Time-domain dynamic constitutive model suitable for mucky soil site seismic response

Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modulus and combining the dynamic skeleton curve and the damping degradation coefficient,the constitutive equation of the logarithmic dynamic skeleton can be obtained,which considers the damping effect in a soil dynamics problem.Based on the finite difference method and the multi-transmitting boundary condition,a 1D site seismic response analysis program called Soilresp1D has been developed herein and used to analyze the time-domain seismic response in three types of sites.At the same time,this study also provides numerical simulation results based on the hyperbolic constitutive model and the equivalent linear method.The results verify the rationality of the new soil dynamic constitutive model.It can analyze the mucky soil site nonlinear seismic response,reflecting the deformation characteristics and damping effect of the silty soil.The hysteresis loop area is more extensive,and the residual strain is evident.

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.

Rock skeleton models and seismic porosity inversion

By substituting rock skeleton modulus expressions into Gassmann approximate fluid equation, we obtain a seismic porosity inversion equation. However, conventional rock skeleton models and their expressions are quite different from each other, resuling in different seismic porosity inversion equations, potentially leading to difficulties in correctly applying them and evaluating their results. In response to this, a uniform relation with two adjusting parameters suitable for all rock skeleton models is established from an analysis and comparison of various conventional rock skeleton models and their expressions including the Eshelby-Walsh, Pride, Geertsma, Nur, Keys-Xu, and Krief models. By giving the two adjusting parameters specific values, different rock skeleton models with specific physical characteristics can be generated. This allows us to select the most appropriate rock skeleton model based on geological and geophysical conditions, and to develop more wise seismic porosity inversion. As an example of using this method for hydrocarbon prediction and fluid identification, we apply this improved porosity inversion, associated with rock physical data and well log data, to the ZJ basin. Research shows that the existence of an abundant hydrocarbon reservoir is dependent on a moderate porosity range, which means we can use the results of seismic porosity inversion to identify oil reservoirs and dry or water-saturated reservoirs. The seismic inversion results are closely correspond to well log porosity curves in the ZJ area, indicating that the uniform relations and inversion methods proposed in this paper are reliable and effective.

A Parameterization of Cooling Rate Calculation under the Non-LTE Condition: Multi-Level Model

Calculations of cooling rate by CO2 15 μm band in the earth's upper mesosphere and lower thermosphere be-come very difficult because of the non-LTE. This is primarily due to the nonlinear vibration-vibrational (VV) transition processes between CO, molecules in different states. This paper suggests that the non-LTE source function be parameterized as a linear combination of two limiting source functions. One limiting source function neglects the VV transitions while the other limiting source function assumes VV transitions being dominant. These two limiting source functions can be derived by linear models. The parameterization schemes proposed here can be applied to the general circulation models including those non-LTE regions.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction.However,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized assessment.In this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton features.Furthermore,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in monkeys.Using the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual detection.Mon Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

Stance detection is the task of attitude identification toward a standpoint.Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting.Moreover,because the target is not always mentioned in the text,most methods have ignored target information.In order to solve these problems,we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory(LSTM)and the excellent extracting performance of convolutional neural networks(CNNs).The method can obtain multi-level features that consider both local and global features.We also introduce attention mechanisms to magnify target information-related features.Furthermore,we employ sparse coding to remove noise to obtain characteristic features.Performance was improved by using sparse coding on the basis of attention employment and feature extraction.We evaluate our approach on the SemEval-2016Task 6-A public dataset,achieving a performance that exceeds the benchmark and those of participating teams.

Multi-level biomechanical models for rehabilitation engineering

Rehabilitation engineering aims in the upmost degree to restore the lost functions for those persons with physical disability. Biomechanical modeling has been widely used for different purposes in rehabilitation engineering to understand the bio-

Effect of Aluminum Hydroxide Adjuvant on the Immunogenicity of the 2009 Pandemic Influenza A/H1N1 Vaccine:Multi-level Modeling of Data with Repeated Measures

