Lithofacies identi cation using support vector machine based on local deep multi-kernel learning

Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacies and seismic information that is affected by many factors is complicated.Machine learning has received extensive attention in recent years,among which support vector machine(SVM) is a potential method for lithofacies classification.Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem,which needs to be solved by means of the kernel function.Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification.However,it is very difficult to determine the kernel function and the parameters,which is restricted by human factors.Besides,its computational efficiency is low.A lithofacies classification method based on local deep multi-kernel learning support vector machine(LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed.The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information.The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification.Both the model data test results and the field data application results certify advantages of the method.This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM.

Multi-channel differencing adaptive noise cancellation with multi-kernel method

Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingre- dient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the clas- sical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least- mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously con- sider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the out- put signal-to-noise ratio （SNR） of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least- mean-square method using the realistic noise data measured in the lake experiment.

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

非线性的系统和它的特定的鉴定方法的 Multi-kernel-based 支持向量机器(SVM ) 模型结构被建议，它 SVM 是镇静的，线性内核功能与花键内核功能由 SVM 在系列列在后面。在这个模型的帮助下，非线性的模型预兆的控制能被转变到线性模型预兆的控制，并且因而预兆的控制是可能的发源的 multi-step-ahead 的最佳的输入的一个统一分析答案。这个算法不要求联机反复的优化以便对有更少的计算的即时控制合适。pH 中立化过程和 CSTR 反应堆的模拟结果显示出介绍算法的有效性和优点。

基于BA-MKELM的微电网故障识别与定位

提出一种基于贝叶斯算法优化多核极限学习机的微电网故障识别和定位方法。针对极限学习机输入参数和隐含层节点数随机选取导致回归能力不足的问题,引入核函数,将多项式与高斯径向基核函数加权组合构成多核极限学习机建立故障识别与定位模型,并采用贝叶斯算法对多核极限学习机相关参数进行优化,进一步提高模型的逼近能力。为了验证所提模型的故障识别与定位性能,选用极限学习机和多核极限学习机分别建立故障诊断模型进行比较分析。实验结果表明,所提方法能够高性能地识别和定位微电网中任何类型的故障,识别和定位精度更高。

多向加权Tsallis熵最大化导向的自动阈值分割方法

受噪声或随机细节、目标和背景的大小比例、成像时的点扩散等不同因素的影响,许多图像的灰度直方图呈现为无模态、单模态、双模态或者多模态样式.为了在统一框架内处理这4种不同模态情形下的自动阈值选择问题,本文提出了一种多向加权Tsallis熵最大化导向的自动阈值分割方法(Multi-directional Weighted Tsallis Entropy,MWTE).基于新设计的反正切方向性卷积核的多尺度乘积效应,该方法将不同模态的灰度直方图转化为统一的单模态右偏灰度直方图.在4个不同方向上提取出这种特殊的单模态右偏灰度直方图后,通过多向加权策略构建出与原始图像灰度值紧密相关的加权Tsallis熵目标函数,并以该目标函数取最大值时对应的灰度值作为最终分割阈值.本文将提出的方法和3个阈值分割方法、1个软分割方法、1个活动轮廓分割方法以及1个自动聚类分割方法进行了比较.在4种不同模态情形下的4幅合成图像和50幅真实世界图像上的实验结果表明,本文提出的方法虽然在计算效率方面不占有优势,但它对不同模态的测试图像具有更稳健的分割适应性,且在量化分割精度所用的马修斯相关系数方面优于其他6个分割方法.

计及随机变量相关性的多点线性化概率潮流计算

为满足考虑源荷双侧强不确定性场景的“双高”电力系统潮流分析计算需求,提出一种新型的概率潮流计算方法。基于核密度估计建立输入随机变量的概率分布模型,构建Copula函数刻画多维随机输入变量间的相关性,获取更加符合系统实际运行情况的样本数据。引入多点线性化潮流计算方法,在降低非线性潮流计算量的同时减小单点线性化潮流计算的截断误差。在IEEE-30节点系统上进行算例测试,验证所提方法的准确性和有效性。

高效多刀位推进式全自动坚果取仁装置应用研究

坚果取仁是坚果加工过程中一道重要工序,取仁装置是该工序的主要设备。传统取仁装置分为人工取仁和人工协同取仁装置取仁,存在费时费力、自动化程度低、进料不均、效率低、质量差、易堵塞、不防滑等缺点。根据坚果取仁工艺存在的问题,研制出了多刀位推进式全自动坚果取仁装置,具有进料均匀、全自动、取仁效率高、取仁质量好、省时省力、不易堵塞和打滑等优点,经济效益和社会效益较好,有显著的推广应用前景。

