Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

THE THREE-DIMENSIONAL ANALYSIS OF THE SELF-VIBRATIONAL CHARACTERI-STICS OF THE 2050MM HOT-STRIP MILL HOUSING BY MEANS OF LASER HOLOGRAPHIC INTERFEROMETRY AND FINITE ELEMENT METHOD

According to the theory of similarity, a three-dimensional simulation study on the self-vibrational characteristics of the 2050mm hot-strip finishing mill housing at Baoshan Iron and Steel Complex has been carried out. The analysis of the main vibrational modes of the first three orders has also been done by means of holographic interferometry. In addition, the authors have carried out the numerical analysis of finite elements in three dimensions. The comparison of the results of both analyses (simulation analysis and numerical analysis of finite element) shows that they are consistent.

Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

Utilization of biomarkers for the prognostic prediction of cardiac arrest survivors using a multi-modal approach

International guidelines for post-cardiac arrest care recommend using multi-modal strategies to avoid the withdrawal of life-sustaining therapy(WLST)in patients with the potential for neurological recovery.[1]However,a clear methodology for multi-modal approaches has yet to be developed.Neuron-specific enolase(NSE)is currently the only recommended biomarker,and the European Resuscitation Council(ERC)and the European SocietyofIntensiveCareMedicine(ESICM)have proposed a cutoff value of 60μg/L at 48 and/or 72 h after the return of spontaneous circulation(ROSC)as a multimodal prognostic tool for predicting poor neurological outcomes.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

The role of multimodality imaging in calcified valves with infective endocarditis

A 68-years old woman presented with ahistory of recurrent fever of 38-39°ac-companied by chills and weakness over the past month.Her physical examination was unremarkable except for an audible 3/6 ejection murmur at the 2nd right intercostal space.Her vital signs were normal with no fever at presentation.Laboratory tests showed elevated white blood count of 11,800cells/mm3 with a remarkable neutrophilia and elevated C-reactive protein of 14 mg/dL.Blood glucose,renal and liver function tests were all normal.

Saturated Models and Ultrafilter Extension for Weakly Aggregative Modal Logics

Weakly aggregative modal logics (WAML) are a series of natural weakenings o the minimal modal logic K.The natural semantics for them are based on Kripke frames with an N+1-ary relation,where □φ is true at a world iff all of its successor N-tuples has at leas one world making φ true.We study the notion of saturated models and ultrafilter extension fo this relational semantics of WAML.The Goldblatt-Thomason theorem for WAML is proved as an application.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

基于深度学习的人脸呈现攻击检测方法研究进展

随着人脸识别技术广泛应用于公共安全、金融支付等领域,呈现攻击(Presentation Attacks,PAs)对人脸识别系统的安全性构成了威胁。呈现攻击检测技术(Presentation Attacks Detection,PAD)旨在判断输入人脸的真伪,对维护识别系统的安全性和鲁棒性具有重要的研究意义。由于大规模数据集的不断涌现,基于深度学习的呈现攻击检测方法逐渐成为该领域的主流。文章对近期基于深度学习的人脸呈现攻击检测方法进行了综述。首先,概述了呈现攻击检测的定义、实施方式和常见的攻击类型;其次,分别从单模态和多模态入手,对近五年来深度学习类方法的发展趋势、技术原理和优缺点进行详细分析和总结;然后,介绍了PAD研究中使用的典型数据集及其特点,并给出算法的评估标准、协议和性能结果;最后,总结了PAD研究中面临的主要问题并展望了未来的发展趋势。

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

面向社交网络平台的多模态网络欺凌检测模型研究

随着社交网络平台的迅速发展,网络欺凌问题日益突出,文本与图片相结合的多样化网络表达形式提高了网络欺凌的检测和治理难度.构建了一个包含文本和图片的中文多模态网络欺凌数据集,将BERT(bidirectional encoder representations from transformers)模型与ResNet50模型相结合,分别提取文本和图片的单模态特征,并进行决策层融合,对融合后的特征进行检测,实现了对网络欺凌与非网络欺凌2个类别的文本和图片的准确识别.实验结果表明,提出的多模态网络欺凌检测模型能够有效识别出包含文本与图片的具有网络欺凌性质的社交网络帖子或者评论,提高了多模态形式网络欺凌检测的实用性、准确性和效率,为社交网络平台的网络欺凌检测和治理提供了一种新的思路和方法,有助于构建更加健康、文明的网络环境.

燃气涡轮界面参数对模态频率影响规律研究

燃气涡轮是驱动船舶、车辆的核心动力机械,叶-盘结构是燃气轮机进行能量转化的关键部分,探究其界面参数对模态频率的影响规律,对提高叶-盘结构安全性具有重大意义。以燃气涡轮枞树形叶-盘结构为研究对象,建立叶-盘结构有限元仿真模型,仿真计算接触刚度、材料弹性模量、材料密度等不同界面参数下的模态频率,分析其影响规律,可为叶-盘结构耦合系统的模态频率预测及其改进提供必要的理论支撑与技术指导。

基于音频特征的拖拉机发动机状况识别系统设计

拖拉机发动机是保证拖拉机正常运行的关键部件,目前主要采用振动信号开展发动机故障预测与状况识别。为此,提出了一种基于GRU的循环神经网络模型,通过对拖拉机发动机在不同作业条件下产生的音频信号进行分析,提取Mel作为主要特征,构建基于音频特征的拖拉机发动机状况识别系统。预测结果表明:系统能够准确地识别发动机的正常运行状态和不同类型的故障状况,对拖拉机发动机异常的识别率可以达到97.15%。研究结果可以提高拖拉机的运行安全性和可靠性,减少故障停机时间,提高农业生产效率。