Objective To evaluate the effect of the aluminum hydroxide （Al-OH） adjuvant on the 2009 pandemic influenza A/H1N1 （pH1N1） vaccine. Methods In a multicenter, double-blind, randomized, placebo-controlled trial, participants received two doses of split-virion formulation containing 15 ug hemagglutinin antigen, with or without aluminum hydroxide （N-OH）. We classified the participants into six age categories （〉61 years, 41-60 years, 19-40 years, 13-18 years, 8-12 years, and 3-7 years） and obtained four blood samples from each participant on days 0, 21, 35, and 42 following the first dose of immunization. We assessed vaccine immunogenicity by measuring the geometric mean titer （GMT） of hemagglutination inhibiting antibody. We used a two-level model to evaluate the fixed effect of aluminum Al-OH and other factors, accounting for repeated measures. Results The predictions of repeated measurement on GMTs of formulations with or without Al-OH, were 80.35 and 112.72, respectively. Al-OH significantly reduced immunogenicity after controlling for time post immunization, age-group and gender. Conclusion The Al-OH adjuvant does not increase but actually reduces the immunogenicity of the split-virion pH1N1 vaccine.

Aircraft wing box rapid modeling based on skeleton model

In the process of traditional aircraft structure design, repetitive manual modeling of aircraft wing box parts is time consuming. Meanwhile, it＇s difficult to update associated models when product design changes. Aiming at solving these problems, a method for rapid modeling and updating associated models was put forward. Based on skeleton model, combining with the technology of template re-use, it makes aircraft wing box rapid modeling and correct updating possible. The detailed implementation process of the method was given, and a certain aircraft wing box was taken as an instance to verify the feasibility and effectiveness of this method.

Skeleton-Based Volumetric Parameterizations for Lattice Structures

Lattice structures with excellent physical properties have attracted great research interest.In this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensional lattice structures.The skeleton model is divided into three types of nodes.And the corresponding algorithms are utilized to construct diverse types of volume parametric nodes.The unit-cell is assembled with distinct nodes according to the geometric features.The final lattice structure is created by the periodic arrangement of unit-cells.Several different types of volume parametric lattice structures are constructed to prove the stability and applicability of the proposed method.The quality is assessed in terms of the value of the Jacobian matrix.Moreover,the volume parametric lattice structures are tested with the isogeometric analysis to verify the feasibility of integration of modeling and simulation.

Model Predictive Control Strategy of Multi-Port Interline DC Power Flow Controller

There are issues with flexible DC transmission system such as a lack of control freedom over power flow.In order to tackle these issues,a DC power flow controller(DCPFC)is incorporated into a multi-terminal,flexible DC power grid.In recent years,a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability.This work proposes a model predictive control(MPC)strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance.Initially,the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed,and the relationship between each regulated variable and control variable is determined;The power flow controller is then discretized,and the cost function and weight factor are built with numerous control objectives.Sub module sorting method and nearest level approximation modulation regulate the power flow controller;Lastly,theMATLAB/Simulink simulation platformis used to verify the correctness of the establishedmathematicalmodel and the control performance of the suggestedMPC strategy.Finally,it is demonstrated that the control strategy possesses the benefits of robust dynamic performance,multiobjective control,and a simple structure.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

反复荷载下圆钢管型钢再生混凝土组合柱恢复力模型研究

为建立圆钢管型钢再生混凝土组合柱的恢复力模型,对11根圆钢管型钢再生混凝土组合柱试件进行了低周反复荷载试验研究,考虑了再生骨料取代率、配钢率及钢管径厚比等不同设计参数的影响,分析了组合柱的地震破坏形态及滞回性能。基于组合柱的力学特征及曲线形状,提出了圆钢管型钢再生混凝土组合柱骨架曲线的三折线参数模型,采用理论推导与数据拟合的方法确定了组合柱骨架曲线的模型参数。在此基础上,给出了组合柱的滞回规则和卸载规律,构建了组合柱的恢复力模型,计算滞回曲线与试验滞回曲线吻合良好,表明该恢复力模型较好地反映了反复荷载下组合柱的受力特征点及滞回性能,可为此类组合柱的推广提供技术参考。

基于V形记忆曲线的汽车轮毂造型衍生设计方法

为提高汽车轮毂造型设计的效率,增强品牌造型识别度,提出一种高效的轮毂造型衍生创新设计方法。通过分析轮毂V形记忆曲线的变化规律和特点,总结出两类V形记忆曲线,并根据控制点数量分别进行了定量化表达;以轮毂造型的5个设计要素为基础,提取轮毂骨骼的特征并建立运算法则,构建基于V形记忆曲线的轮毂造型衍生设计方法;通过参数化实现造型构建方案衍生,验证了该方法的可行性。研究结果表明:基于V形记忆曲线的轮毂造型衍生设计方法提高了轮毂设计效率,实现了轮毂造型创新及提升品牌特征延续的设计目标,为同类型产品的创新设计提供参考。