基于多核模糊条件熵的多类型混合数据属性约简算法

对数据进行有效属性约简是数据挖掘中一个具有挑战性的任务。当前,粗糙集理论是构造属性约简的一种常用方法。然而,现有的属性约简方法都侧重于单类型的数据,对现实环境下多类型混合的数据并不适用。为了解决这一问题,提出一种多核模糊条件熵的多类型混合数据属性约简算法。首先,针对标记型、数值型、区间型和集值型混合的多类型数据,提出了一种多核模糊相似关系。然后,基于这种多核模糊相似关系,定义了一种多核模糊条件熵模型,并讨论了它的单调性和有界性。最后,利用多核模糊条件熵的单调性提出了一种多类型混合数据的属性约简算法。通过UCI数据集的实验分析验证了该算法的有效性。

Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine

In order to accurately identify a bearing fault on a wind turbine, a novel fault diagnosis method based on stochastic subspace identification(SSI) and multi-kernel support vector machine(MSVM) is proposed. Firstly, the collected vibration signal of the wind turbine bearing is processed by the SSI method to extract fault feature vectors. Then, the MSVM is constructed based on Gauss kernel support vector machine(SVM) and polynomial kernel SVM. Finally, fault feature vectors which indicate the condition of the wind turbine bearing are inputted to the MSVM for fault pattern recognition. The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.

结合MGCC特征与多尺度通道注意力的环境声深度学习分类方法

环境声分类技术在家居安全监测、人机语声交互等领域具有关键作用。然而,声源的多样性与混合性给环境声分类方法设计带来了重大挑战。为提高分类准确率与节约计算资源,该文提出一种基于多尺度通道注意力机制的深度学习分类模型。所提模型由特征提取模块、多尺度卷积模块、高效通道注意力模块、输出层四部分组成。首先,通过引入加权型梅尔Gammatone频率倒谱系数(MGCC)挖掘环境声频谱幅值与相位结构信息;其次,融合多尺度卷积核与高效通道注意力机制优选出声频关键局部细节和通道特征;最后,在全连接层采用softmax函数映射特征并输出环境声类型的概率值。所提模型在6种环境声的iFLYTEK、10种环境声的Urbansound8k数据集上开展测试验证,分别取得了94%、76.52%、79.24%(iFLYTEK+Urbansound8k)的分类准确率。消融实验结果进一步表明:引入的多尺度卷积模块、通道注意力机制模块对分类准确率的提升贡献率分别接近于3.77%和1.89%。实验还详细对比了7种现有的深度学习分类方法,所提算法在分类准确率上排名第二;另外,在同级别算法中如ResNet18、GoogLeNet,所提算法在模型参数量和计算复杂度方面上实现了进一步的约减。

基于Multi-kernel和KRR的数据还原算法

由于数据被核化后不能还原,使核方法的应用受到局限.对此,提出一种基于Multi-kernel和KRR的数据还原算法.首先,通过同类数据中已知数据进行多次核化迭代,使已知数据在超高维欧氏空间中呈线性;然后,利用已知数据对同类未知数据进行线性表示,并以Kernel ridge regression(KRR)算法进行未知数据的回归;最后实现数据还原.选取Iris flower和JAFFE两类数据集进行还原实验,实验结果表明,所提出的算法可以有效地还原未知数据,而且在其他领域的应用也有较好的效果.

多核支持向量机预测电网系统可靠性

为了改善电网系统可靠性预测性能,构建多个目标函数并采用多核支持向量机算法对配电网进行可靠性预测;从电网样本特征中筛选供电可用率、户均停电时间、户均停电次数3个关键指标,建立可靠性评价目标函数,且采用多核支持向量机训练可靠性指标特征;将高斯核函数、多项式核函数和Sigmoid核函数进行多核组合,采用多核支持向量机求解不同目标函数,获得电网系统可靠性预测结果,进而确定更佳的可靠性预测核函数组合。结果表明,合理选择核函数组合和电网可靠性指标,多核支持向量机对供电可用率、户均停电时间和户均停电次数指标预测准确率较高,且稳定性好,高斯核函数-Sigmoid核函数组合的可靠性预测准确性最佳,高斯核函数-多项式核函数-Sigmoid核函数组合的预测稳定性最好。

Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine

Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine （OM-ELM） which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning （MKL） problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.

An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs

In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction.In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather than the regular emotion classification or emotion component extraction. Since there is no open dataset for this task available, we first designed and annotated an emotion cause dataset which follows the scheme of W3 C Emotion Markup Language. We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events. Since the distribution of events is imbalanced in the training data, we propose an under-sampling-based bagging algorithm to solve this problem. Even with a limited training set, the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method. Evaluations show that our approach achieves an F-measure 7.04%higher than the state-of-the-art methods.

The Optimal Solution of Multi-kernel Regularization Learning

In regularized kernel methods, the solution of a learning problem is found by minimizing a functional consisting of a empirical risk and a regularization term. In this paper, we study the existence of optimal solution of multi-kernel regularization learning. First, we ameliorate a previous conclusion about this problem given by Micchelli and Pontil, and prove that the optimal solution exists whenever the kernel set is a compact set. Second, we consider this problem for Gaussian kernels with variance σ∈（0,∞）, and give some conditions under which the optimal solution exists.

