Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Analysis of prompt fission neutron spectrum and multiplicity for ^(237)Np(n, f) in the frame of multi-modal Los Alamos model

The improved version of Los Alamos model with the multi-modal fission approach is used to analyse the prompt fission neutron spectrum and multiplicity for the neutron-induced fission of 237Np. The spectra of neutrons emitted from fragments for the three most dominant fission modes （standard Ⅰ, standard Ⅱ and superlong） are calculated separately and the total spectrum is synthesized. The multi-modal parameters contained in the spectrum model are determined on the basis of experimental data of fission fragment mass distributions. The calculated total prompt fission neutron spectrum and multiplicity are better agreement with the experimental data than those obtained from the conventional treatment of the Los Alamos model.

Calculation of Prompt Fission Neutron from ^(233)U(n, f) Reaction byMulti-Modal Los Alamos Model

An attempt is made to improve the evaluation of the prompt fission neutron emis- sion from 233U（n, f） reaction for incident neutron energies below 6 MeV. The multi-modal fission approach is applied to the improved version of Los Alamos model and the point by point model. The prompt fission neutron spectra and the prompt fission neutron as a function of fragment mass （usually named ＂sawtooth＂ data） v（A） are calculated independently for the three most dominant fission modes （standard I, standard II and superlong）, and the total spectra and v（A） are syn- thesized. The multi-modal parameters are determined on the basis of experimental data of fission fragment mass distributions. The present calculation results can describe the experimental data very well, and the proposed treatment is thus a useful tool for prompt fission neutron emission prediction.

Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

EXPERIMENTAL MODELING OF MIDDLE-RISE BUILDING VIA AMBIENT MODAL IDENTIFICATION

Experimental modeling of a middle-rise office building via ambient modal identification is presented. A 200-DOF (Dimension of freedom) test model is designed to correlate with finite element mode. A newly developed frequency-spatial domain decomposition ( FSDD ) technique is used to identify modal characteristics of the full-size building by using ambient response measurements. In the interested frequency ranges of 0～4.5 Hz and 0～ 6.5 Hz altogether 9 bending and torsion modes are identified. As one of the major focuses of the project, the accurate damping estimation is conducted based on FSDD. The identified modal frequencies and mode shapes are utilized for finite element model tuning. Excellent agreement has been achieved with respect to the final tuned finite element (FE) model up to 9 modes.

Empirical Likelihood Diagnosis of Modal Linear Regression Models

In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study.

A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION

The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.

Dynamical Model Updating Based on Modal Tests with Changed Structure

A new approach to modifying the stiffness and mass matrices of finite element models is presented to improve the calculation precision.By measuring the mode frequencies and shapes of both of the original and the new structures with changed stiffness and mass,the stiffness and mass matrices of the finite element model can be updated through matrices calculation and solving algebra equations.Taking a multi-freedom model as an example,the relation between the number of the modes and the correction precision of stiffness and mass matrix elements is researched.The facility and precision of the method are totally confirmed especially when the modeling error is known limited to a definite local range.The feasibility of the approach is proven by an effective engineering application to the model updating of a wing piece used in flutter test.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

一艘轮船在极其复杂的环境下面被操作，并且波浪和风被假定是随机的刺激。而且，推进器，主人和机械设备能也导致泛音回答。以便减少结构的颤动，获得一艘轮船的形式的参数信息是重要的。然而，自从刺激信息是困难的获得，传统的形式的参数鉴定方法不是合适的。自然刺激 technique-eigensystem 实现算法(下时代) 是仅仅从反应信号提炼形式的参数的一个运作的形式的鉴定方法，并且它基于到结构的输入是纯白噪音的假设。因此，当把下时代方法用于轮船结构时，学习泛音刺激的影响是必要的。这篇研究论文的结果在周围的刺激下面显示实际经验，仅仅当泛音频率不离形式的频率太靠近时，轮船模型实验成功地在形式的参数鉴定被做。