橡胶混合黏土小应变剪切模量特性试验研究

为了研究橡胶混合黏土(混合土)的动变形特性,对不同橡胶掺量、橡胶粒径和围压的混合土开展共振柱试验,分析动剪切模量G和阻尼比λ的发展规律,并基于二元介质模型,提出了表达混合土接触状态的骨架孔隙比e_(sk)计算方法,进一步依托骨架孔隙比e_(sk)对混合土的最大动剪切模量G_(max)进行评价。结果表明:掺入橡胶颗粒后,混合土的G减小,λ增大,且橡胶掺量增大时,混合土的G减小、λ增大;围压增大时,混合土的G增大、λ减小;橡胶粒径增大时,混合土的G增大、λ减小。随着橡胶掺量增大,骨架孔隙比e_(sk)增大,G_(max)降低;在相同橡胶掺量时,随着橡胶粒径的增大,e_(sk)增大,G_(max)升高。在Hardin公式的基础上,基于骨架孔隙比e_(sk)提出了考虑橡胶掺量和橡胶粒径的G_(max)表征模型,该模型具有较好的准确性,可为评价橡胶混合黏土G_(max)提供依据。

基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别方法

识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。

超大跨劲性骨架拱桥缩尺模型试验方案设计研究

为对超大跨劲性骨架拱桥缩尺模型试验方案进行合理设计,以计算跨径600 m的劲性骨架拱桥——天峨龙滩特大桥为背景,围绕模型缩尺比、结构设计、材料及加载系统等关键环节,开展主拱圈缩尺模型试验方案设计研究。最终确定模型拱缩尺比为1∶10,劲性钢骨架采用单肋形式,采用与实桥主拱圈性能相近、便于施工的材料,采用阵列式滑轮组加载系统实现模型拱和实桥主拱圈的应力等效,还原外包混凝土分环分段浇筑施工,并简化劲性钢骨架与管内混凝土施工。将模型拱外包混凝土应力实测值、ANSYS建模计算得到的模型拱理论值及实桥主拱圈理论值进行对比,结果表明:模型拱和实桥主拱圈外包混凝土在施工阶段的应力变化趋势基本相同,最大绝对误差在2 MPa以内,模型拱应力实测值和理论值也吻合较好,缩尺模型试验方案设计合理。

基于扭转剪切试验的沥青混合料抗剪强度分析

沥青路面的高温抗变形能力与沥青混合料的抗剪强度密切相关.根据路面荷载作用下沥青混合料中矿料颗粒的受力特点及转动状态,自主研发了扭转剪切试验装置.针对不同级配沥青混合料,在不同条件下进行扭转剪切试验;分析了骨架稳定性、沥青用量和温度对扭转剪切特性的影响,建立了考虑骨架稳定性指数、沥青用量和温度的抗剪强度预估模型.结果表明:最大扭转剪切力可以用于评价沥青混合料的抗剪强度;最大扭转剪切力随温度升高呈指数形式减小,随沥青用量增加呈二次函数关系变化,随骨架稳定性指数增大呈幂函数关系增大.构建的抗剪强度预估模型能够较为准确地表征各因素耦合作用下沥青混合料的抗剪强度,可以为沥青混合料优化设计及路面养护决策提供参考.

核心筒结构残余位移模型及恢复力模型研究

为了研究核心筒结构的残余位移及滞回特性,本文利用国内23个核心筒试件参数及拟静力试验结果,采用逐步回归分析建立了核心筒结构的残余位移预测模型,并据此给出了减小核心筒残余变形的措施与方法,基于三折线骨架模型,对屈服点、峰值点、极限点的荷载与位移进行计算分析,进而构建了核心筒结构的恢复力模型。研究结果表明:本文建立的核心筒结构残余位移预测模型与结构恢复力模型与试验结果吻合度较高,能够较为真实地反映核心筒结构的滞回特性,弥补了国内对于核心筒结构恢复力模型研究的不足,为核心筒在弹塑性分析领域以及残余位移控制方面提供一定理论依据。