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正交异性钢桥面板横梁切口裂纹的Lamb波监测分析

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Journal of Southeast University(English Edition) 2024年第1期40卷 24-32页

作者：石林泽程斌向升赵启斌上海交通大学船舶海洋与建筑工程学院上海200240 上海交通大学海洋工程国家重点实验室上海200240 上海交通大学上海市公共建筑和基础设施数字化运维重点实验室上海200240 RIKEN Center for Advanced Intelligence Project Saitama 351-0198Japan

为探究钢桥面板横梁切口裂纹的Lamb波技术监测效果,设计了2个钢桥面板足尺模型,通过循环加载使其横梁切口处发生疲劳开裂,利用Lamb波监测裂纹扩展情况,进而从波信号中提取标准化特征,用于评估裂纹扩展.建立并验证了钢桥面板有限元模型,... 详细信息

为探究钢桥面板横梁切口裂纹的Lamb波技术监测效果,设计了2个钢桥面板足尺模型,通过循环加载使其横梁切口处发生疲劳开裂,利用Lamb波监测裂纹扩展情况,进而从波信号中提取标准化特征,用于评估裂纹扩展.建立并验证了钢桥面板有限元模型,进行了裂纹长度、传感器位置等各种参数分析,以确定最佳传感器布置.试验结果表明:当裂纹扩展长度与波传播路径垂直时,标准化特征增大;当两者路径平行时,标准化特征减小.参数化分析结果确定了切口处裂纹监测的最佳传感器位置,建议在选定横梁切口区域,预先布置1个激励器和4个接收器,激励器位于距切口50 mm处,接收器位于距离切口50 mm处或距顶板50~100 mm处.

关键词：正交异性钢桥面板横梁切口疲劳裂纹 Lamb波监测传感器布置

Semi-supervised non-negative Tucker decomposition for tensor data representation

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Science China(Technological Sciences) 2021年第9期64卷 1881-1892页

作者： QIU YuNing ZHOU GuoXu CHEN XinQi ZHANG DongPing zhao XinHai zhao qibin School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China

Non-negative Tucker decomposition(NTD) has been developed as a crucial method for non-negative tensor data ***, NTD is essentially an unsupervised method and cannot take advantage of label information. In this paper, we claim that the low-dimensional representation extracted by NTD can be treated as the predicted soft-clustering coefficient matrix and can therefore be learned jointly with label propagation in a unified framework. The proposed method can extract the physicallymeaningful and parts-based representation of tensor data in their natural form while fully exploring the potential ability of the given labels with a nearest neighbors graph. In addition, an efficient accelerated proximal gradient(APG) algorithm is developed to solve the optimization problem. Finally, the experimental results on five benchmark image data sets for semi-supervised clustering and classification tasks demonstrate the superiority of this method over state-of-the-art methods.

关键词： tensor factorization non-negative Tucker decomposition semi-supervised learning label propagation

EEG-based asynchronous BCI control of a car in 3D virtual reality environments

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Chinese Science Bulletin 2009年第1期54卷 78-87页

作者： zhao qibin ZHANG LiQing CICHOCKI Andrzej Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Saitama 351-0198 Japan

Brain computer interface (BCI) aims at creating new communication channels without depending on brain’s normal output channels of peripheral nerves and muscles. However,natural and sophisticated interactions manner between brain and computer still remain challenging. In this paper,we investigate how the duration of event-related desynchronization/synchronization (ERD/ERS) caused by motor im-agery (MI) can be modulated and used as an additional control parameter beyond simple binary deci-sions. Furthermore,using the non-time-locked properties of sustained (de)synchronization,we have developed an asynchronous BCI system for driving a car in 3D virtual reality environment (VRE) based on cumulative incremental control strategy. The extensive real time experiments confirmed that our new approach is able to drive smoothly a virtual car within challenging VRE only by the MI tasks with-out involving any muscular activities.

关键词：大脑计算机界面脑电图去同步化频率

Robust tensor decomposition via orientation invariant tubal nuclear norms

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Science China(Technological Sciences) 2022年第6期65卷 1300-1317页

作者： WANG AnDong zhao qibin JIN Zhong LI Chao ZHOU GuoXu School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Tensor Learning Team RIKEN Center for Advanced Intelligence ProjectTokyo 103-0027Japan Key Laboratory of Intelligent Perception and System for High-Dimensional Information Ministry of EducationNanjing 210094China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China

Aiming at recovering an unknown tensor(i.e.,multi-way array)corrupted by both sparse outliers and dense noises,robust tensor decomposition(RTD)serves as a powerful pre-processing tool for subsequent tasks like classification and target detection in many computer vision and machine learning ***,tubal nuclear norm(TNN)based optimization is proposed with superior performance as compared with other tensorial nuclear norms for tensor ***,one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple *** this end,we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way(K≥3)*** further formulate two RTD models via the proposed norms and develop two algorithms as the ***,we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation *** on real-world datasets demonstrate the superiority and effectiveness of the proposed norms.

关键词： tensor recovery t-SVD estimation error tensor completion

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

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自动化学报:英文版 2023年第11期10卷 2154-2167页

作者： Haonan Huang Guoxu Zhou Naiyao Liang qibin zhao Shengli Xie School of Automation Guangdong University of Technologyand also with the Key Laboratory of Intelligent Detection and the Internet of Things in ManufacturingMinistry of EducationGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangzhou 510006China RIKEN AIP Japanand also with the School of AutomationGuangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology(GDUT) and also with the Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangdong University of TechnologyGuangzhou 510006China IEEE

Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary *** this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of *** regularization is utilized to preserve the high-order geometry structure of data in each *** efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence *** results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.

