Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.

双圈连通图的L(2，1)-labelling(英文)

给定图G,G的一个L(2,1)-labelling是指一个映射f:V(G)→{0,1,2,…},满足:当dG(u,v)=1时,|f(u)-f(v)|≥2;当dG(u,v)=2时,|f(u)-f(v)|≥1。如果G的一个L(2,1)-labelling的像集合中没有元素超过k,则称之为一个k-L(2,1)- labelling.G的L(2,1)-labelling数记作l(G),是指使得G存在k-L(2,1)-labelling的最小整数k.如果G的一个L(2,1)-labelling中的像元素是连续的,则称之为一个no-hole L(2,1)-labelling.本文证明了对每个双圈连通图G,l(G)=△+1或△+2.这个工作推广了[1]中的一个结果.此外,我们还给出了双圈连通图的no-hole L(2,1)-labelling的存在性.

仙人掌图的L(2，1)-标号(英文)

一个图G的L(2,1)-标号是给图G上的顶点分配非负整数标号,使得G上相邻的两个点的标号至少相差2,距离为2的两个点的标号则不同.G的L(2,1)-标号数λ(G)是所有能使图G正常标号的最小标号.如果一个图的任何两个圈不含有公共边,则称这个图为仙人掌图.显然树是它的一个子图类.对于任何树T,有△(T)+1≤λ(T)≤△(T)+2.本文中我们证明了在一些条件下,这个界也适用于仙人掌图.

大鼠耳蜗α_(1D)L-型电压门控钙通道组织特异性异构体的剪切方式及其意义

目的 研究α1DL 型电压门控钙通道基因在大鼠耳蜗的剪切 (splicing )方式及其意义。 方法 以显微解剖取材的大鼠耳蜗基底膜为起始材料 ,利用外显子特异性 (exon specific)引物的RT PCR扩增和序列测定确定耳蜗表达的α1DL 型电压门控钙通道的剪切方式。结果 耳蜗表达的α1D钙通道cDNA剪切部位发生在功能域Ⅰ、Ⅱ之间的细胞内连接区和羧基末端。结论 大鼠耳蜗存在α1D钙通道组织特异性的剪切异构体 。

Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.

Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks

The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.

A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing research methods show poor performance.AQN3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the performance.The proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)phases.In DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered path.Next,it is tested with the trained IDS.The IDS encompasses‘3’steps:pre-processing,matrix reduction,and classification.In pre-processing,the data is organized in a clear format.Then,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix values.Next,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal data.In an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing algorithms.The proposed work attains an enhanced outcome than the prevailing methods.

LMI Approach to Observer-based FD Systems Designing

Increasing the robustness to the unknown uncertainty and simultaneously enhancing the sensibility to the faults is one of the important issues considered in the fault detection development. Considering the L2-gain of residual system, this paper deals the observer-based fault detection problem. By using of H∞ control theory,an LMI approach to design fault detection observer is given. A numerical example is used to illustrate the effectiveness of the proposed approach.

CONVERGENCE ANALYSIS OF MIXED VOLUME ELEMENT-CHARACTERISTIC MIXED VOLUME ELEMENT FOR THREE-DIMENSIONAL CHEMICAL OIL-RECOVERY SEEPAGE COUPLED PROBLEM

The physical model is described by a seepage coupled system for simulating numerically three-dimensional chemical oil recovery, whose mathematical description includes three equations to interpret main concepts. The pressure equation is a nonlinear parabolic equation, the concentration is defined by a convection-diffusion equation and the saturations of different components are stated by nonlinear convection-diffusion equations. The transport pressure appears in the concentration equation and saturation equations in the form of Darcy velocity, and controls their processes. The flow equation is solved by the conservative mixed volume element and the accuracy is improved one order for approximating Darcy velocity. The method of characteristic mixed volume element is applied to solve the concentration, where the diffusion is discretized by a mixed volume element method and the convection is treated by the method of characteristics. The characteristics can confirm strong computational stability at sharp fronts and it can avoid numerical dispersion and nonphysical oscillation. The scheme can adopt a large step while its numerical results have small time-truncation error and high order of accuracy. The mixed volume element method has the law of conservation on every element for the diffusion and it can obtain numerical solutions of the concentration and adjoint vectors. It is most important in numerical simulation to ensure the physical conservative nature. The saturation different components are obtained by the method of characteristic fractional step difference. The computational work is shortened greatly by decomposing a three-dimensional problem into three successive one-dimensional problems and it is completed easily by using the algorithm of speedup. Using the theory and technique of a priori estimates of differential equations, we derive an optimal second order estimates in 12 norm. Numerical examples are given to show the effectiveness and practicability and the method is testified as a powerful tool to solve the important problems.

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

最大度为3的树的L(2,1)-标号数的一个刻画

图G的一个L(2,1)-标号是对G顶点集合的一个非负整数分配,使得其中相邻的点取得的整数差值至少为2并且距离为2的点取得不同的整数.L(2,1)-标号数就是所有这样的标号分配中最小的标号跨度值.Griggs和Yeh的[Labelling graphs with a condition at distance 2,SIAM J.Discrete Math.,1992,5:586-595]已经证明了,一棵树的L(2,1)-标号数不是△就是△+1.对于最大度为3的树的L(2,1)-标号数,本文给出了一个完全的刻画.

