Delay Trigger in the Application of Network Testing System

The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discussed which were clock synchronization, trigger synchronization, and response synchronization. Synchronous process between LXI instruments was analyzed and each time factor affecting the synchronization accuracy was discussed. On the basis of the analysis,it can be found that delay trigger plays an important role in the network testing system's synchronization. Delay trigger can produce an additional time interval to correct the difference of each LXI instrument's response time. Then,a method to realize the delay trigger was introduced. Delay time can be adjustable according to the actual demand. Finally,synchronization accuracy of network testing system can reach nanoseconds.

Networked collaborative pseudo-dynamic testing of a multi-span bridge based on NetSLab

Modem dynamic tests such as networked collaborative pseudo-dynamic testing （PDT） provide new tools to study the dynamic performance of large and complex structures. In this paper, several networked collaborative PDT systems established in China and abroad are introduced, including a detailed description of the first networked collaborative platform that involved the construction of a standardized demonstration procedure for networked collaborative PDT. The example is a multi-span bridge with RC piers retrofitted by FRP, and a networked structural laboratory （NetSLab） platform is used to link distributed laboratories located at several universities together. Substructure technology is also used in the testing. The characteristics, resource sharing and collaborative work of NetSLab are described, and the results illustrate that use of the NetSLab is feasible for studying the dynamic performance of multi-span bridge structures.

Research on Testing Technologies of Network System in Smart Substations

The network structures of smart substations and the characteristics of industrial Ethernet switches are analyzed.The testing technologies of network systems based on smart substations are specifically elaborated.A viewpoint is proposed that special testing policy&method of smart substation networks should be followed,so that the results can reveal the real network data exchange performance of the whole station.This view ensures the safety and stability of smart substations and lays a foundation for future upgrades and expansions.

Data Consistency Tests through the Use of Neural Networks and Virial Equation. Application of the Proposed Methodology to Critical Study of Density Data

This paper focuses on a very important point which consists in evaluating experimental data prior to their use for chemical process designs. Hexafluoropropylene P, ρ, T data measured at 11 temperatures from 263 to 362 K and at pressures up to 10 MPa have been examined through a consistency test presented herein and based on the use of a methodology implying both neural networks and Virial equation. Such a methodology appears as very powerful to identify erroneous data and could be conveniently handled for quick checks of databases previously to modeling through classical thermodynamic models and equations of state. As an application to liquid and vapor phase densities of hexafluoropropylene, a more reliable database is provided after removing out layer data.

Test Effort Estimation Using Neural Network

In software industry the major problem encountered during project scheduling is in deciding what proportion of the resources has allocated to the testing phase. In general it has been observed that about 40%-50% of the resources need to be allocated to the testing phase. However it is very difficult to predict the exact amount of effort required to be allocated to testing phase. As a result the project planning goes haywire. The project which has not been tested sufficiently can cause huge losses to the organization. This research paper focuses on finding a method which gives a measure of the effort to be spent on the testing phase. This paper provides effort estimates during pre-coding and post-coding phases using neural network to predict more accurately.

<i>Inmap-t</i>: Leveraging TTCN-3 to Test the Security Impact of Intra Network Elements

This paper rejuvenates the notion of conformance testing in order to assess the security of networks. It leverages the Testing and Test Control Notation Version 3 (TTCN-3) by applying it to a redefined notion of System under Test (SUT). Instead of testing, as it is classically done, a software/firmware/ hardware element, an intangible object, namely the network, is tested in order to infer some of its security properties. After a brief introduction of TTCN-3 and Titan, its compilation and execution environment, a couple of use cases are provided to illustrate the feasibility of the approach. The pros and cons of using TTCN-3 to implement a scalable and flexible network testing environment are discussed.

