融合注意力机制的残差网络晶体硅片分类方法

为了更好地利用晶体硅片资源,实现对晶体硅片准确高效的分类,提出了一种改进的ResNet34卷积神经网络,且用于对晶体硅片高清图像进行分类.通过拍摄晶体硅片高清图像建立自有数据集,并对其进行离线扩充来有效扩大数据集.基于ResNet34网络建立分类模型,采取自适应矩估计权重衰减优化算法(AdamW)来提高ResNet34网络的泛化能力,同时将注意力机制的方法融入到ResNet34网络中增强模型的特征提取能力,之后将改进的模型载入到晶体硅片数据集上训练,实验结果发现,所提W-ResNet34+SC-SEAM分类模型的准确率可达99.91%,比在仅利用ResNet34模型分类结果上提高了2.68%的准确率,实现了对晶体硅片的精确分类,证明了所提分类方法是可行的.     

An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks北大核心CSCD

心电信号分类是医疗保健领域的重要研究内容.针对大多数方法不能很好地降低样本数量少的类别漏诊率,以及降低预处理操作的复杂性问题,提出了一种基于改进深度残差收缩网络(IDRSN)的心电信号分类算法(即DRSL算法).首先,使用合成少数类过采样技术(SMOTE)扩充数量少的类别样本,从而解决了类不平衡问题;其次,利用改进深度残差收缩网络提取空间特征,其残差模块可以避免网络层加深造成的过拟合,压缩激励和软阈值化子网络可以提取重要局部特征并自动去除噪声;然后,通过长短期记忆网络(LSTM)提取时间特征;最后,利用全连接网络输出分类结果.在MIT-BIH心律失常数据集上的实验结果表明,该算法的分类性能优于IDRSN、DRSN、GAN+2DCNN、CNN+LSTM_ATTENTION、SE-CNN-LSTM分类算法.     

基于ARIMA与神经网络集成的GDP时间序列预测研究

本文深入分析了单整自回归移动平均(ARIMA)模型与神经网络(NN)模型的预测特性和优劣,并在此基础上建立了由ARIMA模型和NN模型集成的GDP时间序列预测模型与算法。其基本思想是充分发挥两种模型在线性空间和非线性空间的预测优势,据此将GDP时间序列的数据结构分解为线性自相关主体和非线性残差两部分,首先用ARIMA模型预测序列的线性主体,然后用NN模型对其非线性残差进行估计,最终集成为整个序列的预测结果。仿真实验表明:集成模型的预测准确率显著高于单一模型的预测准确率,从而证实了集成模型用于GDP预测的有效性。     

基于上凸序列的GM(1,1)模型及其优化

针对GM(1,1)模型对上凸序列建模时会出现误差较大的情况进行了研究.首先分析了GM(1,1)对上凸序列建模时的残差变化规律,然后通过分析得出了残差变化规律的精确描述,同时证明了残差序列的几个性质定理.基于残差序列的性质定理提出了基于上凸序列建模的残差修正GM(1,1)模型.将新模型与多种改进的GM(1,1)模型进行对比,实证结果表明新模型具有很高的模拟预测精度,并且适用于一切上凸序列的建模.     

混合区间多尺度分解的区间时间序列组合预测

鉴于传统预测方法一直基于“点”来衡量时间序列数据,然而现实生活中在给定的时间段内许多变量是有区间限制的,点值预测会损失波动性信息。因此,本文提出了一种基于混合区间多尺度分解的组合预测方法。首先,建立区间离散小波分解方法(IDWT)、区间经验模态分解方法(IEMD)和区间奇异普分析方法(ISSA)。其次,用本文构建的IDWT、IEMD和ISSA对区间时间序列进行多尺度分解,从而得到区间趋势序列和残差序列。然后,用霍尔特指数平滑方法(Holt's)、支持向量回归(SVR)和BP神经网络对区间趋势序列和残差序列进行组合预测得到三种分解方法下的区间时间序列预测值。最后,用BP神经网络对各预测结果进行集成得到区间时间序列最终预测值。同时,为证明模型的有效性进行了AQI空气质量的实证预测分析,结果表明,本文所提出基于混合区间多尺度分解的组合预测方法具有较高的预测精度和良好的适用性。     

基于混合高斯过程模型的高光谱图像分类算法

提出了一种基于混合高斯过程模型的高光谱遥感图像分类算法,它不同于传统的基于多元统计的分类方法.为更好利用高光谱遥感图像的高谱分辨率特点,首先将函数数据分析的思想引进高光谱数据的分类问题,分类对象视为像元对应的谱线,故它们是函数型数据.为了有效模拟地物在空间上的分片聚集特性,则将混合高斯分布模型推广到混合高斯过程模型并用于高光谱数据分类算法中.数值实验表明,混合高斯过程模型是处理函数型数据的有效方法.     

