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.

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.

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.

非手术治疗慢性踝关节不稳者足踝功能和动态平衡能力的网状Meta分析

目的:尽管多种非手术疗法改善慢性踝关节不稳患者足踝功能和动态平衡能力的效果已得到证实,但由于治疗方法的不断更新,目前最佳的慢性踝关节不稳非手术疗法尚不明确。文章通过网状Meta分析的方法,探讨不同非手术疗法对慢性踝关节不稳患者足踝功能和动态平衡能力的疗效,以期寻找最佳治疗措施。方法:以“CAI,Exercise,Randomized controlled trial”等为检索词,通过计算机网络对PubMed,Embase,Cochrane图书馆和Web of Science数据库进行文献检索,收集各数据库建库至2024年3月关于非手术疗法治疗慢性踝关节不稳患者足踝功能或动态平衡能力的随机对照试验。运用EndNote软件进行文献管理,采用RevMan 5.4软件和Cochrane风险偏倚评估工具对纳入文献进行风险偏倚评价。使用Stata 14.0软件的network命令对足踝能力评估日常活动子量评分、足踝能力评估体育活动子量表评分、前侧星形偏移平衡测试评分、后内侧星形偏移平衡测试评分、后外侧星形偏移平衡测试评分和坎伯兰踝关节不稳问卷评分结局指标进行传统Meta分析和网状Meta分析。根据GRADE证据等级及推荐强度分级标准对结局指标进行证据强度等级评价。结果:在符合纳入标准的22项随机对照试验中,1项研究被评为低风险,8项研究被评为中风险,13项研究被评为高度风险,共计纳入952例患者,25种治疗措施。①网状Meta分析结果显示,与对照组相比,等速力量训练、平衡训练、平衡+频闪眼镜训练、力量训练、关节活动度训练、CrossFit训练、CrossFit+自我动员训练、震动板训练、美国国家运动医学学会矫正锻炼计划、触发点干针法和神经肌肉训练在改善慢性踝关节不稳患者足踝功能和动态平衡能力方面具有不同程度的显著提升效果(P<0.05)。②累积概率排序结果显示,坎伯兰踝关节不稳问卷评分排名最靠前的3种治疗措施为:关节活动度训练(88.6%)>视觉反馈平衡训练(83.1%)>CrossFit+自我动员训练(74.8%);前侧星形偏移平衡测试评分排名最靠前的3种治疗措施为:关节活动度训练(88.4%)>等速力量训练(86.9%)>美国国家运动医学学会矫正锻炼(65.0%);后内侧星形偏移平衡测试评分排名最靠前的3种治疗措施为:平衡+频闪眼镜训练(87.4%)>神经肌肉训练(74.6%)>力量训练(68.9%);后外侧星形偏移平衡测试评分排名最靠前的3种治疗措施为:CrossFit+自我动员训练(74.6%)>平衡+频闪眼镜训练(70.0%)>神经肌肉训练(63.7%);足踝能力评估日常活动子量评分排名最靠前的3种治疗措施为:美国国家运动医学学会矫正锻炼(91.9%)>平衡+频闪眼镜训练(85.6%)>震动板训练(82.2%);足踝能力评估体育活动子量表评分排名最靠前的3种治疗措施为:平衡+频闪眼镜训练(93.5%)>平衡训练(86.7%)>美国国家运动医学学会矫正锻炼(86.4%)。结论:非手术疗法可以显著改善慢性踝关节不稳患者的足踝功能和动态平衡能力,美国国家运动医学学会矫正锻炼计划在改善慢性踝关节不稳患者足踝日常活动功能方面疗效最佳;平衡+频闪眼镜训练在改善足踝体育运动功能和后内侧动态平衡能力方面疗效最佳;关节活动度训练改善前侧动态平衡能力和踝关节不稳病情方面疗效最佳;CrossFit+自我动员训练改善后外侧动态平衡能力方面疗效最佳。受纳入研究方法学偏倚和发表偏倚风险影响,各项结局指标证据强度均为低强度。因此,未来尚需更多高质量试验研究验证此结论。

Time Delay Characteristic of Industrial Wireless Networks Based on IEEE 802.15.4a

The IEEE 802.15.4a standard provides a framework for low-data-rate communication systems,typically sensor networks.In this paper,we established a realistic environment for the time delay characteristic of industrial network based on IEEE 802.15.4a.Several sets of practical experiments are conducted to study its various features,including the effects of 1） numeral wireless nodes,2） numeral data packets,3） data transmissions with different upper-layer protocols,4） physical distance between nodes,and 5） adding and reducing the number of the wireless nodes.The results show that IEEE 802.15.4a is suitable for some industrial applications that have more relaxed throughput requirements and time-delay.Some issues that could degrade the network performance are also discussed.

