,用以加快磁共振图像重建速度及提高重建精度。方法考虑到磁共振不同线圈之间的数据噪声并不完全独立,本文首先将多线圈数据合并成单线圈幅值数据,以消除不同线圈噪声间的相关性,并以此作为训练数据的样本标签;同时考虑到相位信息的重要性,我们将损失函数定义为幅值和相位均方误差的加权和,权重系数用来平衡两者在不同应用中的重要程度。为了验证方法的有效性,本文利用FastMRI数据集中真实的脑部和膝部K空间数据进行训练和测试,并与一些以均方误差或平均绝对误差作为损失函数的相关方法进行了比较。结果所提方法能够有效去除噪声及重叠伪影,同时较好地保留图像细节。定量结果显示,相比于已有的以均方误差或平均绝对误差作为损失函数的方法,本文提出的方法可以提高重建图像的峰值信噪比(PSNR)1dB左右。结论本文针对深度磁共振重建提出了一种新的损失函数,以适应并行磁共振数据中的噪声特点,利用该方法可以加快重建速度,同时可以有效地提高重建质量。

关键词：深度学习复值损失函数并行磁共振成像图像重建