基于光谱特征和纹理特征协同学习的高光谱图像数据分类

高光谱遥感图像中包含有大量的高维数据,传统的有监督学习算法在对这些数据进行分类时要求获取足够多的有标记样本用于分类器的训练.然而,对高光谱图像中大量的复杂地物像元所属类别进行准确标注通常需要耗费极大的人力.在本文中,我们提出了一种基于半监督学习的光谱和纹理特征协同学习(STF-CT)--法,利用协同学习机制将高光谱图像光谱特征和空间纹理特征这两种不同的特征结合起来,用于小训练样本集下的高光谱图像数据分类问题.STF-CT算法充分利用了高光谱图像的光谱和纹理特征这两个独立视图,构建起一种有效的半监督分类方法,用于提升分类器在小训练样本集情况下的分类精度.实验结果表明该算法在小训练样本集下的高光谱地物分类问题上具有很好的效果.     

基于改进半监督生成对抗网络的少量标签轴承智能诊断方法EI北大核心CSCD

轴承在线监测大数据主要是由大量无标签数据和少量有标签数据组成的。已有的智能诊断方法多依赖于大量有标签数据的监督学习。针对此问题,提出一种改进半监督生成对抗网络,利用生成器与分类器的对抗学习增强分类器的辨识能力,提出了增强特征匹配算法,借助分类器的深层特征优化网络损失值计算,进而提高网络收敛速度,结合半监督学习使用少量有标签数据进一步提升分类器的学习能力,最终实现对无标签数据的正确归类。使用四组轴承试验数据的迁移学习验证和对比了改进深度网络对不同轴承、工况、故障生成模式、故障程度的辨识能力,结果表明该网络具有更高的分类准确率和更快的收敛速度。     

基于协同采样主动学习的恶意代码检测

研究了基于机器学习分类算法的恶意代码检测,考虑到目前主要采用传统分类方法对恶意代码进行分类识别,这些方法需要通过学习大量标记样本来获得精准的分类器模型,然而样本标记工作只有少数专家才能完成,导致标记样本往往不足,致使分类结果准确率不高,提出了一种基于协同采样的主动学习方法。运用这种学习方法,仅需少量标记样本即可有效识别出恶意代码。实验证明,相对于传统的恶意代码分类方法,该方法能够显著提升分类准确率和泛化性能。     

基于小波包变换和随机森林算法的光伏系统故障分类

针对光伏系统故障分类问题,提出一种小波包变换和随机森林算法相结合的故障分类方法。采集光伏系统的故障电压数据,利用小波包变换对电压信号进行分解,提取各频带能量作为故障特征,将特征样本送入随机森林算法中进行分类。随机森林算法是结合集成学习理论和随机子空间方法的一种算法,可以对多种故障做出准确分类。使用PSCAD/EMTDC搭建独立光伏发电系统,选取12种故障进行模拟,得到600个故障样本,选取其中360个样本用于训练分类器,240个样本用于测试分类器的分类性能。仿真结果表明:该方法可有效辨别光伏系统的12种故障,分类准确率达到97.92%。与RBF神经网络分类器相比,故障分类准确率提高了4.17%,对进一步实现光伏系统故障诊断研究具有重要意义。     

一种增量式半监督VPMCD齿轮故障在线诊断方法

针对齿轮故障诊断中难以获得大量故障样本的问题及实时在线诊断的需求,提出了一种基于增量式半监督多变量预测模型(Incremental Semi-supervised Variable Predictive Model based Class Discriminate,ISVPMCD)的齿轮故障在线检测方法。首先使用VPMCD方法给少量的已知样本建立初始预测模型,接着利用VPMCD方法中的判据给未标识样本赋予初始伪标识,然后通过互相关准则筛选出伪标识样本,最后利用伪标识样本和已知样本作为训练样本更新初始预测模型,使得更新的预测模型能兼顾整个样本集的信息,从而可以有效地解决小样本的故障诊断问题,另外,由于该方法在实时更新新样本的过程中不需要再次建立判别模型,从而缩短了分类时间,为实时在线诊断提供了新的思路。对UCI标准数据以及齿轮实测数据的分析结果表明,适合于小样本的ISVPMCD模式识别方法可以更快更准确地识别齿轮工作状态和故障类型。     