Multi-kernel dictionary learning for classifying maize varieties

The automatic classification and identification of maize varieties is one of the important research contents in agriculture.A multi-kernel maize varieties classification approach was proposed in this paper in order to improve the recognition rate of maize varieties.In this approach,four kinds of maize varieties were selected,in each variety 200 grains were selected randomly as the samples,and in each sample 160 grains were taken as the training samples randomly;the characteristics of maize grain were extracted as the typical characteristics to distinguish maize varieties,by which the dictionary required by K-SVD was constructed;for the test samples,the feature-matrixes were extracted by dimension reduction method which were mapped to the high-dimension space by muti-kernel function mapping.The high-dimension characteristic matrixes were trained by K-SVD method and the corresponding feature dictionary was obtained respectively.Finally,the test samples representing were trained and classified by l2,1 minimization sparse coefficient.The experiment results showed that recognition rate was improved obviously through this approach,and the poor-effect to maize variety identification from partial occlusion can be eliminated effectively.

基于Optuna框架的L_(p)范数约束下多核支持向量机在违约风险预测中的应用

针对违约数据存在数据量大、维度多、不平衡及噪声大等缺点,提出一种改进的支持向量机方法,即基于Optuna框架的L_(p)范数约束的代价敏感的多核支持向量机(L_(p)-Optuna-SVM)。该方法采用成本矩阵对不同预测错误赋予不同数值,通过多核学习引入多核混合核函数组合;同时采用Optuna优化框架对犯错成本、核函数的参数和权重实现了自动化的调优过程;还在核函数权重上引入L_(p)范数约束,以提高模型对噪声和异常数据的鲁棒性。最后,对4种常用的基础核函数组合的L_(p)-Optuna-SVM进行探讨,并与单核支持向量机以及K邻近法、逻辑回归、高斯贝叶斯进行对比。结果表明,在给定数据集上,L_(p)-Optuna-SVM在违约数据上的g-mean和AUC均高于其他算法,并且在加了不同方差的噪声数据集上,该算法整体依旧保持较好的鲁棒性。

基于三支特征表示的抽象画情感聚类分析

针对绘画图像情感标注所需资源巨大的问题,设计一种针对抽象画图像的情感聚类方法。提出一种基于三支决策的颜色特征表示方法和纹理特征表示方法,结合改进的深度学习模型,从抽象画图像中提取颜色特征、纹理特征和高层语义特征;使用多核k均值算法,自适应地融合3种特征,实现图像的情感聚类分析。实验结果表明,在MART和Deviant-Art数据集上,与4种基准方法相比,提出方法在准确度、Fowlkes-Mallows指数和标准化互信息上分别平均提高了30、23和49个百分点。提出方法在抽象画图像的情感聚类分析应用中表现出色,这也为其它绘画作品的无监督情感分析研究提供了参考。

基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别

针对多基地水下小目标分类识别问题,本文提出了一种基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别方法 .对水下目标多角度散射信号提取6种典型的具有信息互补性和关联性的特征,提出一种随机森林(Random Forest,RF)和最小冗余最大相关(minimum Redundancy and Maximum Relevance,mRMR)相结合的特征选择方法(RF-mRMR),得出综合的特征重要性排序结果 .通过实验得出分类模型所需的最优特征子集,达到降低数据处理复杂度和提高目标分类结果的目的 .为了捕捉到数据中的高阶结构,在联合稀疏表示模型的基础上,使用核函数将线性不可分的特征数据映射到高维核特征空间.为了充分挖掘稀疏重构后包含在残差波段中的有用信息,使用指数平滑公式对具有一定意义的残差信息进行再利用,最后由核特征空间下的最小误差准则判定目标的类别.应用本文提出的方法对4类目标的海试数据进行识别,结果表明,相较于其他7种对比算法,本文提出的改进方法具有更好的分类性能,而且大多数情况下,本文提出的算法在双基地声呐模式下具有比单基地声呐更高的识别准确率和更低的虚警率.

基于DE-VMD和GMDE的往复压缩机轴承间隙故障诊断方法

针对往复压缩机轴承间隙故障特征提取困难、识别准确率不高等问题,提出了差分进化算法优化变分模态分解方法和广义多尺度散布熵相结合的往复压缩机间隙故障诊断方法。首先,采用差分进化算法对变分模态分解算法的两个核心参数进行了优化,并利用优化后的变分模态分解方法对轴承间隙振动信号进行了信号分解和重构处理;然后,研究了多尺度散布熵的粗粒化过程,通过将方差粗粒化代替均值粗粒化,进行了多尺度处理,构建了广义多尺度散布熵算法,利用广义多尺度散布熵算法对重构信号进行了故障特征提取分析;最后,设计了核极限学习机模型对故障特征向量集进行了分类识别,完成了往复压缩机轴承间隙不同故障状态的智能诊断研究。研究结果表明,该故障诊断方法的识别准确率高达97%,高效地实现了轴承不同种类故障的智能诊断目的。