Structural modal parameter identifi cation and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.

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 Theory and Practice of the Interpretation Identification System Designing of Geoparks

In this paper,the author defined the interpretation system of geoparks,studied the designing princeples and methods of the interpretation identification system,including the information,content and appearance. Furthermore,the designing of the interpretation identification system designing of the Hanas National Geopark was conducted as an empirical study,

An Optical Disdrometer for Measuring Present Weather Parameters

A new present weather identifier(PWI) based on occlusion and scattering techniques is presented in the study. The present weather parameters are detectable by the meteorological optical range(MOR) approximately up to 50 km and by droplets with diameters ranging from 0.125 mm to 22 mm with velocities up to 16 m s-1. The MOR error is less than 8% for the MOR within 10 km and less than 15% for farther distances. Moreover, the size errors derived from various positions of the light sheet by the particles were checked within ± 0.1 mm ± 5%. The comparison shows that the MOR, in a sudden shower event, is surprisingly consistent with those of the sentry visibility sensors(SVS) with a correlation coefficient up to 98%. For the rain amounts derived from the size and velocity of the droplets, the daily sums by the PWI agree within 10% of those by the Total Rain Weighing Sensor(TRwS205) and the rain gauge. Combined with other sensors such as temperature, humidity, and wind, the PWI can serve as a present weather sensor to distinguish several weather types such as fog, haze, mist, rain, hail, and drizzle.

Random but System-Wide Unique Unlinkable Parameters

When initializing cryptographic systems or running cryptographic protocols, the randomness of critical parameters, like keys or key components, is one of the most crucial aspects. But, randomly chosen parameters come with the intrinsic chance of duplicates, which finally may cause cryptographic systems including RSA, ElGamal and Zero-Knowledge proofs to become insecure. When concerning digital identifiers, we need uniqueness in order to correctly identify a specific action or object. Unfortunately we also need randomness here. Without randomness, actions become linkable to each other or to their initiator’s digital identity. So ideally the employed (cryptographic) parameters should fulfill two potentially conflicting requirements simultaneously: randomness and uniqueness. This article proposes an efficient mechanism to provide both attributes at the same time without highly constraining the first one and never violating the second one. After defining five requirements on random number generators and discussing related work, we will describe the core concept of the generation mechanism. Subsequently we will prove the postulated properties (security, randomness, uniqueness, efficiency and privacy protection) and present some application scenarios including system-wide unique parameters, cryptographic keys and components, identifiers and digital pseudonyms.

Point-of-care ultrasound identifi cation of yolk stalk sign in a case of failed fi rst trimester pregnancy

Emergency physicians are often the first providersto encounter patients with complications in earlypregnancy. Point-of-care （POC） pelvic ultrasound isbeing increasingly used in the evaluation of emergencydepartment （ED） patients with first trimester symptoms.[1]While the initial aim of POC ultrasound in this settingis to confirm an intrauterine pregnancy, a secondarygoal is to differentiate between a normal and abnormalpregnancy. There exist a number of sonographic featuresto suggest a pregnancy is non-viable.

Geometrical Frameworks in Identification Problem

The purpose of this review is to apply geometric frameworks in identification problems. In contrast to the qualitative theory of dynamical systems (DSQT), the chaos and catastrophes, researches on the application of geometric frameworks have not been performed in identification problems. The direct transfer of DSQT ideas is inefficient through the peculiarities of identification systems. In this paper, the attempt is made based on the latest researches in this field. A methodology for the synthesis of geometric frameworks (GF) is proposed, which reflects features of nonlinear systems. Methods based on GF analysis are developed for the decision-making on properties and structure of nonlinear systems. The problem solution of structural identifiability is obtained for nonlinear systems under uncertainty.

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.

