Cross-scale Correlation of Macro-micro Evaluation Indexes for Asphalt Binder

In order to further study the reliability of macro evaluation indexes,molecular dynamics (MD) was applied to the evaluation of asphalt binder.Micro evaluation indexes (potential energy,surface free energy,solubility parameter and diffusion coefficient) of asphalt binder in different service phases (virgin,modified,aged and rejuvenated) were simulated.Combined with the variation characteristics of asphalt binder macro evaluation indexes (permeability,ductility,viscosity and softening point) in different service phases,the cross-scale correlation of macro-micro evaluation indexes was explored.The results show that the macro and micro evaluation indexes of asphalt binder have different characteristics in different service phases.The essence of the variation in the properties of asphalt binders is the difference in micro composition.In addition,there is a certain correlation between macro and micro evaluation indexes,which can be described by the gray relation theory.The cross-scale correlation of macro-micro evaluation indexes can provide a certain theoretical basis for the development of asphalt binder.

Ultra-high-temperature application of MXene: Stabilization of 2D Ti_(3)C_(2)T_(x) for cross-scale strengthening and toughening of 3D TiC

Transition metal carbide/nitride cores within MXenes make them considerably useful for ultra-high-temperature reinforcement.However,extensive research on Ti_(3)C_(2)T_(x) MXene has revealed its tendency to undergo a phase transition to TiCy at temperatures above 800℃due to high activity of a superficial Ti atomic layer.Herein,spark plasma sintering of Ti_(3)C_(2)T_(x) and TiC is performed to prevent the Ti_(3)C_(2)T_(x) phase transition at temperatures up to 1900℃through the fabrication of composites at a pressure of 50 MPa.Using a focused ion beam scanning electron microscope to separate layered substances in the composites and examining selected area diffraction spots in a transmission electron microscope enabled identification of non-phase-transitioned MXene.First-principles calculations based on density functional theory indicated the formation of strong chemical bonding interfaces between Ti_(3)C_(2)T_(x) and TiC,which imposed a stability constraint on the Ti atomic layer at the Ti_(3)C_(2)T_(x) surface.Mechanical performance tests,such as three-point bending and fracture toughness analysis,demonstrated that the addition of Ti_(3)C_(2)T_(x) can effectively improve the cross-scale strengthening and toughening of the TiC matrix,providing a new path for designing and developing two-dimensional(2D)carbides cross-scale-enhanced three-dimensional(3D)carbides with the same elements relying on a wide variety of MXenes.

A 3-DOF piezoelectric robot with continuous walking gait aiming at cross-scale smooth motion

Piezoelectric robots play important roles in the field of micromanipulation, but it is difficult for them to generate steady precision motion at any moment. In order to eliminate the changing inertial force and improve the motion smoothness, this work proposes a piezoelectric robot with continuous walking gait inspired by ants. The idea is verified with theoretical models and numerical simulation, and the performances are evaluated with experiments. The robot is proven to have the ability to generate 3-DOF(dgeree of freedom) continuous smooth motions with constant speeds. The maximum and minimum smooth velocities have a difference of six orders of magnitude, realizing cross-scale velocity control. Besides, the motion resolution reaches several nanometers with the unlimited workspace, so the cross-scale displacement control can be also obtained. Furthermore, with great robustness against varying loads, the stable actuation capability of the robot is more than 22 times of the self-weight. To sum up,the proposed robot generates cross-scale smooth motion in both aspects of displacement and velocity, so it has good prospects in the applications requiring steady precision motion. The design philosophy and research methods in this work can be valuable references for further advances of micromanipulation robots.

