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Clustering for Bivariate Functional Data

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Acta Mathematicae Applicatae Sinica 2024年第3期40卷 613-629页

作者： Shi-yun CAO yan-qiu ZHOU yan-ling wan Tao ZHANG School of Science Guangxi University of Science and TechnologyLiuzhou 545006China School of Marxism Guangxi University of Science and TechnologyLiuzhou 545006China Key Laboratory of Intelligent Information Processing and Graph Processing Guangxi University of Science and TechnologyLiuzhou 545006China

In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each *** k-centres surface clustering method based on marginal functional principal component analysis is proposed for the bivariate functional data,and a novel clustering criterion is presented where both the random surface and its partial derivative function in two directions are *** addition,we also consider two other clustering methods,k-centres surface clustering methods based on product functional principal component analysis or double functional principal component *** results indicate that the proposed methods have a nice performance in terms of both the correct classification rate and the adjusted rand *** approaches are further illustrated through empirical analysis of human mortality data.

关键词： bivariate functional data -centres surface clustering functional principal component analysis partial derivative function

Deep density estimation via invertible block-triangular mapping

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Theoretical & Applied Mechanics Letters 2020年第3期10卷 143-148页

作者： Keju tang Xiaoliang wan Qifeng Liao School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Department of Mathematics and Center for Computation and Technology Louisiana State UniversityBaton RougeLA 70803USA

In this work,we develop an invertible transport map,called KRnet,for density estimation by coupling the Knothe–Rosenblatt(KR)rearrangement and the flow-based generative model,which generalizes the real-valued non-volume preserving(real NVP)model(arX-iv:1605.08803v3).The triangular structure of the KR rearrangement breaks the symmetry of the real NVP in terms of the exchange of information between dimensions,which not only accelerates the training process but also improves the accuracy *** have also introduced several new layers into the generative model to improve both robustness and effectiveness,including a reformulated affine coupling layer,a rotation layer and a component-wise nonlinear invertible *** KRnet can be used for both density estimation and sample generation especially when the dimensionality is relatively *** experiments have been presented to demonstrate the performance of KRnet.

关键词： Deep learning Density estimation Optimal transport Uncertainty quantification

Locomotion Optimization and Manipulation Planning of a Tetrahedron-Based Mobile Mechanism with Binary Control

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Chinese Journal of Mechanical Engineering 2018年第1期31卷 78-99页

作者： Ran Liu yan-An Yao wan Ding Xiao-Ping Liu School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University Department of Mechanism Theory and Dynamics of Machines RWTH Aachen University Department of Systems and Computer Engineering Carleton University

Locomotion and manipulation optimization is essential for the performance of tetrahedron-based mobile mechanism. Most of current optimization methods are constrained to the continuous actuated system with limited degree of freedom(DOF), which is infeasible to the optimization of binary control multi-DOF system. A novel optimization method using for the locomotion and manipulation of an 18 DOFs tetrahedron-based mechanism called 5-TET is proposed. The optimization objective is to realize the required locomotion by executing the least number of *** control strategy is adopted, and forward kinematic and tipping dynamic analyses are performed, *** on a developed genetic algorithm(GA), the optimal number of alternative struts between two adjacent steps is obtained as 5. Finally, a potential manipulation function is proposed, and the energy consumption comparison between optimal 5-TET and the traditional wheeled robot is carried out. The presented locomotion optimization and manipulation planning enrich the research of tetrahedron-based mechanisms and provide the instruction to the successive locomotion and operation planning of multi-DOF mechanisms.

关键词： Tetrahedron-based mobile mechanism Binary control GA Locomotion optimization Manipulation planning

Numerical Investigation on Wet Steam Non-equilibrium Condensation Flow in Turbine Cascade

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Journal of Thermal Science 2012年第6期21卷 525-532页

作者： JU Feng-ming yan Pei-gang HAN wan-jin School of Energy Science & Engineering Harbin Institute of Technology Harbin 150001 China

Based on the two-phase wet steam flow with spontaneous condensation, experimental verification and flow analysis on nozzle and 2D cascade are carried out. The 3D Reynolds-Averaged gas-liquid two-phase flow control equation solver is explored with k-e-kp turbulence model. Furthermore, 3D flow numerical simulation on the last stage stator of the steam turbine is carried out. The results show that a sudden pressure rise on blade suction surface is mainly caused by the droplet growth in condensation flow. The more backward the condensation position is in cascade passage, the less the sudden pressure rise from condensation is, and the larger the nucleation rate is, the maximum under-cooling and the number of droplets per unit volume are. Interaction of condensation wave and shock wave has imposed greater influence on the parameters of the blade cascade outlet.