一种基于自适应加权的鲁棒联邦学习算法

联邦学习(Federated Learning,FL)允许多个数据所有者联合训练机器学习模型,而无需他们共享私有训练数据。然而,研究表明,FL容易同时遭受拜占庭攻击和隐私泄露威胁,现有的研究都没有很好地解决这一问题。在联邦学习场景中,保护FL免受拜占庭攻击,同时考虑性能、效率、隐私、攻击者数量、简单可行等问题,是一个极具挑战性的问题。为解决这一问题,基于l 2范数和两次归一化方法提出了一种隐私保护鲁棒联邦学习算法DP-FedAWA。提出的算法不需要训练过程之外的任何假设,并且可以自适应地处理少量和大量的攻击者。无防御设置下选用DP-FedAvg作为比较基线,防御设置下选用Krum和Median作为比较基线。MedMNIST2D数据集上的广泛实验证实了,DP-FedAWA算法是安全的,对恶意客户端具有很好的鲁棒性,在Accuracy,Precision,Recall和F1-Score等性能指标上全面优于现有的Krum和Median算法。

加权距离下模糊数的区间逼近

在加权L2距离意义下得到了模糊数的最近区间逼近。基于此,引入了最近区间逼近算子,并讨论了这个算子的基本性质,证明了算子关于加权L2距离Lipschitz连续,其Lipschitz常数为1。

A Note on L(2,1)-labelling of Trees

An L（2, 1）-labelling of a graph G is a function from the vertex set V（G） to the set of all nonnegative integers such that │f（u） - f（v）│≥2 if dG（u, v） = 1 and │f（u） - f（v）│ ≥ 1 if dG（u, v） = 2. The L（2, 1）-labelling problem is to find the smallest number, denoted by A（G）, such that there exists an L（2, 1）-labelling function with no label greater than it. In this paper, we study this problem for trees. Our results improve the result of Wang [The L（2, 1）-labelling of trees, Discrete Appl. Math. 154 （2006） 598-603].

Weighted L^2-Estimates of Solutions for Damped Wave Equations with Variable Coefficients

The authors establish weighted L^2-estimates of solutions for the damped wave equations with variable coefficients utt-div A(x)▽u + au_t = 0 in IR^nunder the assumption a(x) ≥ a_0[1 + ρ(x)]^(-l),where a_0 > 0, l < 1, ρ(x) is the distance function of the metric g = A^(-1)(x) on IR^n. The authors show that these weighted L^2-estimates are closely related to the geometrical properties of the metric g = A^(-1)(x).

基于K-means对马赛克瓷砖选色问题的研究

本文采用K-means聚类法构建模型对马赛克瓷砖选色问题进行研究,并用色差法对两个样例图片进行表现力效果的量化,参照CIE标准,显示该模型较稳定。通过综合考虑开发成本和表现力效果,本文还采用K-means算法筛选瓷砖颜色备用集,基于色差最小进行搜索后给出了瓷砖颜色的增补方案。

Angel estimation via frequency diversity of the SIAR radar based on Bayesian theory

The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency diversity process.As using the traditional Fourier transform will result in the target spectral with large sidelobe,the method presented in this paper firstly makes the preordering treatment for the position of the received antenna.Then,the Bayesian maximum posteriori estimation with l2-norm weighted constraint is utilized to achieve the equivalent uniform array echo.The simulations present the spectrum estimation in angle precision estimation of multiple targets under different SNRs,different virtual antenna numbers and different elevations.The estimation results confirm the advantage of SIAR radar both in array expansion and angle estimation.

Sum of squares methods for minimizing polynomial forms over spheres and hypersurfaces

Abstract This paper studies the problem of minimizing a homogeneous polynomial （form） f（x） over the unit sphere Sn-1 = {x ∈ R^n： ｜｜X｜｜2 = 1}. The problem is NP-hard when f（x） has degree 3 or higher. Denote by fmin （resp. fmax） the minimum （resp. maximum） value of f（x） on S^n-1. First, when f（x） is an even form of degree 2d, we study the standard sum of squares （SOS） relaxation for finding a lower bound of the minimum .fmin ：max γ s.t.f（x）-γ.｜｜x｜｜2^2d is SOS.Let fos be be the above optimal value. Then we show that for all n ≥ 2d,Here, the constant C（d） is independent of n. Second, when f（x） is a multi-form and ^-1 becomes a muilti-unit sphere, we generalize the above SOS relaxation and prove a similar bound. Third, when f（x） is sparse, we prove an improved bound depending on its sparsity pattern; when f（x） is odd, we formulate the problem equivalently as minimizing a certain even form, and prove a similar bound. Last, for minimizing f（x） over a hypersurface H（g） = {x E lRn： g（x） = 1} defined by a positive definite form g（x）, we generalize the above SOS relaxation and prove a similar bound.

On the k-sample Behrens-Fisher problem for high-dimensional data

For several decades, much attention has been paid to the two-sample Behrens-Fisher (BF) problem which tests the equality of the means or mean vectors of two normal populations with unequal variance/covariance structures. Little work, however, has been done for the k-sample BF problem for high dimensional data which tests the equality of the mean vectors of several high-dimensional normal populations with unequal covariance structures. In this paper we study this challenging problem via extending the famous Scheffe’s transformation method, which reduces the k-sample BF problem to a one-sample problem. The induced one-sample problem can be easily tested by the classical Hotelling’s T 2 test when the size of the resulting sample is very large relative to its dimensionality. For high dimensional data, however, the dimensionality of the resulting sample is often very large, and even much larger than its sample size, which makes the classical Hotelling’s T 2 test not powerful or not even well defined. To overcome this difficulty, we propose and study an L 2-norm based test. The asymptotic powers of the proposed L 2-norm based test and Hotelling’s T 2 test are derived and theoretically compared. Methods for implementing the L 2-norm based test are described. Simulation studies are conducted to compare the L 2-norm based test and Hotelling’s T 2 test when the latter can be well defined, and to compare the proposed implementation methods for the L 2-norm based test otherwise. The methodologies are motivated and illustrated by a real data example.