关键词： Deep matrix factorization(DMF) diversity hypergraph regularization multi-view data representation(MDR)

Semi-supervised multi-view clustering with dual hypergraph regularized partially shared non-negative matrix factorization

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Science China(Technological Sciences) 2022年第6期65卷 1349-1365页

作者： ZHANG DongPing LUO YiHao YU YuYuan zhao qibin ZHOU GuoXu School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Center for Advanced Intelligence Project(AIP) RIKENTokyo 103-0027Japan

Real-world data can often be represented in multiple forms and views,and analyzing data from different perspectives allows for more comprehensive learning of the data,resulting in better data clustering ***-negative matrix factorization(NMF)is used to solve the clustering problem to extract uniform discriminative low-dimensional features from multi-view *** clustering methods based on graph regularization have been proposed and proven to be effective,but ordinary graphs only consider pairwise relationships between *** order to learn the higher-order relationships that exist in the sample manifold and feature manifold of multi-view data,we propose a new semi-supervised multi-view clustering method called dual hypergraph regularized partially shared non-negative matrix factorization(DHPS-NMF).The complex manifold structure of samples and features is learned by constructing samples and feature *** improve the discrimination power of the obtained lowdimensional features,semi-supervised regression terms are incorporated into the model to effectively use the label information when capturing the complex manifold structure of the ***,we conduct experiments on six real data sets and the results show that our algorithm achieves encouraging results in comparison with some methods.

关键词： multi-view clustering semi-supervised learning nonnegative matrix factorization(NMF) dual hypergraph

Multidimensional clinical data denoising via Bayesian CP factorization

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Science China(Technological Sciences) 2020年第2期63卷 249-254页

作者： CUI GaoChao ZHU Li GUI LiHua zhao qibin ZHANG JianHai CAO JianTing Graduate School of Engineering Saitama Institute of TechnologySaitama 3690293Japan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Cognitive Science Department School of Information Science and EngineeringXiamen UniversityXiamen 361005China Tensor Learning Unit Center for Advanced Intelligence ProjectRIKENSaitama 1030027Japan School of Automation Guangdong University of TechnologyGuangzhou 510006China

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) Bayesian tensor factorization CANDECOMP/PARAFAC(CP)

生物刺激素对烤烟根际微生态及烟叶品质的影响

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西北农林科技大学学报（自然科学版） 2024年第10期52卷 59-68页

作者：王政欧清华冯烨君吴彪赵岂彬邓泳李博敖金成广西中烟工业有限责任公司广西南宁530001 云南省烟草公司罗平县分公司云南曲靖655800 云南农业大学植物保护学院云南昆明650201

【目的】探究生物刺激素在烤烟栽培上的应用效果,为植烟土壤改良及烟叶品质改善提供新的思路。【方法】采用大田试验,以常规施肥为对照(CK),在CK基础上设施用海藻酸(BS1)、古生物源生物刺激素(BS2)和矿源黄腐酸(BS3)3个生物刺激素处理,... 详细信息

【目的】探究生物刺激素在烤烟栽培上的应用效果,为植烟土壤改良及烟叶品质改善提供新的思路。【方法】采用大田试验,以常规施肥为对照(CK),在CK基础上设施用海藻酸(BS1)、古生物源生物刺激素(BS2)和矿源黄腐酸(BS3)3个生物刺激素处理,于烤烟盛花期取烟株根围土壤测定土壤理化性质(pH、阳离子交换量及有机质、碱解氮、速效磷、速效钾含量)、酶(蔗糖酶、硝酸还原酶、过氧化氢酶、脲酶)活性及微生物群落多样性;于烤烟成熟时调查农艺性状(株高、茎围、有效叶片数、最大腰叶长、最大腰叶宽、顶叶长、顶叶宽、叶面积),并对上部叶(倒数第5~6片烟叶)进行标识,调制后进行化学成分(总糖、还原糖、烟碱、总氮、钾、氯、淀粉含量,糖碱比、氮碱比、钾氯比)测算,分析3种生物刺激素对植烟土壤微生态环境及烟叶品质的影响。【结果】与CK相比,BS1和BS3处理不同程度提高了植烟土壤氮、磷、钾速效养分含量及阳离子交换量,极显著提高了土壤蔗糖酶和脲酶活性;明显提高土壤优势菌门奇古菌门(Thaumarchaeota)和根肿黑粉菌门(Entorrhizomycota)的相对丰度,以及优势菌属根肿黑粉菌属(Entorrhiza)的相对丰度,而降低子囊菌门(Ascomycota)和镰刀菌属(Fusarium)的相对丰度;BS3处理土壤细菌、真菌群落的丰富度和多样性及其功能代谢途径丰度均高于CK、BS1和BS2处理;施用生物刺激素可促进烤烟生长发育和烟叶开片,其中BS3处理有利于提高烟叶化学成分适宜性和协调性。【结论】矿源黄腐酸处理改良土壤、促进烤烟生长及提升烟叶品质的综合效应较优,是适合烤烟施用的生物刺激素。

关键词：生物刺激素植烟土壤根际微生态烟草栽培烟叶品质

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ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines

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ECG Feature Extraction and Classification Using Wavelet Tran...