基于Kelvin模型的圆形巷道位移反分析的唯一性

讨论了黏弹性岩体中圆形巷道位移反分析的唯一性问题。以Kelvin模型为研究对象,在圆形巷道位移解析解的基础上,利用参数可辨识条件,对岩体参数进行了位移反分析唯一性的研究。结果表明:无论测量出多少个测点的位移,都不能唯一地反分析出所有岩体参数,至少要已知2个参数,才有可能唯一地反分析出其余参数;随着已知参数的增多,各参数的可辨识性逐渐增强;竖向地应力与水平地应力是否相等,且二者作为已知条件时其大小是否为0对反分析结果有较为明显的影响;适当减少待反演参数的数量对提升反分析结果的唯一性有显著影响;所有参数中地应力具有最好的可辨识性,其次是弹性模量,泊松比及黏滞系数最差。

对甘精胰岛素有关物质分析用系统适用性试验的研究

目的:对不同企业甘精胰岛素有关物质分析用系统适用性试验进行研究,发现存在的问题,提示企业完善质量标准,更科学有效地控制产品质量。方法:采用高效液相色谱法及液质联用方法对不同企业有关物质分析用系统适用性试验进行研究评价,发现存在多种问题。结果:部分企业系统适用性溶液中所用的“0A-甘精胰岛素”的结构经LC-MS/MS确认后,发现与理论序列不同,部分企业采用酶切方法制备系统适用性溶液,存在杂质较多、操作步骤复杂、溶液易浑浊等问题。结论:建议企业提高对系统适用性试验的重视程度,加强研究,完善质量标准,科学有效地进行药品质量控制。

鹿茸标准鉴别技术进展研究

鹿茸是一种具有重要药用价值的中药材,具有多种功效和广泛的临床应用。然而,由于市场上出现了大量的假冒伪劣产品,对鹿茸的鉴别技术的研究变得十分重要。本文综述了近年来鹿茸的鉴别技术进展,包括传统的鉴别方法以及现代分子生物学和化学分析技术等。同时,还对当前鹿茸鉴别技术的挑战和未来发展方向进行了讨论。

基于HMM与SVM的运动跌倒预警模型研究——以安徽省老年群体为例

人口老龄化问题日益加重,老年人的身体机能随着年龄的增加而降低.为提升运动跌倒预警的检测率和延长预留时间,提出基于HMM与SVM的运动跌倒预警模型,通过人体行为特征分析和判别算法设计实现运动跌倒的成功预测,并以安徽省老年群体为背景,从中采集实验数据.实验结果表明,SVM模型的检测率为69.5%,基于HMM与SVM的运动跌倒预警模型的检测率为91%,对比结果表明基于HMM与SVM的运动跌倒预警模型针对突发情况的误判率较低,具备良好的泛化能力.基于HMM与SVM的运动跌倒预警模型检测的平均预留时间是245 ms,满足充气机构反应耗时,结果有助于老年人跌倒预警以及人体保护.

E型牛肠道病毒新疆BL311株的分离鉴定及致病性分析

以采集自新疆博乐某牛场腹泻牛的粪便,接种胎牛肾细胞(Madin-Darby bovine kidney,MDBK),盲传15代分离牛肠道病毒(Bovine enterovirus,BEV),使用蚀斑纯化法连续纯化5轮得到纯化病毒,并命名为BL311。使用BL311株感染MDBK细胞后,观察细胞病变效应(cytopathic effect,CPE),采用Reed-Muench法计算病毒滴度,利用理化性质检测、透射电镜观察、基因型分析及对小鼠的致病性试验鉴定BL311分离株。结果显示,BL311分离株感染MDBK细胞24 h后细胞出现变圆、破裂和脱落等致细胞病变效应,病毒滴度显著增高(P<0.05);36 h后细胞出现大量CPE,病毒滴度极显著增高(P<0.01)。理化性质鉴定结果显示BL311分离株对有机溶剂具有一定抵抗力,紫外光照射1 h或56℃环境培养1 h可使毒株失活。电镜下观察BL311毒株为直径25~30 nm的球形颗粒,具有典型的小RNA病毒粒子形态。基因测序结果显示BL311毒株的基因全长是7514 nt,与国内报道的HY12分离株(KF748290)的同源性高达99%,属于BEV-E3型。致病性实验结果显示,BALB/c小鼠感染BL311毒株后,器官表现为肿大,且伴有出血;病理组织学观察可见组织器官炎性细胞浸润及出血。本研究分离纯化得到1株具有较强致病性的E种肠道病毒,将为新疆BEV的流行病学调查及致病机制研究提供依据。