基于CEEMD与GA-SVR的猪肉价格集成预测模型

为提高猪肉价格预测的准确性,结合互补集合经验模态分解(CEEMD)的分解能力和基于遗传算法的支持向量回归(GA-SVR)的自适应预测功能,构建猪肉价格集成预测模型.首先为解决猪肉价格的复杂波动特征,通过CEEMD对猪肉价格分解得到本征模态函数(IMF)序列集;然后使用排序熵(PE)对IMF序列进行复杂度分析,进一步使用快速傅里叶变换方法(FFT)分解复杂度高的序列;再利用灰色关联度(GCD)对IMF序列集进行关联性分析,聚合相似IMF序列;最后基于各IMF序列的数据特征构建相应的GA-SVR预测模型,并将子序列的预测结果集成获得最终价格预测值.以中国集贸市场的猪肉价格为研究对象,实证结果表明,该集成预测模型在预测精度和方向性指标上,显著优于其他单预测模型和分解集成预测模型.     

神经网络在复杂自相关预测过程中的应用及对比研究

由于PM_(2.5)日均浓度值受外界多重复杂因素的影响,其较强的自相关性使得时间序列模型ARIMA构建难以实现,因此,给出高映射能力的非线性神经网络预测模型,并分别建立基于BP神经网络和GRNN神经网络的预测模型,进行PM_(2.5)浓度预测实验.结果表明,BP神经网络回检过程和检测过程存在不稳定性,预测残差波动较大,而GRNN神经网络检测残差呈完全U型,回检过程和检测过程较稳定,并且GRNN神经网络回检数据拟合度、预测数据精度和运算速度均优于BP神经网络,建模过程更为方便,易于实际应用.     

基于ICA的时间序列聚类方法及其股票数据分析中的应用

时间序列聚类分析是时间序列数据挖掘中的重要任务之一,通常由于时间序列数据的特殊结构,导致一般的聚类算法不能直接应用于时间序列数据.本文提出了一种基于独立成分分析与改进K-均值算法相结合的时间序列聚类算法,该算法首先利用独立成分分析对时间序列数据进行特征提取,然后利用改进K-均值聚类算法完成对时间序列特征数据的聚类分析,从而得到了一种新的基于特征的时间序列聚类方法.为了验证该方法的有效性和可行性,将其应用于实际的股票时间序列数据聚类分析中,取得了较好的数值结果.     

小邻域方法对数据的分类

为了由数据特征实施对数据更好的查询、图像更好的分割,提出了一种小邻域分类(ε-NC)方法.ε-NC能找出最佳的分类数量,分类时间明显减少.引入相对熵损失来限制样本图像的失真,采用可变步长搜索方法提高最小采样率搜索过程的效率;给出模糊隶属度,形成分类的有效性判断函数,减少迭代次数.采用ε-NC分类方法,对图像分割的准确率高、速度快、抗噪能力强,它对图像目标分类的准确率比传统最好的分类方法的准确率平均提高了5.65%,搜索速度提高9-12倍.     

The polyharmonic local sine transform: A new tool for local image analysis and synthesis without edge effect

We introduce a new local sine transform that can completely localize image information both in the space domain and in the spatial frequency domain. This transform, which we shall call the polyharmonic local sine transform (PHLST), first segments an image into local pieces using the characteristic functions, then decomposes each piece into two components: the polyharmonic component and the residual. The polyharmonic component is obtained by solving the elliptic boundary value problem associated with the so-called polyharmonic equation (e.g., Laplace's equation, biharmonic equation, etc.) given the boundary values (the pixel values along the boundary created by the characteristic function). Subsequently this component is subtracted from the original local piece to obtain the residual. Since the boundary values of the residual vanish, its Fourier sine series expansion has quickly decaying coefficients. Consequently, PHLST can distinguish intrinsic singularities in the data from the artificial discontinuities created by the local windowing. Combining this ability with the quickly decaying coefficients of the residuals, PHLST is also effective for image approximation, which we demonstrate using both synthetic and real images. In addition, we introduce the polyharmonic local Fourier transform (PHLFT) by replacing the Fourier sine series above by the complex Fourier series. With a slight sacrifice of the decay rate of the expansion coefficients, PHLFT allows one to compute local Fourier magnitudes and phases without revealing the edge effect (or Gibbs phenomenon), yet is invertible and useful for various filtering, analysis, and approximation purposes.     

