基于改进迁移学习的运动想象分类识别算法北大核心CSCD

利用迁移学习算法提高分类识别的准确率是运动想象脑机接口应用的热点研究问题,其中样本迁移和特征迁移的传统模型算法在样本量较少或源域数据和目标域数据差异较大情况时,各自的迁移效果并不理想。基于欧式对齐(EA)和改进联合类质心匹配和局部流形自学习(CMMS)迁移学习的运动想象分类算法,将样本迁移和特征迁移的优势有机结合,在考虑样本本身的同时,进一步提高了分类准确率。首先,对样本进行源域和目标域的EA,减少源域和目标域的数据分布差异;其次,基于最小化最大均值差异(MMD)改进CMMS方法,筛选源域数据,再次减小源域样本与目标域的分布差异;最后,将该方法应用于BCI竞赛数据集进行离线测试和在线实验。实验结果表明:所研究的方法与SVM、JDA、BDA、EasyTL、GFK、CMMS相比较,迁移学习模型的识别准确率分别提高了14.38%,8.5%,5.8%,10.4%,11.8%,5.7%。     

基于迁移学习的运动想象脑电信号分类研究

由于单个被试的运动想象脑电训练样本较少,且不同被试的脑电个体差异较大,导致大脑运动想象任务分类正确率不高、训练校准时间过长.本文提出一种欧几里得对齐-正则共空间模式的迁移学习方法,实现在样本和特征上同时进行迁移.通过数据对齐的迁移方法使不同被试间样本分布更相似,采用正则化迁移方法改进共空间模式,提取出更鲁棒的空间特征信息.对BCI competition运动想象脑电数据集进行仿真测试,右手和右脚运动想象任务的平均分类正确率可以达到87.10%;在小训练样本集上,本文的迁移学习方法相比共空间模式方法,其平均分类正确率提高了14.43%;对于跨被试的运动想象脑电,平均分类正确率提高了23.5%.实验结果表明,该方法可以有效提高小样本、跨被试运动想象脑电的分类正确率,减少模型校准训练时间,提高泛化能力.     

基于残差图卷积网络的运动想象任务分类

为了提高运动想象脑机接口任务分类的准确性,需要增强运动想象脑电信号的解码精度。利用脑电的空间分布及多导联信息关联,构建图神经网络,提出了一种基于残差图卷积的运动想象任务分类模型。将残差学习嵌入深度图卷积神经网络,改善网络退化;并将分层图池化方法加入模型,充分提取运动想象脑电特征信息,提高分类准确率。该模型在两个脑机接口竞赛数据集上分别取得93.84%和96.39%的平均分类准确率以及0.917 1和0.953 5的平均Kappa系数。仿真结果表明,模型能有效提高运动想象脑机接口任务分类精度,且具有较好的泛化能力。     

基于盲源分离的运动想象脑电信号特征提取方法的研究

运动想象脑电信号被广泛应用于脑机接口系统中。针对如何准确有效地提取运动想象脑电信号特征的问题,通过分析运动想象脑电信号时域、频域和头皮空间域的特征,提出了以小波变换为预处理,并利用二阶盲辨识算法和信息论特征提取算法相结合获取的空间滤波器,从时域、频域和头皮空间域对运动想象脑电信号进行特征提取的方法。实验结果表明,采用时域、频域和空间域提取特征的方法性能有明显提高,并且将二阶盲辨识算法和信息论特征提取算法相结合获取的空间滤波器能够反映更真实的大脑源活动。     

基于多源域深度迁移学习的机械故障诊断EI北大核心CSCD

针对不同工况下的机械故障诊断问题,迁移学习方法相比于深度学习具有明显的成效,单源域迁移故障诊断仍会出现负迁移和模型泛化能力差的问题。因此,本文提出一种基于多源域深度迁移学习的机械故障诊断方法。首先,进行锚适配器的构建,获得多源域-目标域适配器数据对。其次,建立基于深度域适应的迁移学习网络模型获得每个数据对的分类器与预测结果。最后,采用加权集成的方式进行分类器集成,用于最终的故障诊断识别。所提方法充分集成多源域故障特征信息,提取域不变特征,避免负迁移的问题,提高模型的泛化能力。通过一个滚动轴承数据来验证提出方法的性能,结果表明,多工况迁移故障诊断分类精度明显高于其中任意单一工况迁移,最高可提高8.78%,与其他方法相比,所提方法具有较好的精度和泛化能力。     

