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.

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.

Neurocomputing van der Pauw function for the measurement of a semiconductor's resistivity without use of the learning rate of weight vector regulation

Van der Pauw＇s function is often used in the measurement of a semiconductor＇s resistivity. However, it is difficult to obtain its value from voltage measurements because it has an implicit form. If it can be expressed as a polynomial, a semiconductor＇s resistivity can be obtained from such measurements. Normally, five orders of the abscissa can provide sufficient precision during the expression of any non-linear function. Therefore, the key is to determine the coefficients of the polynomial. By taking five coefficients as weights to construct a neuronetwork, neurocomputing has been used to solve this problem. Finally, the polynomial expression for van der Pauw＇s function is obtained.

STABILITY OF CERTAIN RINGS OF HIGHEST WEIGHT VECTORS

The authors prove the stability of the rings of highest weight vectors of the action of Om x GLn on the complex polynomial rings on Cm,n. As an application, the structure of the rings for m = 3 is determined.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

A∞ weight estimates for vector-valued commutators of multilinear fractional integral

In this paper, the authors get the Coifman type weighted estimates and weak weighted LlogL estimates for vector-valued generalized commutators of multilinear fractional integral with w ∈ A∞. Furthermore, both the boundedness of vector-valued multilinear frac- tional integral and the weak weighted LlogL estimates for vector-valued multilinear fractional integral are also obtained.

Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

基于镜像修正FxLMS控制算法的船舶管路振动主动控制

针对船舶管路减振和抗冲击的需求,本文根据镜像修正自适应滤波算法,设计出了一种管路振动主动控制策略,能够有效地控制管路在低频下的振动,并且在次级通道发生突变时,控制系统可再次快速收敛,进行稳定控制。本文先对镜像修正自适应滤波算法进行理论研究,分析算法的迭代及控制过程;再通过仿真分别验证算法在不同参考信号输入下的收敛性及稳定性;最后搭建实验台架,通过试验验证算法的实际控制效果。试验结果表明:该控制策略在管路振动主动控制中能够降低15.37%的振动强度,比自适应滤波算法控制策略的控制效果好8.85%。所以镜像修正自适应滤波算法能够及时有效地进行管路振动控制。

基于改进INFO-CNN-QRGRU模型的农村分布式光伏发电短期概率预测

随着“双碳”目标的推进,清洁能源所占比重大幅度增加,分布式光伏发电在我国农村地区快速发展,但其随机性、间歇性的特点给新能源消纳和电网稳定带来很大的挑战。光伏发电预测可以在一定程度上改善新能源消纳问题,减少光伏发电的不稳定性对电网的冲击。因此,为提高光伏发电功率预测精度,提出一种基于改进向量加权平均算法优化CNN-QRGRU网络的光伏发电概率预测方法。首先采用ReliefF算法对特征变量进行选择,在此基础上利用高斯混合模型(Gaussian mixture model,GMM)聚类方法将天气分为晴天、晴转多云和阴雨天3种类型,将处理好的数据输入到CNN-GRU模型中,并利用向量加权平均(weighted mean of vectors algorithm,INFO)优化算法对模型超参数进行调参,将分位数回归模型(quantile regression,QR)与INFO-CNN-GRU模型相结合得到光伏功率条件分布,结合核密度估计法从条件分布中获得概率密度函数,完成概率预测。以实际光伏电站数据作为基础,将提出的INFO优化算法与其他几种传统的优化算法进行对比,结果表明INFO的优化效果更好,在此基础上进行概率预测,得到的概率预测结果相较于点预测能提供更多有效信息,更具有应用价值。

面向高维多目标优化的双阶段双种群进化算法

随着目标维度的上升,高维多目标优化问题的帕累托前沿越来越复杂,传统的基于分解的高维多目标进化算法难以挑选出多样性和收敛性良好的种群。针对以上问题提出了一种面向高维多目标优化的双阶段双种群进化算法。该算法将进化过程划分为两个阶段,在第一阶段判断帕累托前沿的形状是否规则,而在第二阶段则根据帕累前沿的形状选择是否对权重向量进行调整,以保证种群在规则及不规则帕累托前沿上都能获得良好的多样性。为了对权重向量进行调整且不影响算法的收敛性,该算法使用了两个种群进行进化,一个主种群正常进化,另一个辅种群作为权重向量。为了在不规则的帕累托前沿上获得一组适应种群分布的权重向量,引入了自然界中能量平衡的概念收集了多样性良好的辅种群作为权重向量。将提出的算法与其他算法在3-10目标的测试问题上进行比较。实验结果表明,提出的算法在大多数测试问题上性能优于比较的算法。

