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Applied Mathematics and Mechanics(English Edition) 2023年第7期44卷 1039-1068页

作者： W.WU M.DANEKER M.A.JOLLEY K.T.TURNER L.LU Department of Anesthesiology and Critical Care Medicine Children's Hospital of PhiladelphiaPhiladelphiaPA 19104U.S.A. Division of Pediatric Cardiology Children's Hospital of PhiladelphiaPhiladelphiaPA 19104U.S.A. Department of Chemical and Biomolecular Engineering University of PennsylvaniaPhiladelphiaPA 19104U.S.A. Department of Mechanical Engineering and Applied Mechanics University of PennsylvaniaPhiladelphiaPA 19104U.S.A.

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical ***,material identification is a challenging task,especially when the characteristic of the material is highly nonlinear in nature,as is common in biological *** this work,we identify unknown material properties in continuum solid mechanics via physics-informed neural networks(PINNs).To improve the accuracy and efficiency of PINNs,we develop efficient strategies to nonuniformly sample observational *** also investigate different approaches to enforce Dirichlet-type boundary conditions(BCs)as soft or hard ***,we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material *** estimated material parameters achieve relative errors of less than 1%.As such,this work is relevant to diverse applications,including optimizing structural integrity and developing novel materials.

关键词： solid mechanics material identification physics-informed neural network(PINN) data sampling boundary condition(BC)constraint

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Lag-Consensus of Leader-Following Multiagent Systems by Pinning Control and data sampling

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Lag-Consensus of Leader-Following Multiagent Systems by Pinn...

A Survey of data Partitioning and sampling Methods to Support Big data Analysis

第40届中国控制会议

作者： Tingjin Liu Duyu Liu Xingwen Liu Key Laboratory of Electronic Information of State Ethnic Affairs Commission College of Electrical EngineeringSouthwest Minzu University College of Electrical Engineering Southwest Minzu University

This paper concerns about lag-consensus of linear multiagent systems employing pinning control. A periodic data sampling method is used to design the leader-following lag-consensus control law. A sufficient condition of lag-consensus is presented by constructing a delay-dependent Lyapunov-Krasovskii functional. The feedback control gain matrix is designed by solving linear matrix inequalities. Finally, a numerical example is provided to demonstrate the proposed theoretical result.

关键词： Lag-consensus multiagent systems data sampling pinning control

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Big data Mining and Analytics 2020年第2期3卷 85-101页

作者： Mohammad Sultan Mahmud Joshua Zhexue Huang Salman Salloum Tamer Z.Emara Kuanishbay Sadatdiynov National Engineering Laboratory for Big data System Computing Technology Shenzhen UniversityUniversityShenzhen 518060China Big data Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China.

Computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and *** cluster computing,data partitioning and sampling are two fundamental strategies to speed up the computation of big data and increase *** this paper,we present a comprehensive survey of the methods and techniques of data partitioning and sampling with respect to big data processing and *** start with an overview of the mainstream big data frameworks on Hadoop *** basic methods of data partitioning are then discussed including three classical horizontal partitioning schemes:range,hash,and random *** partitioning on Hadoop clusters is also discussed with a summary of new strategies for big data partitioning,including the new Random Sample Partition(RSP)distributed *** classical methods of data sampling are then investigated,including simple random sampling,stratified sampling,and reservoir *** common methods of big data sampling on computing clusters are also discussed:record-level sampling and blocklevel ***-level sampling is not as efficient as block-level sampling on big distributed *** the other hand,block-level sampling on data blocks generated with the classical data partitioning methods does not necessarily produce good representative samples for approximate computing of big *** this survey,we also summarize the prevailing strategies and related work on sampling-based approximation on Hadoop *** believe that data partitioning and sampling should be considered together to build approximate cluster computing frameworks that are reliable in both the computational and statistical respects.