有序判别分析新算法及其应用

判别分析是用已知分类数据建模对未知分类数据进行判别的方法,所用数据和分类不分顺序。要对有序又有周期数据进行判别分析,就要探索有序判别的新方法。这种方法的分类应当是有序的,并且能够排除事物发展周期性的干扰。本文介绍多元数据有序判别分析新方法的原理、建模流程、应用流程和应用实例。这种判别分析将分类建模与判别归类分开。新方法对多元数据建模时在多类模型中建立滑移的多套子模型,应用时根据应用领域的知识对样本归属作初步预估,然后程序选择相关的子模型进行判别归类。这种方法解决了由于时间序列多元数据周期性造成的样本分类颠倒问题,为时间序列数据的分类和预测开辟了新途径,在实际应用中取得了良好的效果,解决了重大难题。     

On classification of quasifinite representations of two classes of Lie superalgebras of block type

Motivated by a well-known theorem of Mathieu’s on Harish–Chandra modules over the Virasoro algebra and its super version, we show that an irreducible quasifinite module over two classes of Lie superalgebras 𝒮(q) of Block type is either a highest or lowest weight module or else a module of the intermediate series if q≠?1. For such a module over 𝒮(?1), we give a rough classification.     

Approximate solutions for time-fractional two-component evolutionary system of order $2$ using coupled fractional reduced differential transform method

In this paper, Coupled Fractional Reduced Differential Transform method is extended to apply to the generalized time-fractional two-component evolutionary system of order 2. By using this method, the solutions in the form of a generalized Taylor series are obtained. The graphics of numerical solutions together with the error analysis demonstrate that the present method is effective and accurate for obtaining approximate solutions of fractional coupled equations. Moreover, the results also indicate that the solutions obtained by residual power series method in previous literature (M. Alquran, Analytical solution of time-fractional two-component evolutionary system of order 2 by residual power series method, J. Appl. Anal. Comput.,5(2015)(4), 589-599.) contain errors.     

Functional data classification: a wavelet approach

In recent years, several methods have been proposed to deal with functional data classification problems (e.g., one-dimensional curves or two- or three-dimensional images). One popular general approach is based on the kernel-based method, proposed by Ferraty and Vieu (Comput Stat Data Anal 44:161–173, 2003). The performance of this general method depends heavily on the choice of the semi-metric. Motivated by Fan and Lin (J Am Stat Assoc 93:1007–1021, 1998) and our image data, we propose a new semi-metric, based on wavelet thresholding for classifying functional data. This wavelet-thresholding semi-metric is able to adapt to the smoothness of the data and provides for particularly good classification when data features are localized and/or sparse. We conduct simulation studies to compare our proposed method with several functional classification methods and study the relative performance of the methods for classifying positron emission tomography images.     

稳健残差控制图的构建及在金融时序中的应用

对于呈现自相关和波动族聚性并存的受控过程,通常采用残差控制图对其进行监控。但异常点的存在会对自相关或波动族聚性模型的拟合产生重要影响,使得基于该模型的残差并非独立同分布导致常规残差控制图监控失效。为解决这类问题,本文提出稳健残差控制图。即建立稳健的ARMA模型解决自相关问题从而得到无自相关的残差序列,用稳健的GARCH模型来构建控制图的上下限。模拟和实证研究表明,本文提出的稳健残差控制图具有很好的抗异常点能力并能更好的对金融时间序列的异常现象进行监控。     

Comparison of some AI and statistical classification methods for a marketing case

Recent progress in data processing technology has made the accumulation and systematic organization of large volumes of data a routine activity. As a result of these developments, there is an increasing need for data-based or data-driven methods of model development. This paper describes data-driven classification methods and shows that the automatic development and refinement of decision support models is now possible when the machine is given a large (or sometimes even a small) amount of observations that express instances of a certain task domain. The classifier obtained may be used to build a decision support system, to refine or update an existing system and to understand or improve a decision-making process. The described AI classification methods are compared with statistical classification methods for a marketing application. They can act as a basis for data-driven decision support systems that have two basic components: an automated knowledge module and an advice module or, in different terms, an automated knowledge acquisition/retrieval module and a knowledge processing module. When these modules are integrated or linked, a decision support system can be created which enables an organization to make better-quality decisions, with reduced variance, probably using fewer people.