Effect of Tire Repeated Root Modal on Tire Modelling with Experimental Modal

The effect of tire repeated root modal（RRM）on tire modeling with an experimental modal is studied.Firstly,a radial tire with radial and tangential RRMs is tested and analyzed.By multi-point exciting of the radial tire,a multiple reference frequency domain method based on a least squares（LMS PolyMAX）algorithm is used to identify modal parameters.Then,modal stability diagram（MSD）,modal indication function（MIF）and modal assurance criteria（Auto-MAC）matrix are utilized to induce multiple inputs multiple outputs（MIMO）frequency response function（FRF）matrixes.The tests reveal that notable repeated roots exist in both radial and tangential response modes.Their modal frequencies and damping factors are approximately the same,the amplitudes of modal vectors are in the same order of magnitude,and the mode shapes are orthogonal.Based on the works mentioned,the method of trigonometric series modal shapes fitting is adopted,the effects of RRM model on tire modeling with a vertical experimental modal are discussed.The final results show that the effects of considering the RRM shapes are equivalent to the tire mode shapes depended on rotating the tire’s different exciting points during tire modeling,and since considering the RRM,the tire mode shapes can be unified and fixed during tire modeling.

A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION

The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously.

新型电力系统中的大模型驱动技术:现状、机遇与挑战

建设新型电力系统是实现社会、经济高质量发展的重要基石,以新一代通用人工智能为主导的信息化技术与能源电力科学深度耦合,为新型电力系统的数字化转型工作提供了重要保障。为了探究电力系统在大模型时代潮流下的发展方向,该文首先系统梳理了当前电力系统的数智化发展现状以及在新的场景需求下遇到的难点问题。然后重点探讨了以多模态大模型为代表的具备深度场景解析和语言描述能力的大模型技术在电力系统中的应用前景,并分析了其在几个典型场景下的相关应用成果,证明大模型技术可行性的同时,进一步总结了大模型技术在相关电力场景所面临的机遇与挑战性问题。最后对未来大模型技术如何与电力系统实现紧密融合做了展望与建议。该研究成果有望为新型电力系统数字化转型过程中的数智化发展提供参考,助力能源电力领域提质增效。

核电工程异形水箱流固耦合分布式参数模型与效用研究

在流固耦合动力分析领域,传统的Housner模型在工程设计中发挥重要作用。但其理论推导仅适用于规则水箱,采用集中质量总体模型描述液体晃动的整体效应,难以有效模拟异形水箱储液结构的局部精细响应,亟须建立适用于不规则水体晃荡的分布式附加质量模型。基于势流理论推导了一种模态综合法模型,实现了分布质量与储液器壁激振加速度的解耦。该模型能够有效剥离分布式的脉冲质量与对流质量;同时,实体网格的振型分析适用于任意形状水体的动力分析。通过算例的形式将该模型与精细声学-结构耦合(Acoustic-Structural coupling,CAS)模拟法进行了比较,验证了模型的精确性与可靠性。最后,基于本文的模型研究了核电异形的安全水箱在不同水位和减晃格栅分布下的动力响应规律,并检验了其在工程中的适用性。

基于全局与序列混合变分Transformer的多样化图像描述生成方法

多样化图像描述生成已成为图像描述领域研究热点.然而,现有方法忽视了全局和序列隐向量之间的依赖关系,严重限制了图像描述性能的提升.针对该问题,本文提出了基于混合变分Transformer的多样化图像描述生成框架.具体地,首先构建全局与序列混合条件变分自编码模型,解决全局与序列隐向量之间依赖关系表示的问题.其次,通过最大化条件似然推导混合模型的变分证据下界,解决多样化图像描述目标函数设计问题.最后,无缝融合Transformer和混合变分自编码模型,通过联合优化提升多样化图像描述的泛化性能.在MSCOCO数据集上实验结果表明,与当前最优基准方法相比,在随机生成20和100个描述语句时,多样性指标m-BLEU(mutual overlap-BiLingual Evaluation Understudy)分别提升了4.2%和4.7%,同时准确性指标CIDEr(Consensus-based Image Description Evaluation)分别提升了4.4%和15.2%.