Texture evolution prediction of 2219 aluminum alloy sheet under hydro-bulging using cross-scale numerical modeling

A simultaneous prediction of macroscopic deformation and microstructure evolution is critical for un-derstanding the deformation mechanism of components.In this work,the hydro-bulging process of 2219 aluminum alloy sheet was investigated using cross-scale numerical modeling,in which the macroscopic finite element method(FEM)and crystal plasticity finite element method(CPFEM)were combined.The calculated texture evolution exhibits good agreement with the experimental results,and the stress er-ror between the two scales is generally small.The effects of different strain states on texture evolution and slip mode are further discussed.As the strain ratioηincreases,the volume fractions of the initial Rotated Copper texture component andγ-Fiber texture component decrease significantly,which tend to be stabilized at P texture component.The initial Rotated Cube texture component is inclined to rotate towards the Cube texture component,while the volume fraction of this orientation is relatively stable.The lower strain ratio can considerably enhance the activity of more equivalent slip systems,promoting a more uniform strain distribution over grains.The difficulty of grain deformation changes as the lat-tice rotates.The grain with easy-to-deform orientation can gradually rotate to a stable orientation during plastic deformation,which has a lower Schmid factor.

Multi-scale observation and cross-scale mechanistic modeling on terrestrial ecosystem carbon cycle

To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. in summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.

The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin[version 1;peer review:1 approved with reservations,1 not approved]

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently.As a key part of the shop-floor,humans'high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior.Therefore,the modeling system for cross-scale human behavior in digital twin shop-floors was developed,powered by the data fusion of macro-behavior and micro-behavior virtual models.Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position.Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation.In this study,we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors.Based on this theoretical system,we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling.Lastly,we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors.Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

固体发动机药柱低温点火开裂失效的跨尺度分析

为了准确预测推进剂装药在低温点火过程中是否发生开裂,提出了一种全局⁃局部单向收缩耦合的跨尺度分析方法。针对高能硝酸酯增塑聚醚(NEPE)推进剂,开展低温中应变率单轴拉伸试验,获取了推进剂的典型失效模式。结果表明,基于发展的推进剂非线性粘弹性本构模型,实现了固体发动机药柱低温点火的宏观结构分析,获取了推进剂药柱结构危险点的位置;同时建立了考虑颗粒与基体界面脱湿和颗粒断裂的细观颗粒填充模型,进一步将宏观结构分析结果作用于相应的细观代表性体积单元(RVE)上。最后,建立推进剂细观失效准则,表明在低温点火条件下药柱结构完整性满足要求。收缩跨尺度分析方法可作为预测药柱在低温点火过程中开裂行为的有效手段。

基于多尺度时空特征和篡改概率改善换脸检测的跨库性能

目前大多DeepFake换脸检测算法过于依赖局部特征,尽管库内检测性能尚佳,但容易出现过拟合,导致跨库检测性能不理想,即泛化性能不够好。有鉴于此,文中提出一种基于多尺度时空特征和篡改概率的换脸视频检测算法,目的是利用假脸视频中广泛存在的帧间时域不连续性缺陷来解决现有检测算法在跨库、跨伪造方式和视频压缩时性能明显下降的问题,改善泛化检测能力。该算法包括3个模块:为检测假脸视频在时域上留下的不连续痕迹,设计了一个多尺度时空特征提取模块;为自适应计算多尺度时空特征之间的时空域关联性,设计了一个三维双注意力机制模块;为预测随机选取的像素点的篡改概率和构造监督掩膜,设计了一个辅助监督模块。将所提出的算法在FF++、DFD、DFDC、CDF等公开大型标准数据库中进行实验,并与基线算法和近期发布的同类算法进行对比。结果显示:文中算法在保持库内平均检测性能优良的同时,跨库检测和抗视频压缩时的综合性能最好,跨伪造方法检测时的综合性能中等偏上。实验结果验证了文中算法的有效性。

基于多尺度对比度增强和跨维度交互注意力机制的红外与可见光图像融合

针对目前红外与可见光图像融合存在特征提取不足、融合图像目标区域不显著、细节信息缺失等问题,提出了一种多尺度对比度增强和跨维度交互注意力机制的红外与可见光图像融合方法。首先,设计了多尺度对比度增强模块,以增强目标区域强度信息利于互补信息的融合;其次,采用密集连接块进行特征提取,减少信息损失最大限度利用信息;接着,设计了一种跨维度交互注意力机制,有助于捕捉关键信息,从而提升网络性能;最后,设计了从融合图像到源图像的分解网络使融合图像包含更多的场景细节和更丰富的纹理细节。在TNO数据集上对提出的融合框架进行了评估实验,实验结果表明本文方法所得融合图像目标区域显著,细节纹理丰富,具有更优的融合性能和更强的泛化能力,主观性能和客观评价优于其他对比方法。