基于独立分量分析和普鲁克分析的运动想象迁移学习策略

针对传统上基于运动想象的脑机接口系统在应用之前需要进行枯燥冗长的校准实验的问题,提出基于独立分量分析(ICA)和普鲁克分析的迁移学习算法。该算法采用独立分量分析对脑电信号进行空间滤波,对比应用多种对齐变换方法,实现对样本数据的平移和缩放。并提出欧氏空间下的旋转变换方法,以进一步匹配目标受试者与其他受试者的样本分布,实现有效的跨受试者和跨数据集的迁移学习。所提算法相较基于黎曼普鲁克分析的方法具有更好的分类性能以及计算效率,并在公共数据集PhysionetMI和BCIIV-2a上较传统机器学习算法将平均kappa值提高了约0.1和0.04。结果表明该方法能有效提高小样本下的分类正确率,这有助于降低对校准数据量的需求,从而减少校准实验的耗时。     

基于深度对比迁移学习的变工况下机械故障诊断

机械设备实际运行中的工况具有时变性，这加剧了源域（训练集）和目标域（测试集）数据之间的分布差异，因而导致智能故障诊断模型的性能下降。提出了一种基于深度对比迁移学习的方法，可用于机械设备变工况下的故障智能诊断。利用多层卷积块作为模型前置特征提取器，能够有效提取原始振动数据的代表性特征，提升故障分类器和域判别器的诊断性能。将前置特征提取器提取的特征传递给特征融合器，特征融合器提炼并联接局部感受野和全局感受野卷积特征，增强模型特征表达能力。将特征融合器提炼的特征用于故障分类器和域判别器诊断不同工况下的机械故障，并在故障分类器中使用Wasserstein距离度量源域和目标域数据的差异，基于互信息噪声对比估计提出用于工况区分的互信息对比域判别器，提高模型的迁移诊断性能。将所提方法用于诊断变工况下不同类别的轴承、齿轮故障。结果表明，所提方法能够有效实现变工况下轴承、齿轮故障的迁移诊断。     

基于改进ECANet-TCN和迁移学习的轴承剩余寿命预测

针对轴承运行工况不同、有效数据少、数据无标签、预测准确度低等问题,提出一种基于改进时间卷积网络的迁移学习轴承寿命预测模型,将模型在源域上学习的寿命预测知识迁移到目标域,可用小样本无标签数据训练出迁移模型。首先,采用有效通道注意力模块对源域数据特征重新标定;其次,使用时间卷积网络(temporal convolutional network, TCN)学习特征信息,并训练出最优源域模型;最后,利用源域数据、源域模型和目标域数据训练出迁移模型,迁移模型可以对不同设备不同工况信号进行剩余寿命预测。在IEEE PHM Challenge 2012轴承全寿命数据集和西安交通大学XJTU-SY滚动轴承加速寿命数据集上开展对比试验,结果表明,该方法可以更好地挖掘轴承内在退化趋势,有效提高剩余使用寿命预测精度,对比现有流行预测方法预测误差降低40.1%～77.8%,证明了该方法在不同设备不同工况条件下剩余寿命预测的有效性和可行性。     

基于变分模态分解与深度信念网络的运动想象分类识别研究

传统人工确定最优时段及最优频段的方法会造成信息遗漏进而导致运动想象识别率的降低, 因此基于脑电信号的运动想象分类研究成为了脑-机接口研究领域的难点问题。针对该问题,变分模态分解和深度信念网络被应用于运动想象分类。对脑电信号进行变分模态分解得到窄带分量,利用希尔伯特变换提取边际谱、特征频带下的瞬时能谱以及时-频联合特征; 特征融合后采用深度信念网络对高维特征降维并实现运动想象模式的识别, 避免了人工确定想象最优时段及最优频段造成的信息遗漏。实验结果表明,利用变分模态分解与深度信念网络自动提取最优时段及最优频段特征的方法有效提升了运动想象识别率。     

基于特征参数迁移的滚动轴承故障诊断

针对变工况下的滚动轴承无法获得大量带标签样本数据以及传统深度学习诊断方法识别率低的问题,提出一种基于迁移学习的卷积神经网络模型滚动轴承故障诊断方法.首先,采用短时傅里叶变换处理滚动轴承振动信号获得源域、目标域样本集;其次,利用源域样本预训练卷积神经网络模型;最后,通过目标域样本微调卷积神经网络模型实现滚动轴承故障诊断....     

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.     