基于VMD散布熵与改进灰狼优化SVDD的轴承半监督故障诊断研究

为充分挖掘未标记样本所蕴含的有效信息,进而提升诊断精度,研究提出一种基于变分模态分解(VMD)散布熵与改进灰狼优化支持向量数据描述(SVDD)的轴承半监督故障诊断方法。采用中心频率观察法确定VMD分解模态参数K,进而将原始信号分解为一系列本征模态函数并计算各分量的散布熵值,构成测试样本和部分标记的训练样本;再由半监督模糊C均值(SSFCM)聚类对训练样本进行聚类分析,从而对所得聚类簇进行SVDD建模,同时采用k近邻准则进行决策优化,并由所提自适应变异灰狼算法优化SVDD模型参数;将基于最优参数训练的改进决策SVDD模型用于测试样本的故障模式识别。试验分析和对比结果表明,所提方法具有较好的诊断性能。     

优化KNNC算法在滚动轴承故障模式识别中应用

为有效提高滚动轴承故障诊断率,正确识别不同故障类型,提出基于优化K-最近邻域分类器(K-Nearest Neighbor Classifier,KNNC)的轴承故障模式识别方法。分别求得滚动轴承训练样本与测试样本的振动特征指标,构建样本特征集。为加快分类速度,剔除不良样本干扰,利用K-均值聚类算法对样本进行优化精简,并将所得若干聚类中心作为新的约简训练集。据新训练集进行KNNC分析,实现模式识别。结果表明：该方法能快速、有效识别出滚动轴承4种不同故障模式,识别正确率明显提高。     

一种基于半模糊聚类的故障诊断方法

为满足故障诊断的实时性和准确性要求,采用阈值化类内距离的方法,研究了一种快速收敛的半模糊c均值(SFCM)聚类诊断方法.证明了SFCM算法的模糊加权幂指数m在区间(0,1)取值时能实现半模糊聚类,讨论了阈值η对算法的影响并给出了算法步骤.以机载武器控制系统信息通道为诊断对象,采用该方法对通道进行了样本无监督分类验证和故障模式识别诊断试验.结果表明:SFCM算法能对信息通道故障模式进行快速准确的分类识别.     

基于排列熵与IFOA-RVM的汽轮机转子故障诊断

为了提高汽轮机转子故障诊断的识别准确率和效率,提出基于排列熵与改进的果蝇算法(IFOA)优化相关向量机(RVM)的汽轮机转子故障诊断方法。将实验数据进行自适应完备的集合经验模态分解(CEEMDAN),并选取故障特征敏感的IMF分量计算排列熵,以此构造特征样本集,进而建立"二叉树"IFOA-RVM故障分类器对特征集进行分类,其中IFOA通过两个阶段来定义果蝇群体的搜索范围来提高搜索效率,同时避免RVM核函数陷入局部最优。通过ZT-3汽轮机转子模拟试验台获得的故障数据进行实验研究,结果表明与模糊熵对比,排列熵获得的特征样本集的聚类效果明显;IFOA-RVM分类器在故障识别准确率和效率上优于FOA-RVM等其它分类器;证明了基于排列熵与IFOA-RVM汽轮机转子故障诊断方法的有效性和可行性。     

多波束声呐海底底质半监督学习分类方法

多波束测深声呐的反向散射数据中包含海底表层的声学信息,可以用来进行海底表层底质分类。但实际中通过物理采样获得大范围的底质类型的标签信息所需成本过高,制约了传统监督分类算法的性能。针对实际应用中只拥有大量无标签数据和少量有标签数据的情况,文章提出了基于自动编码器预训练以及伪标签自训练的半监督学习底质分类算法。利用2018年和2019年两次同一海域实验采集的多波束测深声呐反向散射数据,对所提算法进行了验证。数据处理结果表明,相比仅利用有标签数据的监督分类算法,提出的半监督学习分类算法保证分类准确率的同时所需的有标签数据更少。自动编码器预训练的半监督学习分类方法在有标签样本数量极少时的准确率仍高于75%。     

数据特征选择与分类在机械故障诊断中的应用

针对机械故障数据的高维性和不平衡性,提出基于格拉斯曼流形的多聚类特征选择和迭代近邻过采样的故障分类方法。对采集到的振动信号,提取时域和频域相关特征,利用多聚类特征选择将高维数据以局部流形结构映射到低维特征集合。无标签样本借助迭代近邻过采样以恢复最大平衡性为目标进行样本分类,并对剩余无标签样本进行模糊分类。选取滚动轴承正常、外圈、内圈以及滚动体的故障数据,并与支持向量机、基于图的半监督学习算法进行对比。结果表明,提出的方法能有效识别出少数类故障,并在整体上有显著的分类效果。     

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.     