生物热-力-电生理耦合学

由于温度、机械力、生化等多种影响因子对机体的生物学行为产生重要影响,生物体在多物理场下保持动态平衡是其正常活动的关键条件.研究生物热-力-电生理耦合行为,旨在交叉融合多个学科,建立生物传热学、生物力学、神经生理学等数理模型,系统开展跨尺度热-力-电生理耦合行为研究,并将成果应用于生物医学领域,对理解生物体的正常功能以及重大疾病的病理机制和有效诊治具有重要意义.其核心思想在于发现关键科学问题、识别交叉学科,并集成学科优势,推动理论创新和技术创新,为生命医学领域带来新突破.该文以剑桥大学和DNA双螺旋结构的发现为背景,以大脑、皮肤、牙齿等为例,介绍生物热-力-电生理耦合学这一新兴交叉学科的起源和内涵.

融合全局多层次特征的跨尺度河流精准识别方法

高分辨率遥感影像中河流自动化精准识别,在河湖环境监测和流域变化研究等方面具有重要意义和研究价值。然而,因河流在影像中面积占比较小,易造成数据集正负样本不平衡。此外,河流具有形态多变和尺度变换复杂等特点,导致河流识别易出现边界不连续和格网效应等问题。基于此,提出一种融合全局多层次特征的跨尺度河流精准识别方法。首先,选取全球具有明显特征的曲流河和辫状河,创建多特征河流数据集,以此增加数据多样性。其次,以轻量级语义分割模型Segformer为主干网络搭建R-Seg模型,设计全局多层次特征提取GASPP模块,通过各阶段与Transformer级联提取多尺度特征,使得模型能更好捕捉河流影像上下文特征信息,减少信息损失并放大全局维度交互特征。最后,提出基于掩膜加权投票的跨尺度河流影像预测方法,通过对大场景河流影像进行滑窗裁剪,将各单元预测块与特定掩膜加权相乘得到子预测结果,并按照重叠投票方式依次拼接组成最终结果,实现不同尺度河流影像精准识别。实验证明,在所构建包含曲流河和辫状河的多特征数据集中,通过与其他方法对比可发现:在定性方面,R-Seg整体网络结构既能确保主干河流的识别精度,又能缓解细小河流断流现象,有效平滑河流边界,对500×500小尺度河流影像识别具有较好的鲁棒性;此外,采用掩膜加权投票方法,能有效减少格网效应造成的单元图块边缘缺失问题,充分利用单元图块预测结果,提升对更大场景遥感影像的适应能力和河流预测精度,实现不同尺度河流影像精准识别。从定量角度,方法各类精度评价指标相对最优,总体精度可达99.49%;其次,对单张影像识别时间不到1 s,效率可满足大多数实际要求。此外,相比于纯粹重叠预测策略,掩膜加权投票预测策略的河流识别总体精度高约0.28%~6.93%;通过调整重叠度参数可发现,重叠度与精度并非正相关,大约在12.5%精度能达到相对最优。方法通过设计R-Seg网络模型和提出掩膜加权投票预测方法,能一定程度上减少河流边界识别不连续和格网效应等问题,有效提升不同场景下遥感影像河流识别精度,具有较好的鲁棒性和目视效果,识别结果对河流地质勘探及流域变化等有重要应用价值。

跨尺度蒸馏特征感知的轻量化水下图像增强

水下图像增强技术能够提升水下图像的质量和可视性,在丰富数字媒体资源、水下探测、水下通信等领域具有重要应用价值。近年来,深度学习方法在水下图像增强方面取得了显著的效果。然而,现有的方法计算复杂度高,限制了它们在计算资源有限的场景中的使用。针对这一问题,提出了一种轻量化的水下图像增强方法,该方法基于跨尺度深度蒸馏特征感知,采用U型网络结构,在保证非线性抽象层级抽取的同时,大幅减少了模型参数量。实验结果表明,所提出方法在视觉效果和客观评价指标上均取得了具有竞争力的结果。

