粘弹性材料动态特性研究的系统识别法

本文应用系统识别法研究粘弹性材料动态特性,给出了两种粘弹材料动态特性的实验结果,介绍了试验装置及微机测试系统。理论分析和实验结果表明,本方法对高阻尼粘弹材料是有效的。

诊断桩结构故障的振动系统识别方法

本文提出了一种诊断桩结构故障的振动系统识别方法。在理论分析方面,建立了一个“桩-土”系统的连续型动力学模型,并导出了其解析解,提供了用系统识别法诊断桩故障的理论依据;在试验分析方面,采用模态分析理论,通过实测传递函数。识别出“桩-土”系统的诊断参数,用于桩结构完整性检验及单柱承载能力评价。最后给出了几个应用实例,其诊断结论与实际开挖结果相当一致。

用系统识别方法研究大底盘、底部大空间高层建筑抗震性能

本文以作者在同济大学工程结构研究所4×4m模拟地震振动台上所做的小比例微粒混凝土十八层结构模型试验为基础,运用系统识别方法研究了这种结构的动力计算模型和弹塑性动力反应,寻出其在地震作用下反应特征和抗震薄弱环节,并提出一些设计建议。

结构健康监测的两步诊断法

结构健康监测在过去的20年引起了极大的注意,其目的在于对由于超负荷或长期环境效应而引起的结构损伤进行探测、定位以及危险性的估计.已经提出许多种方法,诸如运用模型识别技术的直接法,还有基于结构模型的反演法.本文给出结构健康监测的两步诊断法,第一步,是运用测得的完好结构及损伤结构响应进行模态参数识别.第二步,是运用这些数据并用贝叶斯系统识别法修正结构模型.然后用完好和损伤结构的模型刚度参数修正概率分布来计算某一子结构损伤的概率.

基于迭代法的柔性路面模量动态反分析

根据系统识别法的基本原理,采用以灵敏度分析为基础的迭代法调整参数,对柔性路面结构进行位移动态反分析,编制柔性路面结构模量反算程序。研究结果表明,本文的反算方法稳定收敛,初始值对反算结果影响很小,反算结果精度较高,可在实际工程中应用。

汽车发动机液压悬置研究方法探讨

发动机液压悬置是非线性很强的隔振元件,其动特性因激振频率和激振振幅的改变而改变。试验分析和理论研究是研究液压悬置的两种基本方法。文中通过分析典型液压悬置的结构特征,全面总结液压悬置的试验方法、理论模型和优化设计方法等方面的研究现状,分析了将试验研究和理论分析相结合、采用系统参数识别方法对液压悬置进行研究的可行性,并探讨了最优试验设计准则。

结构检测与健康监测概况

阐述了桥梁结构检测的方法，简要地总结桥梁健康监测的研究状况及研究动态，提出桥梁结构健康监测的新理念。

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

Design and Implementation of Verification Code Identification Based on Anisotropic Heat Kernel

Many websites use verification codes to prevent users from using the machine automatically to register,login,malicious vote or irrigate but it brought great burden to the enterprises involved in internet marketing as entering the verification code manually.Improving the verification code security system needs the identification method as the corresponding testing system.We propose an anisotropic heat kernel equation group which can generate a heat source scale space during the kernel evolution based on infinite heat source axiom,design a multi-step anisotropic verification code identification algorithm which includes core procedure of building anisotropic heat kernel,settingwave energy information parameters,combing outverification codccharacters and corresponding peripheral procedure of gray scaling,binarizing,denoising,normalizing,segmenting and identifying,give out the detail criterion and parameter set.Actual test show the anisotropic heat kernel identification algorithm can be used on many kinds of verification code including text characters,mathematical,Chinese,voice,3D,programming,video,advertising,it has a higher rate of 25%and 50%than neural network and context matching algorithm separately for Yahoo site,49%and 60%for Captcha site,20%and 52%for Baidu site,60%and 65%for 3DTakers site,40%,and 51%.for MDP site.