安全协议形式化分析方法研究综述

介绍了安全协议的基本概念和分类,然后对安全协议形式化分析方法进行了详细介绍,包括基于模态逻辑的方法、基于模型检测的方法、基于定理证明的方法和基于可证明安全性理论的方法。其中,基于模型检测的方法是目前应用最广泛的一种方法,因此详细介绍了一些常用的基于模型检测方法的工具。最后,总结了当前安全协议形式化分析方法的研究热点和未来的发展方向。

基于环境激励的厦门财富中心动力参数实测分析

为揭示环境激励下不同测点反映的模态信息情况,在厦门财富中心布置结构台阵获取环境激励下的结构振动响应。采用Welch平均周期图法获得功率谱密度(power spectral density,PSD)曲线,并在此基础上识别结构模态参数。比较不同测点的PSD曲线和模态识别结果,并与有限元结果进行对比。结果表明:1)结构基底、自由场地和基岩场地三个测点PSD曲线在0.1~1.0 Hz频段高度相似,该频段是由场地次级微地震引起的;不同之处在于结构基底测点PSD曲线Z向峰值是结构竖向基频,而自由场地峰值是场地基本频率。2)结构低楼层测点主要反映其测试方向上的模态频率,而高楼层测点能够反映多方向的模态频率。高楼层测点反映的模态信息更丰富,但易受到密集模态的影响。3)有限元结果与实测结果接近,表明有限元模型可以较好地反映结构的真实动力特性。

基于PSO-BP神经网络的磨机传动系统模型修正

针对磨机传动系统结构的复杂性、部件间约束条件的不确定性以及非线性等因素,提出了一种基于PSO-BP神经网络的有限元模型修正方法。通过改进BP神经网络逼近设计参数和特征量间的非线性映射关系,结合实际结构响应,利用神经网络的泛化特性,得到了模型设计参数值。修正后频率误差从最高18%降到4%左右,修正系数误差范围均在0.5%以内,明显提高了有限元模型精度;同时,又不需要大量迭代求解步骤,避开了传统反问题模型修正法的复杂非线性优化过程,提升了效率,验证了PSO-BP神经网络法应用于大型磨机传动系统上的可行性,为后续传动系统整体分析奠定了基础。

中国省域交通碳排放空间关联格局的驱动特征

利用我国2003~2020年省级交通碳排放数据,基于模体结构分析和指数随机图模型对中国交通碳排放空间关联网络的宏观格局、微观连通和驱动特征进行研究.结果表明,中国交通碳排放空间关联网络呈现周期波动特征,等级均衡化发展和核心虹吸的空间分布,演化出少核心—多半核心—边缘新格局,碳转移路径表现出明显的空间惰性与时间惯性以及区域集中性;经纪属性在推动跨区域交通协同减排的经济活动中发挥重要作用;内生网络的互惠性、连通性、集聚性对交通碳排放网络形成具有重要驱动作用,行为者属性中发出、接收、抑制、互惠的驱动关系明显,外部网络的影响具有明显的地理邻近效应,呈现出地理距离衰减规律特征.由此提出短期重大项目驱动,推动绿色交通转型联动;中期提升协同水平,加强跨区域交通碳减排协调机制;长期顶层零碳设计,工业化-产业化-市场化全面协同等政策建议.

基于知识图谱与BERT的多模态虚假新闻检测模型

面对多模态虚假新闻的检测挑战,文章提出一种融合知识图谱与基于Transformer的双向编码器表示(Bidirectional Encoder Representations from Transformers,BERT)的多模态虚假新闻检测模型。该模型先利用知识图谱深化对实体间关系的理解,然后借助BERT模型对文本内容进行深入分析,从而实现文本、图像及知识数据高效整合。多数据集实验结果显示,该模型在多模态新闻检测,特别是含图像与视频内容的新闻检测上,展现了显著的优势。这为虚假新闻的准确检测开辟了新途径。

自适应特征融合的多模态实体对齐研究

多模态数据间交互式任务的兴起对于综合利用不同模态的知识提出了更高的要求,因此融合不同模态知识的多模态知识图谱应运而生.然而,现有多模态知识图谱存在图谱知识不完整的问题,严重阻碍对信息的有效利用.缓解此问题的有效方法是通过实体对齐进行知识图谱补全.当前多模态实体对齐方法以固定权重融合多种模态信息,在融合过程中忽略不同模态信息贡献的差异性.为解决上述问题,设计一套自适应特征融合机制,根据不同模态数据质量动态融合实体结构信息和视觉信息.此外,考虑到视觉信息质量不高、知识图谱之间的结构差异也影响实体对齐的效果,本文分别设计提升视觉信息有效利用率的视觉特征处理模块以及缓和结构差异性的三元组筛选模块.在多模态实体对齐任务上的实验结果表明,提出的多模态实体对齐方法的性能优于当前最好的方法.