动力电池材料—结构—性能跨尺度关联效应研究

动力电池是新能源汽车的核心部件,其电化学性能和热性能是决定新能源汽车能否规模化推广应用的关键,然而宏观尺度下的电化学性能和热性能既受到微观尺度下的电极材料特性的影响,又受到介观尺度下的电池结构参数的影响。为了揭示电池材料参数、电极结构参数和电池工作参数对动力电池性能的影响机理,以18650型镍钴锰三元动力电池锂为研究对象,通过构建电池电化学-热耦合模型,分别探究了上述各参数对动力电池电化学性能和热性能的影响规律,综合分析了电池材料—结构—性能跨尺度关联效应。研究结果表明:电池材料参数、电极结构参数、电池工作参数对动力电池性能的影响具有跨尺度强相关的特征。单一尺度下的动力电池设计及性能优化并不能得到最优结果,开展材料—结构—性能跨尺度设计及优化是动力电池安全性能和动力性能提升的根本途径。

全局-局部注意力引导的红外图像恢复算法

针对真实世界的红外图像恢复算法中存在的图像模糊、纹理失真、参数过大等问题,提出了一种用于真实红外图像的全局-局部注意力引导的超分辨率重建算法。首先,设计了一种跨尺度的全局局部特征融合模块,利用多尺度卷积和Transformer并行融合不同尺度的信息,并通过可学习因子引导全局和局部信息的有效融合。其次,提出了一种新颖的退化算法,即域随机化退化算法,以适应真实红外场景图像的退化域。最后,设计了一种新的混合损失函数,利用权重学习和正则化惩罚来增强网络的恢复能力,同时加快收敛速度。在经典退化图像和真实场景红外图像上的测试结果表明,与现有方法相比,该算法恢复的图像纹理更逼真,边界伪影更少,同时参数总数最多可减少20%。

基于改进Deformable DETR的无人机视频流车辆目标检测算法

针对无人机视频流检测中小目标数量多、因图像传输质量较低而导致的上下文语义信息不充分、传统算法融合特征推理速度慢、数据集类别样本不均衡导致的训练效果差等问题,提出一种基于改进Deformable DETR的无人机视频流车辆目标检测算法。在模型结构方面,该算法设计了跨尺度特征融合模块以增大感受野,提升小目标检测能力,并采用针对object_query的挤压-激励模块提升关键目标的响应值,减少重要目标的漏检与错检率;在数据处理方面,使用了在线困难样本挖掘技术,改善数据集中类别样本分布不均的问题。在UAVDT数据集上进行了实验,实验结果表明,改进后的算法相较于基线算法在平均检测精度上提升了1.5%,在小目标检测精度上提升了0.8%,并在保持参数量较少增长的情况下,维持了原有的检测速度。

融合多尺度CNN和CRF的通用细粒度事件检测

事件检测是自然语言处理领域中事件抽取的主要任务之一,它旨在从众多非结构化信息中自动提取出结构化的关键信息.现有的方法存在特征提取不全面、特征分布不均等情况.为了提高事件检测的准确率,提出了一种融合BERT预训练模型与多尺度CNN的神经网络模型(BMCC,BERT+Multi-scale CNN+CRF).首先通过BERT(Bidirectional Encoder Representations from Transformers)预训练模型来进行词向量的嵌入,并利用其双向训练的Transformer机制来提取序列的状态特征;其次使用不同尺度的卷积核在多个卷积通道中进行卷积训练,以此来提取不同视野的语义信息,丰富其语义表征.最后将BIO机制融入到条件随机场(CRF)来对序列进行标注,实现事件的检测.实验结果表明,所提出的模型在MAVEN数据集上的F1值为65.17%,表现了该模型的良好性能.