基于深度迁移学习的微型细粒度图像分类

现有的细粒度分类模型不仅利用图像的类别标签,还使用大量人工标注的额外信息。为解决该问题,本文提出一种深度迁移学习模型,将大规模有标签细粒度数据集上学习到的图像特征有效地迁移至微型细粒度数据集中。首先,通过衔接域定量计算域间任务的关联度。然后,根据关联度选择适合目标域的迁移特征。最后,使用细粒度数据集视图类标签进行辅助学习,通过联合学习所有属性来获取更多的特征表示。实验表明,本文方法不仅可以获得较高精度,而且能够有效减少模型训练时间,同时也验证了进行域间特征迁移可以加速网络学习与优化这一结论。     

冲击动力学反问题及其反演方法综述

随着计算机技术的快速发展,深度学习在工程领域的应用越来越广泛。在实际应用中,用于训练的数据集往往具有“小样本”、“高维度”、“稀疏”等特征,这导致传统深度学习模型的适用范围十分有限。该文建立了一种基于迁移学习增强的物理信息神经网络模型,用于解决数据稀疏的力学正、反问题。结合迁移学习策略,利用源模型中已有知识来加强目标任务中的学习,从而提高学习的效率,实现不需要大量数据就能得到较好预测性能的目标。该方法在薄板(两端简支+两端固支)的数据集上训练源模型,基于深度迁移学习从源模型上提取神经网络特征;利用目标任务中稀疏数据集实现源模型的微调,进而对不同边界的薄板响应预测(正问题)和边界识别(反问题)的目标任务进行验证。研究结果表明,该方法在小样本的目标任务上具有良好的精度和泛化能力。相比数据驱动的深度学习模型,物理信息神经网络模型可以有效避免数据生成带来的成本和网格独立性等问题。     

Sentiment Analysis Method Based on Kmeans and Online Transfer Learning

Sentiment analysis is a research hot spot in the field of natural language processing and content security. Traditional methods are often difficult to handle the problems of large difference in sample distribution and the data in the target domain is transmitted in a streaming fashion. This paper proposes a sentiment analysis method based on Kmeans and online transfer learning in the view of fact that most existing sentiment analysis methods are based on transfer learning and offline transfer learning. We first use the Kmeans clustering algorithm to process data from one or multiple source domains and select the data similar to target domain data to establish the classifier, so that the processed data does not negatively transfer the data in the target domain. And then create a new classifier based on the new target domain. The source domain classifier and target domain classifier are combined with certain weights by using the homogeneous online transfer learning method to achieve sentiment analysis. The experimental results show that this method has achieved better performance in terms of error rate and classification accuracy.     

深度置信网络迁移学习的行星齿轮箱故障诊断方法

实际工程中行星齿轮箱受工况、运行情况等因素的影响,获取的数据难以满足训练和测试数据独立同分布且训练数据充足的条件,直接影响故障诊断效果.为此,提出一种深度置信网络(Deep Belief Network,DBN)迁移学习的行星齿轮箱故障诊断方法.首先,将辅助标记数据的原始信号频谱作为DBN网络的输入,逐层更新网络的权重...     

医用内窥镜光能传递效率的评价方法

建立医用硬性内窥镜光能传递效率的评价方法.通过影响光能传递效率的原因分析并结合光能传递效率的实际应用意义,提出了有效光度率定义和表达式,以表示医用硬性内窥镜的光能传递效率.运用光度学原理导出有效光度率测量的应用公式,并通过对相关的模拟视场面与光度量关系的理论推导,得出有效光度率测量的应用公式与模拟视场形状无关的结果,并进一步采用几个典型视场面下的实际测量,验证了该应用公式的通用性.此方法可用于医用硬性内窥镜光能传递效率的评价,便于实际测量应用.     

基于多特征提取和改进马田系统的滚动轴承故障分类方法研究

为提高旋转机械的使用效率,及时识别滚动轴承的潜在故障,提出一种基于多特征提取和改进马田系统(MTS)的故障分类方法。通过时域、频域和自适应白噪声的完备经验模态分解(CEEMDAN)提取多维特征,构建初始特征集。结合马田系统和有向非循环图(DAG)的特点,构建DAG-MTS多分类模型,并将其运用到轴承故障诊断中。利用滚动轴承故障数据测试该模型的有效性和实用性,结果表明,该模型能够准确识别出滚动轴承的故障。     

Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance. (2) When utilizing multi-view data, we embed view weighting and manifold regularization into the transfer framework to measure the views’ strengths and weaknesses and improve generalization ability. In the experimental studies, 12 different simulated multi-view & transfer scenarios are constructed from epileptic EEG signals licensed and provided by the University of Bonn, Germany. Extensive experimental results show that MVTL-LSR outperforms baselines. The source code will be available on .     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.