Oscillation detection technique by using Vector Network Analyzer

A Vector Network Analyzer(VNA)can be used to identify oscillation frequency of a signal source with moderate or low Radio Frequency(RF)power if certain care is taken according to experimental results.Unlike reported in the literature that a resonant peak of measured absolute value of reflection coefficient greater than 1 that corresponds to an oscillation frequency,we report that by observing the magnitude change of one-port reflection coefficient across the entire swept frequency range,a sudden peak or a dip corresponds to an oscillation frequency,this is more accurate than other reports.In addition,using modern VNA as a signal detection method can significantly reduce measurement time and increase measurement accuracy to VNA capability for developing emerging signal generating devices at early stage,especially for planar,large quantity and operating in a wide frequency range.

Low-cost portable dielectric spectrometer based on mini-vector network analyzer and open-ended coaxial probe technology

As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of materials.However,the large and expensive vector network analyzers(VNA)with expensive analysis software applied in measuring dielectric properties make research limited to the laboratory.To acquire dielectric spectra in situ,a model for solving relative complex permittivity was derived,and its performance was validated.Then,a low-cost portable dielectric spectrometer with a mini VNA,a Raspberry Pi,and a coaxial probe as core parts was developed over the frequency range of 100-3000 MHz.The stability and accuracy of the developed spectrometer were tested using milk and juice.The results indicated that the relative errors of the model were within±5%for dielectric constant(ε′)and loss factor(ε″).The coefficients of variation of measuredε′andε″by the developed spectrometer at 100-3000 MHz were less than 1%and 2%,respectively.Compared with the dielectric properties obtained by using a commercial dielectric measurement system,the relative errors of measuredε′andε″were within±3.4%and±6.0%,respectively.This study makes fast,non-destructive,and on-site food quality detection using dielectric spectra possible.

Fully Distributed Learning for Deep Random Vector Functional-Link Networks

In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.

Research on Node Classification Based on Joint Weighted Node Vectors

Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.

Memristor-based vector neural network architecture

Vector neural network(VNN)is one of the most important methods to process interval data.However,the VNN,which contains a great number of multiply-accumulate(MAC)operations,often adopts pure numerical calculation method,and thus is difficult to be miniaturized for the embedded applications.In this paper,we propose a memristor based vector-type backpropagation(MVTBP)architecture which utilizes memristive arrays to accelerate the MAC operations of interval data.Owing to the unique brain-like synaptic characteristics of memristive devices,e.g.,small size,low power consumption,and high integration density,the proposed architecture can be implemented with low area and power consumption cost and easily applied to embedded systems.The simulation results indicate that the proposed architecture has better identification performance and noise tolerance.When the device precision is 6 bits and the error deviation level(EDL)is 20%,the proposed architecture can achieve an identification rate,which is about 92%higher than that for interval-value testing sample and 81%higher than that for scalar-value testing sample.

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.

ADAPTIVE PINNING SYNCHRONIZATION OF COUPLED NEURAL NETWORKS WITH MIXED DELAYS AND VECTOR-FORM STOCHASTIC PERTURBATIONS

In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.

Learning Vector Quantization Neural Network Method for Network Intrusion Detection

A new intrusion detection method based on learning vector quantization （LVQ） with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages： （1） feature selection and data normalization processing;（2） learning the training data selected from the feature data set; （3） identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection.

Novel Method of Predicting Network Bandwidth Based on Support Vector Machines

In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

在片S参数计量比对结果浅析

中国电子科技集团公司第十三研究所作为主导实验室开展了在片S参数计量比对工作,对参比实验室提交的在片S参数测量结果进行了汇总分析,并用E_n值对各参比实验室测量结果进行了评价。通过在片S参数计量比对,确保了量值传递的准确、可靠,特别是对在片S参数测量不确定度的主要来源统一了认识。同时也为业内提供了在片S参数测量一致性的比较平台。

基于轴箱垂向振动加速度的地铁车轮失圆状态诊断方法

首先,建立卷积神经网络、深度置信网络、支持向量机和以一维卷积神经网络全连接层特征为输入的支持向量机模型(1DCNN-SVM),对比上述模型在地铁车轮失圆状态分类识别上的效果;其次,利用代理模型构建轴箱垂向加速度均方根与车速和多边形磨耗幅值之间的映射关系;最后,通过智能优化算法逆向求解幅值,对比不同代理模型和智能优化算法在多边形磨耗幅值识别上的适用性。研究结果表明:1DCNN-SVM模型在正常、低阶多边形、高阶多边形、随机非圆车轮4类典型的车轮不圆度状态分类识别中取得99.82%的准确性,相比另外3种分类方法,其泛化性能和强化学习能力都具有明显的优势。在车轮多边形磨耗幅值识别方面,基于克里金模型(KSM)和粒子群算法(PSO)的波深识别模型具有更好的预测稳定性和时效性。

Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network

Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.

矢量网络分析仪硬件性能对测量精度的影响分析

文中通过深入分析矢量网络分析仪S参数测量过程中的误差来源,建立了误差项与硬件性能的对应关系,在此基础上,通过将原始测量值看作误差项的多元函数,在各误差项理想值附近保留泰勒级数线性项作为合理估计,分析了原始S参数测量结果偏差与误差模型中每个误差项之间的关联程度。对其解析式中除原始测量值以外的其他项使用增量法,求解了最终S参数测量结果偏差与各误差项之间的关联程度,由此确定了每个硬件性能引起的最终测量偏差的大小。最后使用Keysight N5227A和Ceyear 3671E两台矢量网络分析仪,在相同的校准条件下对参数已知的待测网络进行测量,通过对比最终测量结果,检验方法的正确性和有效性。

Research on Vector Road Data Matching Method Based on Deep Learning

Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.

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。