面向铁路巡检的低空无人机智能感知与精密定位研究

铁路巡检作为“低空+轨道”的典型应用场景之一,采用低空无人机巡检辅助或代替人工巡检,在提高巡检效率同时,可以降低巡检成本和巡检人员的事故发生率。调研国内外铁路无人机巡检技术与应用现状,指出当前存在的空-地巡检模式下无人机智能感知、“高密度短间隔”场景无人机精密定位、无人机感知与定位如何协同等问题。针对上述问题,研究融合视觉和激光雷达的低空无人机多模态智能感知技术,与组合高精度卫星导航、惯性导航、UWB和5G的多传感器精密定位技术,提升低空无人机的铁路综合巡检性能。基于两项关键技术,设计一种协同智能感知与精密定位的低空铁路无人机巡检方案,为未来铁路领域的低空无人机巡检推广应用提供支撑。

氢气循环泵的转子动力学特性分析

基于ANSYS构建了用于燃料电池汽车氢气供应系统的氢气循环泵(罗茨泵)转子系统的模型,通过对转子系统的模态分析,得到了独立转子系统和双转子系统的前六阶固有振型和固有频率。在模态分析的基础上分析了转子系统的谐响应特性,获得了齿轮、叶轮边缘两个部位的谐响应特性曲线。结果表明:轴端、转子叶轮等结构振动变形量较大,在接近前三阶固有频率(960Hz左右)时转子系统振动响应幅值最大,双转子耦合系统相对于单独转子系统会出现更加复杂的振型,转子叶轮对不平衡量的响应更显著。研究将为氢气循环泵转子系统的设计制造和振动特性预估提供积极的参考作用,并为氢气循环泵动力学特性的研究奠定基础。

用于红外-可见光图像分类的跨模态双流交替交互网络

多特征模态融合时存在噪声的叠加,而为减小模态间的差异采用的级联方式的结构也未充分利用模态间的特征信息,因此设计一种跨模态双流交替交互网络(DAINet)方法。首先,构建双流交替增强(DAE)模块,以交互双分支形式融合模态特征,并通过学习模态数据的映射关系,以红外-可见光-红外(IR-VIS-IR)和可见光-红外-可见光(VIS-IR-VIS)的双向反馈调节实现模态间噪声的交叉抑制;然后,构建跨模态特征交互(CMFI)模块,并引入残差结构将红外-可见光模态内以及模态间的低层特征和高层特征进行有效融合,从而减小模态间的差异并充分利用模态间的特征信息;最后,在自建红外-可见光多模态台风数据集及RGB-NIR多模态公开场景数据集上进行实验,以验证DAE模块和CMFI模块的有效性。实验结果表明,与简单级联融合方法相比,所提的基于DAINet的特征融合方法在自建台风数据集上的红外模态和可见光模态上的总体分类精度分别提高了6.61和3.93个百分点,G-mean值分别提高了6.24和2.48个百分点,表明所提方法在类别不均衡分类任务上的通用性;所提方法在RGB-NIR数据集上的2种测试模态下的总体分类精度分别提高了13.47和13.90个百分点。同时,所提方法在2个数据集上分别与IFCNN(general Image Fusion framework based on Convolutional Neural Network)和DenseFuse方法进行对比的实验结果表明,所提方法在自建台风数据集上的2种测试模态下的总体分类精度分别提高了9.82、6.02和17.38、1.68个百分点。

两自由度含间隙弹性碰撞振动系统的等效电路仿真

以一类两自由度含间隙及弹性约束碰撞系统为研究对象,通过数值仿真方法,研究了系统在低频工况下周期运动的区域分布及转迁规律,得出随着激振频率的减小,p/1周期运动发生擦边分岔转迁为(p+1)/1周期运动,当碰撞次数足够大时,系统呈现Chattering-impact特性。随后建立与碰撞系统完全等效的电路模型并进行仿真实验。研究结果表明,建立等效电路产生的结果与数值仿真产生的结果相一致,且等效电路在仿真实验时运算速度更高效,能够实现快速的模态转换及参数调节,使之更加便捷,为非线性动力学的研究提供了一种研究方法。

基于应变模态的风力机动态偏航工况下叶片振动感知方法

针对现阶段运行状态下风力机叶片振动测量方法中,加速度传感器常布设到风力机机头靠近风轮处,导致测量结果易受干扰的问题。基于叶片受力分析和应变模态理论,将叶片上应变传感器采集的原始信号分解为叶片应变信号与振动信号的叠加,设计一种基于应变模态理论和变分模态分解的叶片振动感知方法。通过设计静态模态实验和模拟动态偏航的叶片振动实验,对叶片表面应变和机头振动信号进行对比分析,表明叶片振动感知方法在频域和时频域上均能正确识别叶片振动特征,且其谱线更清晰,受干扰更小,从而为动态偏航运行工况下风力机叶片振动测量提供了一种新方法,可为风力机叶片安全、可靠运行提供技术支持。

光伏新能源电能信号游程域模态分解理论与特征提取

高比例光伏新能源接入下的电能信号呈现出强随机、快时变、大波动特性,常导致电能表电能计量严重超差,影响电能交易的公正合理性。针对新能源电能信号敏感特征不清楚、对电能表电能计量影响不明确的问题,建立了游程域映射理论,解决了电能信号在游程域的表征问题;提出了游程域模态分解(R-EMD)方法,基于游程完成电流幅度在长持续时间下局部区间的自适应模态分解,得出光伏新能源电能信号的2个全局重要特征;构建了游程域特征参量与特征函数,并提取得到光伏新能源电能信号的4个游程域敏感特征;通过实验验证了游程域敏感特征对电能计量的影响。