基于人工神经网络的C．TEST阅读理解题目难度的预测研究

实用汉语水平认定考试(简称C.TEST)是用来测试母语非汉语的外籍人士在国际环境下社会生活以及日常工作中实际运用汉语能力的考试。由于C.TEST的考试题目公开,题库数量较小,所以通过一般标准化考试采用的在部分目标被试中实施预测(field test)的方法来获取考试题目的难度参数存在困难。然而,人工神经网络技术作为现代人工智能研究的成果,在预测(prediction)领域发挥了很大作用。本文选取C.TEST(A-D级)的阅读理解题目作为研究材料,运用人工神经网络技术对其难度进行预测,得到了网络预测难度值与实际考试难度值显著相关的研究结果。这一结果表明,利用人工神经网络模型对语言测验的题目难度等参数进行预测是可行的。

Research on leakage current test method

In the leakage current test, through the high speed data collection and digital filtering to the output voltage of the human body impedance network, leakage current test that is in accordance with many kinds of electrical safety standards can be realized, and the frequency distribution information of the leakage current can be got as well, which can be used to much more completely evaluate the possible damage degree of the leakage current to the human body and analyze the reason for the appearance of the leakage current in the electric equipment.

Colorant Formulation Using 3 - layer Artificial Neural Networks

The colorant formulation using artificial neural networks (ANN) was investigated in this study. A simple 3 -layer, input - hidden - output system was constructed for the recipe formulation of one - , two - , and three -dye mixtures. Comprehensive tests were carried out to explore the properties of a 3 - layer simple ANN systematically . These properties include number of neurons in the hidden layer, learning rate of the network, momentum factor of the network, as well as the number of epochs for the learning process. The tests show accurate results for one - and two - dye mixtures while less accurate but comparable results to conventional colorant formulation systems for three - dye mixtures. It is also found that the optimum values of the neural network parameters are important towards the accuracy of the colorant formulation.

Optimization of Injection Molding Process of Bearing Stand Based on BP Network Method

The quality of injection plastic molded parts relates to precise geometry,smooth surface,strength,durability,and other indicators that are associated with the mold,materials,injection process,and service environment.The warpage is one of main defects of injection products,which cost much time and materials.In order to minimize warpage to ensure the precise shape of molded parts,it needs to combine design,service conditions,process parameters,material properties,and other factors in the design and manufacturing.Finite element tools and material database are used to analyze the occurrence of warpage,and analysis results contribute to the improvement and optimization of injection molding process of typical parts.To find the optimal process parameters in the solution space,experimental data are used to establish backpropagation(BP)network for predicting warpage of a bearing stand based on analysis with Moldflow.With a proper transfer function and the BP network architecture,results from the BP network method satisfiy the criteria of accuracy.The optimal solutions are searched in the BP network by the genetic algorithm with the finding that the optimization method based on the BP network is efficient.

Location Verification Systems in Emerging Wireless Networks

As location-based techniques and applications have become ubiquitous in emerging wireless networks, the verification of location information has become more important. In recent years, there has been an explosion of activity related to lo- cation-verification techniques in wireless networks. In particular, there has been a specific focus on intelligent transport systems because of the mission-critical nature of vehicle location verification. In this paper, we review recent research on wireless location verification related to vehicular networks. We focus on location verification systems that rely on for- mal mathematical classification frameworks and show how many systems are either partially or fully encompassed by such frameworks.

基于VAR-NETWORK模型的生猪价格关联网络构建与分析

该文以我国2011—2020年的生猪月度价格为研究对象,利用VAR模型对两地区进行格兰杰因果关系检验,构建我国生猪价格因果关联网络。通过网络分析得出以下结论:(1)我国生猪价格因果关联网络反映我国各省市生猪价格关联较为紧密,但仍有很大提升空间;(2)生猪价格溢出型省市主要集中在北部、中部和东部地区,而受益型主要集中在西部地区;(3)第四板块地区相对其他板块地区来说,更容易受到来自省外生猪价格波动的影响,其稳定性较差。

Assessment of Cyclic Resistance Ratio (CRR) in Silty Sands Using Artificial Neural Networks

In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of laboratory cyclic triaxial, torsional shear and simple shear tests results, was collected and utilized in the ANN model. Several ANN models were developed with different sets of input parameters in order to determine the model with best performance and preciseness. It has been illustrated that the proposed ANN model can predict the measured CRR of the different data set which was not incorporated in the developing phase of the model with the good degree of accuracy. The subsequent sensitivity analysis was performed to compare the effect of each parameter in the model with the laboratory test results. At the end, the participation or relative importance of each parameter in the ANN model was obtained.

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.

基于Speedtest用户测速数据的运营商网络感知对标分析

运营商网络能力对标分析有利于指导运营商的网络发展方向,实现对网络质量的宏观预警。但是,目前市场上缺乏有效真实、公开的数据用于分析运营商网络能力感知,因此无法通过数据手段深入分析运营商网络现状及发展趋势。第三方Speedtest测速平台上的"China Telecom Wuhan Branch"和"Wuhan Hello 5G"两个测速节点,仅2020年累计为各地区用户提供测速服务336万余次,其中为武汉用户提供服务超89万次,覆盖了武汉三家主流运营商的固网宽带、4G和5G用户。基于Speedtest测速平台上的数据,文章搜集并修正了2020年武汉运营商A、运营商B和运营商C的用户测速数据,对比分析了三家运营商固网宽带、4G网络和5G网络的用户体验带宽。