Development Methodologies for Network Softwarization: A Comparison of DevOps, NetOps, and Verification

This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC.This study presents the application of artificial neural networks(ANNs) for a priori prediction of the effectiveness of RDC.The models are trained with in situ dynamic cone penetration(DCP) test data obtained from previous civil projects associated with the 4-sided impact roller.The predictions from the ANN models are in good agreement with the measured field data,as indicated by the model correlation coefficient of approximately 0.8.It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.

Load Reduction Test Method of Similarity Theory and BP Neural Networks of Large Cranes

Static load tests are an important means of supervising and detecting a crane＇s lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes.

猕猴桃便携式无损检测仪影响因素及其精度校准分析

为探究产地、成熟度对便携式可溶性固形物无损检测精度的影响,以同一品种3个产地的猕猴桃为试材,分别采用便携式无损检测仪和常规有损检测仪对其可溶性固形物含量进行测定和比较,并运用人工神经网络对其进行分析。结果表明,在使用便携式无损检测仪对猕猴桃可溶性固形物检测时,3个产地的无损检测R2分别为0.26、0.52、0.61,成熟度会对其检测精度造成一定影响,但在不同产地间没有稳定、统一的规律;基于人工神经网络学习、建模产生的随机预测值可将R2提高5%~49%。因此,可利用预测值来作为中间校准程序,以辅助提高便携式无损检测精度。

Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network

In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network.Because the wavelet ba- sis function neural network (WBFNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively,a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes.And such details as the choice of wavelet basis function,the initialization of the weight value and the input normalization are analyzed,the train- ing and testing algorithm for the network are also studied.The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects.

基于机器学习与DBN网络的网络入侵检测方法研究

随着计算机网络的发展,网络入侵的情况也越来越严重。传统网络入侵检测方法存在检测效率低、误判率高的情况,为了解决这些问题,提出了一种基于支持向量机的深度置信网络(SVM-DBN)的入侵检测方法。通过对支持向量机(SVM)进行优化,将支持向量机与深度信念网络(DBN)融合,利用SVM、DBN与SVM-DBN在网络入侵数据集中进行对比。结果表明,SVM-DBN算法的误差率最低,比DBN和SVM的误差率平均值分别低了8.95%,12.70%,且SVM-DBN算法在训练次数为140次时最大绝对百分比误差为4.8%,均优于对比方法。这说明SVM-DBN网络能够有效地提高网络入侵检测的精度和效率。

基于HFACS的道路运输事故致因分析与分类系统研究

为了深入研究道路运输事故的致因因素,挖掘出关键致因规则,在人因分析和分类系统(HFACS)的基础上,构建道路运输事故致因分析与分类系统(RTAACS),并根据该系统框架对收集到的事故进行分析。首先将RTAACS框架分为监管部门、道路运输企业、驾驶人3个层级,将事故致因因素纳入该系统框架进行频率统计,通过R语言改进卡方检验、让步比分析得出致因因素之间的关联规则,最后根据ANP网络层次分析法计算各项致因因素的权重,并确定关键致因规则。结果表明:工作流程与不充分的企业监督、工具技术与习惯性违规、资源管理与工具技术、资源管理与习惯性违规、未纠正的已知错误与习惯性违规为道路运输事故的关键致因规则。研究构建的系统框架以及提出的分析方法为道路运输业的安全预防提供一定的借鉴意义。

基于虚拟靶场的渗透测试实践教学机制探索

渗透测试实践教学是为信息安全相关专业学生开设的少数攻击类课程,是必不可少的教学环节。随着《中华人民共和国网络安全法》等法律法规的不断完善,渗透测试教学的实践模式需要不断创新。针对实践教学中资源和时间方面的难题,设计并实现基于OpenStack的虚拟靶场系统,从教学过程、教学方法 、考核方式等角度分别提出开放式线上线下混合教学、引导式基础实验过程、赛学融合的考核方式等具体改革方法,探索渗透测试实践教学的新机制。