边加权有限图的Weil-Riemann-Roch定理

Riemann-Roch定理是数学中的一个重要结论,并有了广泛的应用。在有限图和边加权有限图等图中也有对应的Riemann-Roch定理以及应用,但所有这些工作都有一个共同点,那就是它们都聚焦于在除子或和除子线性等价的线丛的情况下,也就是秩为1的情况。为了得到高维秩的情形,可以借助多重除子的术语来描述。本文利用还原群GLn的root datum的概念给出了边加权有限图上主GLn-丛——向量丛的定义,并用多重除子的术语来描述向量丛,进而给出了边加权有限图的Weil-Riemann-Roch定理以及证明,推广了GROSS A.ULIRSCH M.和ZAKHAROV D的结果。

基于特征判定系数的电力变压器振动信号故障诊断

变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义。针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法。通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断。对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率。

多模式低副瓣阵列方向图综合方法

本文面向阵列天线低副瓣的实际应用需求,利用空时编码的思路设计加权矢量,分别提出序贯快速傅里叶变换(fast Fourier transform,FFT)方法、约束方程计算方法和优化函数求解方法,对应多脉冲低副瓣方向图综合时加权矢量时变、不变和奇偶交替变的3种不同工作模式。理论分析和仿真实验表明,序贯FFT和约束方程计算方法均能实现超低副瓣方向图(优于-50 dB),而优化函数求解方法在实现低副瓣的同时仍能保持方向图主瓣的良好性能,避免了传统方向图综合方法无法同时兼顾主副瓣性能的缺点。另外,从计算复杂度、随机幅相误差、干扰抑制等各方面综合分析了不同方法的优缺点,可为实际阵列天线的工程应用提供理论指导和技术参考。

基于外部存档更新及截断的NSGA-Ⅱ改进算法

传统的NSGA-Ⅱ(Non-dominated Sorting Genetic AlgorithmⅡ)算法使用拥挤度作为精英选择的第二指标,该方法在处理高维多目标优化问题时,常常由于选择压力不足,以及不同目标间优化冲突加剧等原因,很难维持种群收敛性和多样性的平衡。针对上述问题,提出一种基于外部存档更新及截断机制的NSGA-Ⅱ改进算法NSGA-Ⅱ-UTEA(NSGA-Ⅱalgorithm based on Update and Truncation of External Archive)。该算法首先在精英选择中引入基于权重向量分解的外部存档机制,然后根据个体与所在权重向量及超平面距离之和更新外部存档,并基于个体间角度计算实现外部存档截断,进一步提升了算法在高维多目标优化问题中种群的收敛性和多样性。与NSGA-Ⅱ、NSGA-Ⅲ、MOEA/D(Multi-Objective Evolutionary Algorithm based on Decomposition)、NSGA-Ⅱ-ARSBX(NSGA-Ⅱwith Adaptive Rotation based Simulated Binary crossover)和RPD-NSGA-Ⅱ(Reference Point Dominance-based NSGA-Ⅱ)这5种先进的进化算法的对比实验结果表明,NSGA-Ⅱ-UTEA算法在10目标以上的高维DTLZ(Deb Thiele Laumanns Zitzler)和WFG(Walking Fish Group)系列测试函数上,各项性能指标整体优于其他算法,在解集的分布性和多样性方面具有显著优势。特别是在大部分高维WFG4~WFG7凹问题上都能取得最佳的性能指标值。与传统的NSGA-Ⅱ算法相比,NSGA-Ⅱ-UTEA算法在10目标以上的高维DTLZ系列测试函数上,反世代距离(IGD)性能平均提升了50.6%;在15目标以上的高维WFG系列测试函数上,超体积(HV)性能平均提升了60.7%。实验结果验证了NSGA-Ⅱ-UTEA算法改进的有效性。

基因表达式编程算法改进及在社会效益综合评价中的应用:以赣南离子型稀土资源开发为例

以赣南离子型稀土资源开发为例,探索社会效益综合评价模型构建方法,为矿产资源开发提供参考。从国家资源安全、社会经济、人民生活、生态环境四个方面进行分析,构建赣南离子型稀土矿产资源开发社会效益综合评价指标体系。考虑到基因表达式编程(GEP)算法在拟合函数上有良好成效,可以反映指标之间的函数关系,因此,提出利用GEP算法挖掘各个指标之间的函数关系来构建稀土矿产资源开发社会效益综合评价模型,并提出基于变化率的量化方法求解各指标权重来客观评价赣南离子型稀土资源开发的社会效益。传统GEP算法在此案例上函数拟合效果不够理想,平均相对误差较大,因此,基于传统GEP算法的变异算子及IS转座算子进行改进来增加算法勘探能力,并在算法进化长期停滞时引入“择新”产生子代来增加种群多样性。实验证明改进后的算法更易跳出局部最优且更快收敛,能够获得较好的函数拟合效果,减小实验误差。利用自变量变化率,结合改进GEP得到的函数关系式,求解综合评价指标体系的权重。其中,国家资源安全保障程度、对社会经济的影响、对人民生活的影响、对生态环境的影响的权重依次为0.084、0.243、0.313、0.360。研究结论可为决策者提供参考,以便对赣南离子型稀土资源开发进行合理规划。