关键词： turbine cascade wet steam non-equilibrium condensation two-phase flow

Microstructure Evolution and Strain-Dependent Constitutive Modeling to Predict the Flow Behavior of 20Cr–24Ni–6Mo Super-Austenitic Stainless Steel During Hot Deformation

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Acta Metallurgica Sinica(English Letters) 2018年第4期31卷 401-414页

作者： yan-Sen Hao wan-Chun Liu Zhen-Yu Liu State Key Laboratory of Rolling and Automation Northeastern University

Hot compression tests were carried out with specimens of 20Cr-24Ni-6Mo super-austenitic stainless steel at strain rate from 0.01 to 10 s^-1 in the temperature range from 950 to 1150 ℃,and flow behavior was *** analysis indicated that dynamic recrystallization（DRX）behavior was more sensitive to the temperature than strain rate,and full DRX was obtained when the specimen deformed at 1150℃.When the temperature reduced to 1050 ℃,full DRX was completed at the highest strain rate 10 s-l rather than at the lowest strain rate 0.01 s-1 because the adiabatic heating was pronounced at higher strain *** addition,flow behavior reflected in flow curves was inconsistent with the actual microstructural evolution during hot deformation,especially at higher strain rates and lower ***,flow curves were revised in consideration of the effects of adiabatic heating and friction during hot *** results showed that adiabatic heating became greater with the increase of strain level,strain rate and the decrease of temperature,while the frictional effect cannot be neglected at high strain ***,based on the revised flow curves,strain-dependent constitutive modeling was developed and verified by comparing the predicted data with the experimental data and the modified *** result suggested that the developed constitutive modeling can more adequately predict the flow behavior reflected by corrected flow curves than that reflected by experimental flow curves,even though some difference existed at 950℃ and 0.01 s^-*** main reason was that plenty of precipitates generated at this deformation condition and affected the DRX behavior and deformation behavior,eventually resulted in dramatic increase of deformation resistance.

关键词： Super-austenitic stainless steel Hot compression Adiabatic heating Constitutive modeling Microstructure evolution

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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Chinese Journal of Mechanical Engineering 2017年第4期30卷 782-795页

作者： wan Zhang Min-Ping Jia Lin Zhu Xiao-An yan School of Mechanical Engineering Southeast UniversityNanjing 211189 China

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

关键词： Computational intelligence Machinerycondition monitoring Fault diagnosis Neural networkFuzzy logic Support vector machine - Evolutionaryalgorithms

Coupled KdV Equations and Their Explicit Solutions Through Two-Dimensional Hamiltonian System with a Quartic Potential

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Communications in Theoretical Physics 2008年第6期49卷 1379-1382页

作者： CAO Jian-Li ZHANG Hua JIAO wan-tang College of Science Henan University of Technology Zhengzhou 450052 China Henan Economy and Trade Vocational College Zhengzhou 450053 China

Based on the second integrable ease of known two-dimensional Hamiltonian system with a quartie potentiM, we propose a 4 × 4 matrix speetrM problem and derive a hierarchy of coupled KdV equations and their Hamiltonian structures. It is shown that solutions of the coupled KdV equations in the hierarchy are reduced to solving two compatible systems of ordinary differentiM equations. As an application, quite a few explicit solutions of the coupled KdV equations are obtained via using separability for the second integrable ease of the two-dimensional Hamiltonian system.

关键词： coupled KdV equations integrable system explicit solution

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

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Chinese Journal of Mechanical Engineering 2005年第4期18卷 592-596页

作者： wan Shuting Li Heming Li Yonggang tang Guiji College of Mechanical Engineering North China Electric Power University Baoding 071003 China College of Electrical Engineering North China Electric Power University Baoding 071003 China

Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

关键词： Generator Fault diagnosis Rotor vibration characteristic Stator winding parallel branches circulating current

Machine learning assisted adsorption performance evaluation of biochar on heavy metal

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 29-42页

作者： Qiannan Duan Pengwei yan Yichen Feng Qianru wan Xiaoli Zhu Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity College of Urban and Environmental SciencesNorthwest UniversityXi’an710127China Department of Environment Science Shaanxi Normal UniversityXi’an710062China

Heavy metals(HMs)represent pervasive and highly toxic environmental pollutants,known for their long latency periods and high toxicity levels,which pose significant challenges for their removal and ***,the removal of heavy metals from the environment is crucial to ensure the water *** materials,known for their intricate pore structures and abundant oxygen-containing functional groups,are frequently harnessed for their effectiveness in mitigating heavy metal ***,conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and *** address this challenge,this paper proposes a data-driven machine learning(ML)approach to identify the optimal biochar preparation and adsorption reaction conditions,with the ultimate goal of maximizing their adsorption *** utilizing a data set comprising 476 instances of heavy metal absorption by biochar,seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by *** predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction *** results demonstrate that the stacking model,which integrates multiple algorithms,allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.

关键词： Machine learning Biochar Heavy metal Adsorption efficiency