基于模糊综合分析法的引调水工程水质响应关系分析

为提升引调水工程的环境改善效果,须对水体的自身连通水平、水质现状、水环境主要影响因子进行分析。以太湖流域跃进联圩为研究对象,利用图论连通度理论对研究区域静态基流连通性进行评价,探索圩内水系连通性的空间差异及变化特性。同时,开展短期引调水实验,结合模糊综合分析法评价引调水带来的圩内水质变化情况及规律,研究圩内水环境和水系连通性的响应关系。结果表明,模糊综合评价法在引调水环境改善分析中具有适用性,可实现对圩内各监测点位水质变化的全面分析;水体流动性是影响圩区内水质的重要环境因素,高锰酸盐指数是导致圩内水质恶化的主要影响因子。引调水工程对圩区水环境具有一定的持续改善性作用,但对基流连通性较差的区域,改善效果有限。

稳定车-轨道耦合作用下轨排响应特性试验研究

稳定车作为一种大型轨道交通运维装备,对保障铁路线路运行安全至关重要,但是其不具备道床横向阻力的在线感知能力,难以实时智能控制作业参数。为探究稳定车作业时在线感知道床横向阻力的方法,在试验线路上开展轨排动力学试验,测量稳定车-轨道耦合作用下的轨排横向载荷和轨排横向位移,并依据现有标准及现场原位试验法,测量标记轨枕处的道床横向阻力。根据实测的试验数据,采用函数模型拟合道床横向阻力与轨排横向载荷以及轨排横向位移的关系式。并进一步构建径向基函数(Radial Basis Function, RBF)神经网络,采用正交最小二乘学习算法训练网络参数,建立三者之间的非线性模型,对比函数拟合与RBF神经网络模型的准确性。结果表明:道床横向阻力测试中,随着测试轨枕承受横向载荷的增加,轨枕横向位移非线性增大。轨排动力学试验中,轨排横向载荷、轨排横向位移与道床横向阻力之间存在非线性关系。采用函数拟合得到的关系式一定程度上反映了道床横向阻力的非线性变化趋势,但拟合误差较大。采用RBF神经网络建立的模型,在测试集验证中精度更高。道床横向阻力的离线检测方法效率低下,基于RBF神经网络模型的车载感知方法可以提高检测效率。研究成果可为稳定车作业中实时在线感知道床横向阻力及智能控制作业参数提供理论支撑。

基于时变传染网络和分位数Granger因果检验的系统性风险传染研究

基于18个经济体760家金融机构SRISK系统性风险数据,采用时变参数向量自回归模型和广义方差分解法测度全球系统性风险的时变传染网络,进而采用分位数Granger因果检验考察各经济体对中国高分位段系统性风险的影响。研究结果表明,结合SRISK数据和时变传染网络能有效识别重大风险事件的发生;发达和新兴经济体均会产生风险净输出效应,且会通过直接输出或香港地区间接输出风险至中国内地;在全样本与2008年金融危机、欧债危机、中美贸易战和新冠疫情重大风险事件期间,中国皆为风险净溢入国且新冠疫情期间溢入效应达历史最高;美英两国在重大风险事件期间均对中国高分位段系统性风险产生显著影响,新冠疫情期间中国高分位段系统性风险遭受外部冲击的形势最为严峻。

卷积神经网提取特征的红外与可见光图像融合研究

当前红外与可见光图像融合存在一些难题,导致前红外与可见光图像精度低,误差大,而且前红外与可见光图像融合效率低,为了解决当前红外与可见光图像过程存在的问题,设计了基于卷积神经网提取特征的红外与可见光图像融合方法。首先分别采集对象的红外图像和可见光图像,并对原始图像去噪等预处理,改善图像的质量,然后采用卷积神经网络提取红外与可见光图像融合特征,根据特征得到红外与可见光图像融合结果,最后进行了仿真实验,结果表明本方法红外与可见光图像的融合结果的融合比率提高了0.24,平均梯度值提升了0.22,图像融合质量更高。