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data, especially in the field of Synthetic Aperture Radar (SAR) image recognition. In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model, this paper proposes a semi-supervised learning method of threshold filtering. In the training process, through the two selections of data by the model, unlabeled data outside the category is filtered out to optimize the performance of the model. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and compared with existing several state-of-the-art semi-supervised classification approaches, the superiority of our method was confirmed, especially when the unlabeled data contained a large amount of out-of-category data.     

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains small subsets to these issues. First, this research introduces a deep neural network (DNN)-based GANomaly for semi-supervised learning. Second, this paper presents the proposed adaptive algorithm for the DNN-based GANomaly, which is validated with four subsets of the adaptive dataset. Finally, this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations, reconstruction error visualization, and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage, semi-supervised learning, and adaptive learning. Compared to other single-class classification techniques, the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13% and 8% of F1 scores and 4.17% and 11.51% for accuracy, respectively. Furthermore, experiments of the proposed adaptive learning reveal mostly improved results over the initial values. An analysis and monitoring system based on the combination of the three explainable methodologies is also described. Thus, the proposed method has the potential advantages to be applied in practical industry, and future research will explore handling unbalanced real-time datasets in various scenarios.     

半监督极限学习机用于Android手机活动识别的研究

基于对现有Android手机活动识别技术的分析,针对从不完全、不充分的移动传感器数据中推断人体活动的难题,将能根据无标签样本提高识别预测准确性和速度的半监督(SS)学习和体现模式分类回归的有效学习机制的极限学习机(ELM)相结合给出了解决Android手机平台的人体活动识别问题的半监督极限学习机(SS-ELM)方法,并进一步提出了主成分分析(PCA)和半监督极限学习机(SS-ELM)结合的PCA+SS-ELM新方法。实验结果表明,该方法对人体活动的识别正确率能达到95%,优于最近提出的混合专家半监督模型的正确率,从而验证了该新方法是可行性。     

基于TSVM与主动学习融合的蛋白质交互作用关系抽取

针对蛋白质交互作用关系(PPI)抽取研究中已标注语料有限而未标注生物医学自由文本易得的问题,进行了基于直推式支持向量机(TSVM)与主动学习融合的蛋白质交互作用关系抽取研究.通过自主选择最优的未标注样本加入到TSVM的训练过程中,最大程度地提高了系统的性能.实验结果表明,TSVM与主动学习融合的算法在少量已标注样本和大量未标注样本组成的混合样本集上取得了较好的学习效果,与传统的支持向量机(SVM)和TSVM算法相比,能有效地减少学习样本数,提高分类精度,在AImed语料上取得了F测度为64.12%的较好性能.     

Detection of pneumonia in chest X‐ray images by using 2D discrete wavelet feature extraction with random forest

Pneumonia is one of the most common and fatal diseases in the world. Early diagnosis and treatment are important factors in reducing mortality caused by the aforementioned disease. One of the most important and common techniques to diagnose pneumonia disease is the X‐ray images. By evaluating these images, various machine‐learning methods are used for accuracy in diagnosis. The presented study in this article utilizes machine‐learning techniques to evaluate these X‐ray images. The diagnosis of pediatric pneumonia is classified with a proposed machine learning method by using the chest X‐ray images. The proposed system firstly utilizes a two‐dimensional discrete wavelet transform to extract features from images. The features obtained from the wavelet method are labeled as normal and pneumonia and applied to the classifier for classification. Besides, Random Forest algorithm is used for the classification technique of 5856 X‐ray images. A 10‐fold cross‐validation method is used to evaluate the success of the proposed method and to ensure that the system avoided overfitting. By using various machine learning algorithms, simulation results reveal that the Random Forest method is proposed and it gives successful results. Results also show that, at the end of the training and validation process, the proposed method achieves higher success with an accuracy of 97.11%.