考虑宏微观几何形貌的螺栓连接结合面接触性能模型

为准确预测螺栓连接结合面的真实接触状态,提出了考虑宏微观几何形貌的螺栓连接结合面接触性能模型。通过小波分解方法,解决了实测螺栓连接结合面粗糙表面提取与重构的问题。采用两粗糙表面接触等效、数据驱动建模、宏微观变网格划分等技术,构建了考虑微观形貌与宏观几何尺寸耦合的螺栓连接结合面跨尺度模型,实现了结合面接触性能的准确计算。通过压力薄膜传感器测量被连接件结合面的压力分布,验证了计算模型的正确性,并以此为基础系统分析了被连接件厚度、螺栓预紧力以及螺栓规格尺寸对结合面接触性能的影响。计算结果表明:螺栓连接结合面真实压力分布具有非连续性,结合面压力最大值并非在被连接件孔径边缘处,而是在孔径与螺栓头外径之间;结合面真实接触面积远小于名义接触面积;结合面真实压力分布范围随着被连接件厚度、螺栓预紧力、螺栓规格尺寸的增加而增加,厚度和预紧力的影响呈现明显的非线性,螺栓尺寸的影响呈现一定的线性规律,且斜率为0.37。研究结果可为刚度、阻尼、密封等机械装备装配性能的可靠评估提供支撑。

跨尺度移位的有向目标检测方法

目标类内尺度差异大以及类间相似程度高给传统遥感目标检测带来挑战,多尺度信息融合以及基于FasterR-CNN的有向检测方法是解决尺度差异性及类间相似性的有效手段。但多尺度权重融合策略忽视了跨尺度对图像语义特征的提取导致检测精度低,同时基于FasterR-CNN的有向检测方法精度低速度慢。针对上述问题,提出一种跨尺度移位的有向目标检测方法。首先通过改进特征金字塔网络(FPN)实现多层特征图高效融合;其次在网络中加入跨尺度移位模块(CSM)提升FPN中多尺度特征间的相关性;最后采用有向区域建议网络(ORPN)提升了有向候选框的转换效率。本文方法分别在DOTA-v1.5和HRSC2016遥感数据集上开展测试,相比对照组,均值准确率(mAP)分别提升了3.68%和1.32%;同时在单块2080ti上用1024×1024图像进行测试,检测速度提升5.9%。

跨尺度复合织构化摩擦副端面密封性能分析

在密封副的表面上引入大尺度的凹槽与小尺度微孔结合的跨尺度复合织构,提出椭圆形微孔与T形槽结合的复合织构化机械密封端面,并创建间隙液膜流体模型;根据质量守恒空化边界条件建立数学模型,采用有限单元法进行数值求解,研究不同长短轴比的椭圆微孔分别与T形槽复合对密封性能的影响,确定最佳复合构型;对比分析不同工况参数下,不同织构化端面密封的开启力、泄漏量等密封性能的变化规律。结果表明:最佳复合构型为长短轴比1.8的椭圆微孔与T形槽复合,在相同工况参数下,相较单一织构,跨尺度复合织构化端面密封具有更强的动压润滑效应、更低的泄漏量、更高的开启力,综合密封性能最好。研究结果可应用于机械密封织构化摩擦副端面的设计及优化。

改进视觉Transformer的视频插帧方法

针对现有的视频插帧方法无法有效处理大运动和复杂运动场景的问题,提出了一种改进视觉Transformer的视频插帧方法。该方法融合了基于跨尺度窗口的注意力和可分离的时空局部注意力,增大了注意力的感受野并聚合了多尺度信息;对时空依赖和远程像素依赖关系进行联合建模,进而增强了模型对大运动场景的处理能力。实验结果表明,该方法在Vimeo90K测试集和DAVIS数据集上的PSNR指标分别达到了37.13 dB和28.28 dB,SSIM指标分别达到了0.978和0.891。同时,可视化结果表明,该方法针对存在大运动、复杂运动和遮挡场景的视频能产生清晰合理的插帧结果。