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Low-frequency bandgap and vibration suppression mechanism of a novel square hierarchical honeycomb metamaterial

Applied Mathematics and Mechanics(English Edition) 2024年第10期45卷 1841-1856页

作者： Xingjian DONG Shuo WANG Anshuai WANG Liang WANG Zhaozhan ZHANG Yuanhao TIE Qingyu LIN Yongtao SUN State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong UniversityShanghai 200240China Department of Mechanics Tianjin UniversityTianjin 300350China Tianjin Key Laboratory of Nonlinear Dynamics and Control Tianjin UniversityTianjin 300350China School of Mechanical and Automotive Engineering Guangxi University of Science and TechnologyLiuzhou 545006Guangxi Zhuang Autonomous RegionChina

The suppression of low-frequency vibration and noise has always been an important issue in a wide range of engineering *** address this concern,a novel square hierarchical honeycomb metamaterial capable of reducing low-frequency noise has been *** combining Bloch’s theorem with the finite element method,the band structure is *** results indicate that this metamaterial can produce multiple low-frequency bandgaps within 500 Hz,with a bandgap ratio exceeding 50%.The first bandgap spans from 169.57 Hz to 216.42 *** reveal the formation mechanism of the bandgap,a vibrational mode analysis is *** analysis demonstrates that the bandgap is attributed to the suppression of elastic wave propagation by the vibrations of the structure’s two protruding corners and overall expansion ***,detailed parametric analyses are conducted to investigate the effect ofθ,i.e.,the angle between the protruding corner of the structure and the horizontal direction,on the band structures and the total effective bandgap *** is found that reducingθis conducive to obtaining lower frequency *** propagation characteristics of elastic waves in the structure are explored by the group velocity,phase velocity,and wave propagation ***,the transmission characteristics of a finite periodic structure are investigated *** results indicate significant acceleration amplitude attenuation within the bandgap range,confirming the structure’s excellent low-frequency vibration suppression capability.

关键词： wave propagation vibration suppression metamaterial low-frequency bandgap

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Correction to “A Magnetic Capsule Robot With an Exoskeleton to Withstand Esophageal Pressure and Delivery Drug in Stomach”

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IEEE Robotics and Automation Letters 2025年第4期10卷 4004-4004页

作者： Ruomao Liu Yujun Chen Zhen Yin Jiachen Zhang Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Department of Control Science and Engineering Tongji University Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

The name of the third author in [1] is incorrect. The correct author is shown in the red text highlighted.

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Study on Model of Ash Fouling Monitoring in Rotary Air Preheater of Coal-fired Power Plant Boiler 36

Study on Model of Ash Fouling Monitoring in Rotary Air Prehe...

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第36届中国控制会议

作者： Ziyang Wei Jingcheng Wang Hongyuan Wang Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

ISBN: (纸本)9781538629185

As an important equipment to preheat air and to improve coal-fired power plant boiler efficiency with the heat of boiler tail flue gas,the air preheater's safe and economic operation influences unit efficiency *** present,above 80%utility-type unit in domestic uses tri-sectional rotary air ***,the rotary air preheater has become an indispensable auxiliary machinery in modern large ***,a kind of air preheater heat transfer model of inverse flue gas flow is proposed in this paper,the ash fouling situation of air preheater is showed with the cleanliness factors based on heat transfer model and flue gas pressure drop model ***,the cleanliness factor of air preheater is calculated and gained through thermodynamics indirectly with energy balance *** to the verification results of the actual operation data of a 600 MW coal-fired unit,both of these two kinds of mode can reflect the dynamic change of ash fouling situation of air preheater in real time,and among which,the cleanliness factor based on flue gas pressure drop mode has a better online monitoring result on ash fouling situation of heat transfer surface.

关键词： Coal-fired Power Plant Boiler Tri-sectional Rotary Air Preheater Reverse Flue Gas Flow Ash Fouling Cleanliness Factor

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Robustness of the complex networks by statistical physics method 36

Robustness of the complex networks by statistical physics me...

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第36届中国控制会议

作者： Hai Lin Jincheng Wang Xiaocheng Li Hongyuan Wang Bin Ma Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of China Autonomous Systems and Intelligent Control nternational Joint Research Center Xian Technological University

ISBN: (纸本)9781538629185

In this study,we present a novel analytical approach for robustness on Erdos-Renyi（ER） networks by introducing statistical physics *** establish an exact mapping relation between ER networks and Ising *** on the mapping relation,we obtain the partition function of the ER networks and use it to determine the size of the giant component and the value of the critical edge present probability in ER *** extend the approach to investigate the size of remaining giant component and the critical fraction of the nodes removed in the ER networks whose nodes under the randomly attack.

