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Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

《信息与电子工程前沿（英文）》 2015年第16卷第5期页码 358-366 doi: 10.1631/FITEE.1400323

摘要： Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including -means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

关键词： Speech emotion recognition Unsupervised feature learning Neural network Affect computing

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联合局部学习和组稀疏回归的无监督特征选择 Regular Papers

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

《信息与电子工程前沿（英文）》 2019年第20卷第4期页码 538-553 doi: 10.1631/FITEE.1700804

摘要：近十年，特征选择备受关注。通过挑选特征子集，可有效提升学习算法效率。由于难以获取标签信息，无监督特征选择算法相较于有监督特征选择算法应用更为广泛，其关键在于找出更能反映数据分布的特征集合。由于数据集中存在冗余和噪声，使用全部特征并不能很好展现数据的真实分布。为解决这一问题，本文提出联合局部学习和组稀疏回归的无监督特征选择算法。将基于局部学习聚类方法与组稀疏回归算法有机整合，选出有效反映数据流形分布同时保持组稀疏结构的特征。通过迭代算法，回归系数汇聚到重要特征上，选出能得到更优聚类效果的特征。对多个实际数据集（图像、声音和网页）的实验证明了该算法的有效性。

关键词：无监督；局部学习；组稀疏回归；特征选择

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

《机械工程前沿（英文）》 2021年第16卷第4期页码 829-839 doi: 10.1007/s11465-021-0652-4

摘要： Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

关键词： imbalanced fault diagnosis graph feature learning rotating machinery autoencoder

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

《能源前沿（英文）》 2023年第17卷第4期页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection unary classification self-supervised representation learning multivariate nonlinear time series

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联邦无监督表示学习 Research Article

张凤达1,况琨1,陈隆1,游兆阳1,沈弢1,肖俊1,张寅1,吴超2,吴飞1,庄越挺1,李晓林3,4,5

《信息与电子工程前沿（英文）》 2023年第24卷第8期页码 1181-1193 doi: 10.1631/FITEE.2200268

摘要：为利用分布式边缘设备上大量未标记数据，我们在联邦学习中提出一个称为联邦无监督表示学习(FURL)的新问题，以在没有监督的情况下学习通用表示模型，同时保护数据隐私。FURL提出了两个新挑战：（1）客户端之间的数据分布转移（非独立同分布）会使本地模型专注于不同的类别，从而导致表示空间的不一致；（2）如果FURL中客户端之间没有统一的信息，客户端之间的表示就会错位。为了应对这些挑战，我们提出带字典和对齐的联合对比平均(FedCA)算法。FedCA由两个关键模块组成：字典模块，用于聚合来自每个客户端的样本表示并与所有客户端共享，以实现表示空间的一致性；对齐模块，用于将每个客户端的表示与基于公共数据训练的基础模型对齐。我们采用对比方法进行局部模型训练，通过在3个数据集上独立同分布和非独立同分布设定下的大量实验，我们证明FedCA以显著的优势优于所有基线方法。

关键词：联邦学习；无监督学习；表示学习；对比学习

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

《结构与土木工程前沿（英文）》 2015年第9卷第1期页码 1-16 doi: 10.1007/s11709-014-0277-3

摘要： A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

关键词： structural health monitoring early-damage detection principal component analysis symbolic data symbolic dissimilarity measures cluster analysis numerical model damage simulations

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Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

《能源前沿（英文）》 2020年第14卷第4期页码 817-835 doi: 10.1007/s11708-020-0709-9

摘要： Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.

关键词： gas turbine dynamic simulation data-driven transfer learning feature similarity

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BUEES: a bottom-up event extraction system

Xiao DING,Bing QIN,Ting LIU

《信息与电子工程前沿（英文）》 2015年第16卷第7期页码 541-552 doi: 10.1631/FITEE.1400405

摘要： Traditional event extraction systems focus mainly on event type identification and event participant extraction based on pre-specified event type paradigms and manually annotated corpora. However, different domains have different event type paradigms. When transferring to a new domain, we have to build a new event type paradigm and annotate a new corpus from scratch. This kind of conventional event extraction system requires massive human effort, and hence prevents event extraction from being widely applicable. In this paper, we present BUEES, a bottom-up event extraction system, which extracts events from the web in a completely unsupervised way. The system automatically builds an event type paradigm in the input corpus, and then proceeds to extract a large number of instance patterns of these events. Subsequently, the system extracts event arguments according to these patterns. By conducting a series of experiments, we demonstrate the good performance of BUEES and compare it to a state-of-the-art Chinese event extraction system, i.e., a supervised event extraction system. Experimental results show that BUEES performs comparably (5% higher -measure in event type identification and 3% higher -measure in event argument extraction), but without any human effort.

关键词： Event extraction Unsupervised learning Bottom-up

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无监督域自适应的动态参数化学习 Research Article

蒋润华1,2,韩亚洪1,2

《信息与电子工程前沿（英文）》 2023年第24卷第11期页码 1616-1632 doi: 10.1631/FITEE.2200631

摘要：无监督领域自适应通过学习域不变表示实现神经网络从有标签数据组成的源域到无标签数据组成的目标域迁移。近期研究通过直接匹配这两个域的边缘分布实现这一目标。然而，已有研究大多数忽略域对齐和语义判别学习之间的动态平衡，因此容易受负迁移和异常样本影响。为解决这些问题，引入动态参数化学习框架。首先，通过探索领域级语义知识，提出动态对齐参数自适应地调整域对齐和语义判别学习的优化过程。此外，为获得判别能力强和域不变的表示，提出在源域和目标域上对齐优化过程。本文通过综合实验证明了所提出方法的有效性，并在3个视觉任务的7个数据集上进行广泛比较，证明可行性。