An application of system identification in the two-degree-freedom VIV experiments

Experiments on the two-degree-freedom vortex-induced vibration （VIV） of a flexibly-mounted, rigid, smooth cylinder were performed at MIT. The research reported here is an analysis of the cylinder＇s trajectories. System identification methods were used to derive a best Fourier representation for these motions and to parse these motions into symmetric and asymmetric behaviors. It was postulated that the asymmetric behavior was due to distortions caused by the free surface and bottom used at the test facility, and that the symmetric behavior is representative of deepwater VIV. Further application of systems identification methods was used to associate the symmetric behavior and test conditions to a traditional vortex street model. These models were analyzed for their ability to predict details of VIV trajectories.

State Representation Methodology （SRM） for Bridge Condition Assessment in SHM

This paper introduces a new concept of ＂State Representation Methodology （SRM）＂ which is a kind of bridge condition assessment method for structural health monitoring system （SHM）. There are many methods for system identification from the simplicity comparison of damage index to the complicated statistical pattern recognition algorithms in SHM. In these methods, modal analysis and parameters identification or many defined indices are common-used for extracting the dynamic or static characteristics of a system. However, there is a common problem： due to the complexity of a large size system with high-order nonlinear characteristics and severe environment interference, it is impossible to extract and quantify exactly these modal parameters or system parameters or indices as the feature vectors of a system in damage detection in an easy way. The SRM considered a more general theory for the non-parametric description of system state.

Two-stage approach to full Chinese parsing

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

Localization of pallets in warehouses using passive RFID system

Warehouse operation has become a critical activity in supply chain. Position information of pallets is important in warehouse management which can enhance the efficiency of pallets picking and sortation. Radio frequency identification(RFID) has been widely used in warehouse for item identifying. Meanwhile, RFID technology also has great potential for pallets localization which is underutilized in warehouse management. RFID-based checking-in and inventory systems have been applied in warehouse management by many enterprises. Localization approach is studied, which is compatible with existing RFID checking-in and inventory systems. A novel RFID localization approach is proposed for pallets checking-in. Phase variation of nearby tags was utilized to estimate the position of added pallets. A novel inventory localization approach combing angle of arrival(AOA) measurement and received signal strength(RSS) is also proposed for pallets inventory. Experiments were carried out using standard UHF passive RFID system. Experimental results show an acceptable localization accuracy which can satisfy the requirement of warehouse management.

Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

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

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Research on the Automatic Pattem Abstraction and Recognition Methodology for Large-scale Database System based on Natural Language Processing

In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connection between nodes, data across different nodes and even regional distribution are well recognized. In order to reduce data redundancy and model design of the database will usually contain a lot of forms we combine the NLP theory to optimize the traditional method. The experimental analysis and simulation proves the correctness of our method.

Face Recognition Using LDA with Wavelet Transform Approach

Linear Discriminant Analysis （LDA） is one of the principal techniques used in face recognition systems. LDA is well-known scheme for feature extraction and dimension reduction. It provides improved performance over the standard Principal Component Analysis （PCA） method of face recognition by introducing the concept of classes and distance between classes. This paper provides an overview of PCA, the various variants of LDA and their basic drawbacks. The paper also has proposed a development over classical LDA, i.e., LDA using wavelets transform approach that enhances performance as regards accuracy and time complexity. Experiments on ORL face database clearly demonstrate this and the graphical comparison of the algorithms clearly showcases the improved recognition rate in case of the proposed algorithm.

System identification of channel roughness for Mid-Route Project of South-to-North Water Diversion

This paper presents a new method for the system identification of the channel roughness for the water diversion projects. According to the principle of hydraulics,the function relationship among channel roughness n, roughness height k s and hydraulic radius R is established,and then a linear model is deduced by means of the mathematical transformation to make use of the least square method for identification. Finally,based on the prototype observation data from the South-to-North Water Diversion Project and considering the influence of channel lengths,cross-section shapes and bottom slopes,etc,a universal formula is obtained for calculation of channel roughness by the system identification.

Research on Recognition Technology of 2-Dimensional Barcode

This paper briefly introduces the characteristics and structure of symbol QR two-dimensional code, a detailed analysis of the image processing method to identify QR code of the whole process, and the bilinear mapping method is applied to image correction, the final steps of decoding are given. The actual test results show that, the design algorithm has theoretical and practical, this recognition system can correctly read QR code, and has high recognition rate and recognition speed, has practical value and application prospect.