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.

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

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.

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.

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.

Some Convergence Properties for Weighted Sums of Martingale Difference Random Vectors

Let{X_(ni),F_(ni);1≤i≤n,n≥1}be an array of R^(d)martingale difference random vectors and{A_(ni),1≤i≤n,n≥1}be an array of m×d matrices of real numbers.In this paper,the Marcinkiewicz-Zygmund type weak law of large numbers for maximal weighted sums of martingale difference random vectors is obtained with not necessarily finite p-th(1<p<2)moments.Moreover,the complete convergence and strong law of large numbers are established under some mild conditions.An application to multivariate simple linear regression model is also provided.

V-BILIPSCHITZ DETERMINACY OF WEIGHTED HOMOGENEOUS ANALYTIC FUNCTION-GERMS ON WEIGHTED HOMOGENEOUS REAL ANALYTIC VARIETIES

In this article, we provide estimates for the degree of V bilipschitz determinacy of weighted homogeneous function germs defined on weighted homogeneous analytic variety V satisfying a convenient Lojasiewicz condition.The result gives an explicit order such that the geometrical structure of a weighted homogeneous polynomial function germs is preserved after higher order perturbations.

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.

WEIGHTED COMPOSITION OPERATORS BETWEEN DIRICHLET SPACES

In this article, we study the boundedness of weighted composition operators between different vector-valued Dirichlet spaces. Some sufficient and necessary conditions for such operators to be bounded are obtained exactly, which are different completely from the scalar-valued case. As applications, we show that these vector-valued Dirichlet spaces are different counterparts of the classical scalar-valued Dirichlet space and characterize the boundedness of multiplication operators between these different spaces.

Simple Bounded Weight Modules for the Vector Field Lie Algebras of Infinite Rank

In this paper,we classify the simple uniformly bounded weight modules for the vector eld Lie algebra W1 of in nite rank.It turns out that any such modules are intermediate series modules.This result is very di erent from the vector eld Lie algebra Wd of nite rank.

基于ICEEMDAN和时变权重集成预测模型的变压器油中溶解气体含量预测

为了实现对变压器油中溶解气体体积分数的精确预测,同时克服仅使用单一预测模型导致预测精度及泛化能力不足的局限,提出了一种基于改进完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition,ICEEMDAN)和灰色关联系数时变权重集成预测模型的变压器油中溶解气体预测方法。首先将溶解气体含量序列模态分解为一系列具有不同时间尺度的子序列。然后,使用门控循环神经网络和麻雀搜索算法优化支持向量机对各子序列进行训练,组合为一个集成预测模型;并比较不同预测方法的预测精度,计算灰色关联系数时变权重,形成各子系列的预测结果。最后将各子序列的预测结果叠加重构,得到最终预测结果。算例分析结果显示:该方法单步预测的均方根误差、平均绝对误差和相关系数分别为0.593、0.422和0.768,相比其他算法在预测精度上有明显提升,同时具有很强的泛化性能,可以为油浸式变压器内部状态监测提供依据。

基于非欧几何权向量产生策略的分解多目标优化算法

随着目标数量的增加,多目标优化问题(Multi Objective Problems,MOPs)的求解越来越困难。基于分解的多目标进化算法表现出更好的性能,但在求解具有复杂Pareto前沿的MOPs时,此类算法易出现种群多样性不足、算法性能下降等问题。为了解决这些问题,提出了一种基于非欧几何权向量产生策略的分解多目标优化算法,通过在非欧几何空间中拟合非支配前沿并进行参数估计,再利用对非支配解目标变量的正态统计采样生成权向量,以此引导种群的进化方向并保持种群的多样性。同时在非欧几何空间中周期性重新确定子问题的邻域,提高分解算法协同进化的效率,进而提高算法的性能。基于MaF基准测试函数的实验结果表明,相比MOEA/D,NSGA-Ⅲ和AR-MOEA算法,所提算法在求解多目标和众目标优化问题方面具有明显的优势。

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。

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。

融合多特征信息与GWO-SVM的机械关键设备故障诊断

为了提高机械关键设备故障诊断的精度,建立机械关键设备故障诊断模型。文章提出一种融合机械关键设备故障信号多特征信息与灰狼优化算法(Grey Wolf Optimization Algorithm,GWO)改进支持向量机(Support Vector Machine,SVM)(GWO-SVM)的机械关键设备故障诊断模型。首先,提取机械关键设备故障信号的时域特征、频域特征和多尺度加权排列熵特征,分别对比不同特征的机械关键设备故障诊断结果。其次,为提高SVM模型性能,运用GWO算法对SVM模型的惩罚参数P和核函数参数g进行优化选择,提出一种融合多特征信息与GWO-SVM的机械设备故障诊断模型。与GA-SVM、PSO-SVM和SVM相比,基于GWO-SVM的机械设备故障诊断模型的诊断精度最高。这里算法可以有效提高机械关键设备故障诊断正确率,为机械关键设备故障诊断提供了新的方法。

基于卡尔曼滤波算法的电池状态估计

为更好地获得锂离子电池荷电状态SOC(state-of-charge)估计值,选用二阶等效电路模型作为研究对象,针对带有遗忘因子的递推最小二乘法在参数辨识中易受到噪声等环境因素干扰的缺点,提出偏差补偿最小二乘法来实现模型参数的准确辨识,并结合无迹卡尔曼滤波算法对SOC进行估计。针对无迹卡尔曼滤波算法稳定性差等缺点,提出利用权重向量更新滤波算法中的卡尔曼滤波增益。实验结果表明,所提算法估计SOC的总误差可控制在2.7%以内,验证了算法的鲁棒性和有效性。

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。

纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析

背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(P<0.05)。④富集分析结果提示,纤维肌痛综合征的富集途径包括9条,主要与嗅觉传导、神经活性配体-受体相互作用及感染等通路密切相关。⑤上述结果显示,纤维肌痛综合征的发生发展与多基因参与、免疫调节异常及多个通路失调有关,但这些基因与免疫细胞之间的相互作用,以及它们与各通路之间的关系尚需进一步研究。

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。

基于变权组合模型的碳排放量预测

碳排放量的预测一直是国内外人们关注的热点,为了进一步提高碳排放量预测模型的准确性,考虑多种因素对碳排放量的影响,利用支持向量机回归、岭回归和BP神经网络三种传统单项碳排放量预测模型,结合误差倒数法构建了一种变权组合模型,并利用新模型预测我国2022—2026年的碳排放量。实证结果显示,组合模型的拟合精度和预测精度分别为99.26%和99.34%,组合模型对比3种单项模型有更高的精度。组合模型的预测结果显示,到2026年,我国碳排放增速较现在有所放缓,以1.8%的速度保持增长。