SLAM精度的向量加权平均自适应调节研究

针对粒子滤波算法需要大量粒子以提高精度以及重采样导致的粒子多样性缺失的问题,提出一种自调INFO(向量加权平均算法)优化的粒子重组粒子滤波算法。首先,通过向量不同的加权平均规则,使得最优粒子引导粒子集向期望区域移动,以此提高估计精度;其次,实时计算最优粒子附近的粒子密度,当密度大于阈值时,自适应调整迭代次数,实时监测粒子密度,根据此指标引入次优粒子的作用自适应调整粒子集分布;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。通过仿真实验检验改进算法在SLAM中的性能,结果表明该算法较标准算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

一种自适应调整权重向量的多目标进化算法

基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)作为一种重要的多目标优化方法,已经成功地应用于解决各种多目标优化问题。然而,MOEA/D算法在解决具有高维目标和复杂帕累托前沿(Pareto frontier,PF)的问题时,容易陷入局部最优并难以获得可行解。本文提出一种改进的MOEA/D算法,包括3个优化策略:首先,使用拉丁超立方抽样方法代替随机方法初始化种群,得到分布均匀的初始种群,同时对权重向量关联解的策略进行优化;其次,提出一种稀疏度函数,用于计算种群中个体的稀疏度并维护外部种群;最后,提出了自适应调整权向量的方法,用于引导种群收敛到帕累托前沿,并且有效平衡种群的多样性和收敛性。将提出算法和4种对比算法在DTLZ和WFG系列问题以及多目标旅行商问题(multi-objective travel salesman problem,MOTSP)上进行对比实验,实验结果表明本文提出自适应调整权重向量的多目标进化(MOEA/D with cosine similarity adaptive weight adjustment,MOEA/D-CSAW)算法在处理具有复杂帕累托前沿和高维多目标的问题时,算法的综合性能要优于对比算法。

基于特征加权混合隶属度的模糊孪生支持向量机

模糊孪生支持向量机(FTSVM)忽略了不同特征间的差异,导致核函数或距离的计算无法准确反映样本间的相似性,使FTSVM在处理含有大量不相关或弱相关特征的高维数据分类时,难以达到良好分类效果;且隶属度的设计未有效区分离群点或噪声。针对以上问题,提出了一种基于特征加权混合隶属度的FM-FTSVM。首先计算每个特征的信息增益,并依据信息增益值的大小为特征赋予权重,降低不相关或弱相关特征的作用,使其能更好地应用于高维数据分类;然后,为每一类样本构造一个最小包围球计算基于紧密度的特征加权隶属度,并结合基于距离的特征加权隶属度得到特征加权混合隶属度,综合考虑样本点到类中心的特征加权欧式距离和样本间的紧密程度,可更好识别离群点或噪声数据;最后,融合特征加权核函数,降低不相关特征对核函数或距离计算产生的影响。与对比算法在人工数据集、高维数据集和UCI数据集上进行比较,发现本文提出的方法在区分离群点、噪声和有效样本上有明显优势,且在高维数据集上可获得更好分类效果。

基于weight-pooling词向量的上下文广告推荐算法

提出一种基于weight-pooling词向量的上下文广告推荐算法,利用用户访问记录的互信息权重,计算weight-pooling词向量的余弦相似度。该算法改进了传统基于关键词匹配的推荐算法,避免了数据稀疏性和冷启动问题。通过实验分析,基于weightpooling词向量的上下文广告推荐算法在推荐效果上明显优于传统算法。

薄盘工件加工过程中的不平衡量分析及去重方法研究

薄盘工件是转子机构上常见的零/部件,在装配调试前通常需要进行动平衡测试与校正。目前常用的不平衡校正方法存在加工与校正分离、平衡工艺复杂,以及效率低等问题。基于该问题提出在薄盘工件加工过程中不平衡量校正的方法,结合复合数控机床的主轴准停、刀具定位及去重控制等功能,进行薄盘工件不平衡量在线去重校正。同时,为了提高薄盘工件不平衡量在线去重校正精度,提出一种附加去重率的迭代控制方法,并从理论上证明了该方法的稳定性。通过仿真计算讨论了附加去重率的迭代控制方法优势,探讨了理想去重率的选取问题。研究内容可以在无动平衡机的情况下实现薄盘工件不平衡量在线去重校正,对创新性解决薄片工件平衡成本高、效率低等问题以及提高工件加工精度等方面具有重要的意义。