关键词：无监督领域自适应；优化步骤；跨域判别表示；语义判别

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基于两级层次特征学习的图像分类方法 Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

《信息与电子工程前沿（英文）》 2016年第17卷第9期页码 897-906 doi: 10.1631/FITEE.1500346

摘要：概要：在图像分类任务中，不同类别之间的相似度是不同的，样本经常被误分到相似度较高的类别中。为了区分高度相似类别中的样本，需要更加具体的图像特征，以便于分类器能够提高分类性能。本文提出了一种新颖、有效的基于深度卷积神经网络的两级层次特征学习框架。首先，不同层次的深度特征抽取器使用迁移学习方法进行训练。然后，从全部类别中抽取的通用特征和从高度相似类别中抽取的具体特征被融合成一个特征向量，并将其输入线性分类器进行分类。最后，基于Caltech-256、Oxford Flower-102和Tasmania Coral Point Count三个图像数据集的实验证明，通过两级层次特征学习的深度特征的表达能力十分强大，与传统的扁平多分类方法相比，我们提出的方法能有效的提高分类精度。

关键词：迁移学习；特征学习；深度卷积神经网络；层次分类；谱聚类

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针对无监督域自适应问题的深度逐层领域修正算法 Article

Shuang LI, Shi-ji SONG, Cheng WU

《信息与电子工程前沿（英文）》 2018年第19卷第1期页码 91-103 doi: 10.1631/FITEE.1700774

摘要：深度神经网络凭借强大的特征抽象能力，已成功应用在机器学习的多个领域。然而，传统深度网络假设训练样本和测试样本来自同一分布，这一假设在很多实际应用中并不成立。为借助深度网络解决领域偏移问题，本文提出逐层领域修正（layer-wise domain correction, LDC）深度域自适应算法。该算法通过在已有深度网络中增加领域修正层，将源域网络成功适配到目标领域。逐层增加的领域修正层能够将两个领域特征的最大均值偏差（maximum mean discrepancy, MMD）距离最小化，从而完美匹配源域和目标域样本的特征表示。与此同时，网络深度的增加极大提高了网络表达能力。LDC算法不需要目标领域有标记样本，在几个跨领域分类识别数据集都取得了当时最好结果，且其训练比已有深度域自适应算法快近10倍。

关键词：无监督域自适应；最大均值偏差；残差网络；深度学习

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微阵列数据集的特征选择技术：综合评述、分类和未来方向 Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI

《信息与电子工程前沿（英文）》 2022年第23卷第10期页码 1451-1478 doi: 10.1631/FITEE.2100569

摘要：

为获得最佳结果，从微阵列数据集中检索相关特征已成为特征选择（FS）技术的研究热点。本综述旨在全面阐述各种最新特征选择技术，同时介绍了基于微阵列数据集的处理多类分类问题的技术以及提高学习算法性能的不同方法。我们试图理解和解决数据集不平衡问题，以证实研究人员在微阵列数据集上的工作。对文献的分析为理解和强调在通过各种特征选择技术寻找最佳特征子集时存在的众多挑战和问题铺平了道路。同时提供了一个案例说明该方法的实施过程，该方法使用3个微阵列癌症数据集评估一些包装方法和混合方法的分类精度和收敛能力，以确认最优特征子集。

关键词：特征选择；高维；学习技术；微阵列数据集

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Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

《机械工程前沿（英文）》 2023年第18卷第1期 doi: 10.1007/s11465-022-0725-z

摘要： As parameter independent yet simple techniques, the energy operator (EO) and its variants have received considerable attention in the field of bearing fault feature detection. However, the performances of these improved EO techniques are subjected to the limited number of EOs, and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction. As a result, the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises. To address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively. Specifically, the proposed strategy is conducted through the following three steps. First, a multi-dimensional information matrix (MDIM) is constructed by performing the higher order energy operator (HOEO) on the analysis signal iteratively. MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region. Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses. Third, the intrinsic manifolds are weighted to recover the fault-related transients. Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and the empirical mode decomposition.

关键词： higher order energy operator fault diagnosis manifold learning rolling element bearing information fusion

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Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

《机械工程前沿（英文）》 2017年第12卷第3期页码 333-347 doi: 10.1007/s11465-017-0435-0

摘要：

The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.

关键词： joint subspace learning multiple fault diagnosis sparse decomposition theory coupling feature separation wind turbine gearbox

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种基于特征选择与迁移学习的度量补偿软件缺陷预测方法 Research Article

陈锦富1,2,王小丽1,2,蔡赛华1,2,徐家平1,陈静怡1,陈海波1

《信息与电子工程前沿（英文）》 2022年第23卷第5期页码 715-731 doi: 10.1631/FITEE.2100468

摘要：跨项目软件缺陷预测解决了传统缺陷预测中训练数据不足的问题，克服了将多个不同源项目中学习的模型应用于目标项目的挑战。与此同时，出现两个新问题：（1）模型训练过程中过多无关和冗余特征影响训练效率，降低了模型预测精度；（2）由于开发环境等因素，度量值的分布因项目而异，当模型用于跨项目预测时，预测精度较低。本文引入皮尔逊特征选择方法解决数据冗余问题，采用基于迁移学习的度量补偿技术解决源项目和目标项目之间数据分布差异较大的问题。提出一种基于特征选择和迁移学习的度量补偿软件缺陷预测方法。实验结果表明，用该方法构建的模型在AUC（接收器工作特性曲线下面积）值和F1度量指标上取得较好结果。

关键词：缺陷预测；特征选择；迁移学习；度量补偿

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标题作者时间类型操作

Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

期刊论文

联合局部学习和组稀疏回归的无监督特征选择

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

期刊论文

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

期刊论文

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and