相似异源指纹研究进展及展望

指纹鉴定错案的发生引发了对指纹证据科学性要求的提高,伴随着指纹数据库的不断扩增,指纹相似异源问题已经成为指纹检验领域的重要研究内容之一,其对指纹鉴定标准的构建具有重要意义,也给指纹鉴定带来风险和挑战。本文首先介绍相似异源指纹的研究背景以及概念,然后从相似异源指纹的产生原因和鉴定错误率两方面展开综述,最后结合研究现状对其未来的发展趋势提出几点展望,以期补充指纹鉴定基础理论,为指纹证据的量化评价提供一定的参考。

双馈风电机组传动系统神经网络建模及参数预测

传动系统是双馈风电机组的重要组成部分,其模型对电力系统同步稳定及频率稳定分析具有重要影响,准确的传动系统模型参数是分析新能源电力系统动态特性的前提。为解决因大扰动量测信息不充裕导致模型参数难以辨识的困难,提出利用机组正常运行状态时随机小扰动激励下丰富的历史响应数据,根据响应数据与模型参数的对应关系构建神经网络模型,并根据当前响应数据进行驱动系统模型参数预测。首先讨论了基于BP神经网络进行数据建模的基本流程;针对含双馈风电机组的无穷大系统仿真算例,提取随机风速扰动下响应信号受扰轨迹的功率谱特征;定义功率谱灵敏度指标,提出选取功率谱灵敏度较大的参数作为重点参数;最后基于BP神经网络构建响应信号功率谱与模型参数之间的非线性映射,基于训练得到的BP网络辨识新响应下的模型参数。通过分析BP神经网络动态模型的误差,验证数据驱动建模方法的可行性。

Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of identifiability.In this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is proposed.The load model structure and the combined optimization and regression method to identify the parameters are first introduced.Then,the definition and analysis method of identifiability are discussed.The CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate identifiability.Finally,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid.The results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed PII.Furthermore,the proposed PII can provide guidance in data length selection in practical load model identification situations.

职业本科学校校企合作育人项目风险识别与对策研究

为系统性识别职业本科学校校企合作育人项目中存在的风险因素及其影响机理并提出应对策略,提升职业本科学校就业质量和企业绩效,促进职业本科学校与企业合作育人项目的推进。本研究基于产教融合背景,以职业本科学校为研究主体,采用DEMATEL-ISM方法,对风险因素间相互影响关系以及对项目总体影响强度进行了分析,计算了风险因素的中心度和原因度,构建了多层次解释结构模型,划分了风险因素的影响层次,明确了各层次间的风险传递路径,可为有效管控校企育人项目风险提供决策参考。

基于YOLO算法的智能云预警系统的设计和应用

为了解决当前各类市政施工对通信光缆维护的威胁,本文提出了基于YOLO算法的智能云光缆施工预警系统,通过自动化引流视频上云、基于YOLO_v3算法目标识别技术识别挖掘机、主动触发预警三大功能实现光缆人防到机防的升级,有效提升维护效率。

笔迹鉴定的量化及统计学应用方法综述

统计学方法在笔迹鉴定领域的研究中越来越受到重视,采取有效的统计学方法对笔迹的特征进行客观量化,并对特征数据进行合理的分析,不仅可以为笔迹鉴定结论提供强有力的理论依据,更可以深入挖掘复杂数据背后的信息。本文梳理了笔迹特征量化和特征数据处理中几种常用统计学方法,介绍了它们的原理、应用以及最新进展,并对笔迹鉴定中统计学方法进行了展望。