关键词： big data analysis data partitioning data sampling distributed and parallel computing approximate computing

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sampling via the aggregation value for data-driven manufacturing

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National Science Review 2022年第11期9卷 161-171页

作者： Xu Liu Gengxiang Chen Yingguang Li Lu Chen Qinglu Meng Charyar Mehdi-Souzani School of Mechanical and Power Engineering Nanjing Tech University College of Mechanical &Electrical Engineering Nanjing University of Aeronautics and Astronautics University Research Laboratory in Automated Production Ecole normale superieure Paris-SaclayUniversite Paris-SaclayUniversite Sorbonne Paris Nord

data-driven modelling has shown promising potential in many industrial applications, while the expensive and time-consuming labelling of experimental and simulation data restricts its further *** a more informative but smaller dataset to reduce labelling efforts has been a vital research problem. Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem. In this research, the aggregation value is defined using a novel representation for the value of a sample set by modelling the invisible redundant information as the overlaps of neighbouring values. The sampling problem is hence converted to the maximisation of the submodular function over the aggregation value. The comprehensive analysis of several manufacturing datasets demonstrates that the proposed method can provide sample sets with superior and stable performance compared with state-of-the-art methods. The research outcome also indicates its appealing potential to reduce labelling efforts for more data-scarcity scenarios.

关键词： data-driven modelling intelligent manufacturing data sampling data value

A systematic framework of constructing surrogate model for slider track peeling strength prediction

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Science China(Technological Sciences) 2024年第10期67卷 3261-3274页

作者： DONG XingJian CHEN Qian LIU WenBo WANG Dong PENG ZhiKe MENG Guang State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China KEIPER Seating Mechanisms Co. Ltd.Soochow 215500China Department of Industrial Engineering and Management Shanghai Jiao Tong UniversityShanghai 200240China

Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety *** methods for determining slider peeling strength are primarily physical testing and numerical ***,these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately ***,the efficient and low-cost surrogate model emerges as a promising ***,currently used surrogate models suffer from inefficiencies and complexity in data sampling,lack of robustness in local model predictions,and isolation between data sampling and model *** overcome these challenges,this paper aims to set up a systematic framework for slider track peeling strength prediction,including sensitivity analysis,dataset sampling,and model ***,the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling *** on the variable sensitivity,a distance metric is constructed to measure the disparity of different variable ***,the sparsity-targeted sampling(STS)is proposed to formulate a representative ***,the sequentially selected local weighted linear regression(SLWLR)is designed to achieve accurate track peeling strength ***,a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a *** results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization ***,the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements,achieving an average absolute error of 3.3 kN in the simulated test dataset.

关键词： slider track peeling strength surrogate model sensitivity analysis data sampling local weighted linear regression

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Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis

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Blockchain(Research and Applications) 2024年第3期5卷 106-122页

作者： Mohammad Hasan Mohammad Shahriar Rahman Helge Janicke Iqbal H.Sarker Department of Computer Science and Engineering Premier University4000 ChitagongBangladesh Department of Computer Science and Engineering United International University1212 DhakaBangladesh Cyber Security Cooperative Research Centre 6027 PerthAustralia Centre for Securing Digital Futures Edith Cowan University6027 PerthAustralia

As the use of blockchain for digital payments continues to rise,it becomes susceptible to various malicious *** detecting anomalies within blockchain transactions is essential for bolstering trust in digital ***,the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit *** several studies have been conducted in the field,a limitation persists:the lack of explanations for the model’s *** study seeks to overcome this limitation by integrating explainable artificial intelligence(XAI)techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin *** shapley additive explanation(SHAP)method is employed to measure the contribution of each feature,and it is compatible with ensemble ***,we present rules for interpreting whether a Bitcoin transaction is anomalous or ***,we introduce an under-sampling algorithm named XGBCLUS,designed to balance anomalous and non-anomalous transaction *** algorithm is compared against other commonly used under-sampling and over-sampling ***,the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble *** experimental results demonstrate that:(i)XGBCLUS enhances true positive rate(TPR)and receiver operating characteristic-area under curve(ROC-AUC)scores compared to state-of-the-art under-sampling and over-sampling techniques,and(ii)our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy,TPR,and false positive rate(FPR)scores.

关键词： Anomaly detection Blockchain Bitcoin transactions data imbalance data sampling Explainable AI Machine learning Decision tree Anomaly rules

Web databases sampling Approach Based on Valid Probability of Query Attributes

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Web Databases Sampling Approach Based on Valid Probability o...