关键词： Percolation Erdos-Renyi（ER） networks Ising model mapping relation

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Recurrence characteristics analysis of near-Surface wind speed signal with different sampling frequencies

Recurrence characteristics analysis of near-Surface wind spe...

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4th International Conference on Frontiers of Manufacturing Science and Measuring Technology, ICFMM 2014

作者： Zeng, Ming Wu, Zhan Xie Meng, Qing Hao Li, Jing Hai Ma, Shu Gen Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical Engineering and Automation Tianjin University Tianjin 300072 China

ISBN: (纸本)9783038351924

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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Uncertainty and Reliability Analysis of Neural networks Based Soft-sensors with Applications in Wastewater Treatment

Uncertainty and Reliability Analysis of Neural Networks Base...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Yiqi Yuan, Jingyi Huang, Daoping Key Laboratory of Autonomous Systems and Networked Control Ministry of Education The School of Automation Science & Engineering South China University of Technology Guangzhou510640 China

ISBN: (纸本)9781665465335

To overcome the transparency and interpretability issues of neural network-based soft sensing, a data-driven framework by combining neural network prediction models and sensitivity analysis methods is constructed in this paper, with application in wastewater treatment plant analysis. The results show that readily biodegradable substrate effluent (SS-e) and NH4+ + NH3 nitrogen effluent (SNH-e) are both important input factors affecting the difficult-to-measured effluent quality, chemical oxygen demand (COD), by resorting to PAWN sensitivity analysis and four neural network models. In addition, four neural network models worked together with bilateral sensitivity analysis for the input perturbation analysis, which shows that the addition of Gaussian perturbation to SS-e and flow rate influent (Q-in) has a greater effect on the prediction which results large fluctuations of output results. Finally, the effectiveness of this framework is confirmed based on above results. © 2022 IEEE.

关键词： Sensitivity analysis

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Deterministic Learning-Based Robust Practical Output Regulation for Second-Order Nonlinear systems

Deterministic Learning-Based Robust Practical Output Regulat...

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2024 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2024

作者： Hu, Qingxi Liu, Wei Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798331504755

This article intends to investigate the semi-global robust practical output regulation problem for a class of second-order nonlinear systems with parametric and non-parametric uncertainties based on deterministic learning method. First, by constructing the internal models, the robust output regulation problem is transformed into the semi-global robust practical stabilization problem. Second, we propose an adaptive neural control (ANC) strategy to solve the stabilization problem and utilize the radial basis function neural networks (RBF NNs) to identify unknown dynamic. Moreover, under the persistently exciting (PE) property, the unknown dynamics can be identified and the learned knowledge is saved as some constant NN weights. Finally, we design an deterministic learning-based adaptive neural control (DLANC) law with the learned knowledge to solve the output regulation problem and improve control performance. © 2024 IEEE.

关键词： Contrastive Learning

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Biomechanical analysis of typical upper limb movements based on Kinect-LifeMOD

Biomechanical analysis of typical upper limb movements based...

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4th International Conference on Frontiers of Manufacturing Science and Measuring Technology, ICFMM 2014

作者： Zeng, Ming Chen, Chang Wei Meng, Qing Hao Ren, Hong Lin Ma, Shu Gen Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical Engineering and Automation Tianjin University Tianjin 300072 China

ISBN: (纸本)9783038351924

In traditional biomechanical analysis of upper limb, the high-precision motion data and lifelike human models are needed. It is obvious that those processes are costly and time-consuming. In this paper, a novel and simple combination method based on Kinect-LifeMOD is proposed. Firstly, the Microsoft Kinect (a latest depth sensor) is used to build a cheap and precise motion capture platform. Real-time and reliable key-node rotation data of human skeletons can be acquired by this motion capture system. Next, rotation data is converted into position data as the input of the LifeMOD software which can establish mathematical model of upper limb and execute biomechanical analysis automatically. The experimental results show that the proposed method could achieve the satisfactory performance. © (2014) Trans Tech Publications, Switzerland.

关键词： Biomechanics

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An Online Strategy for Nonlinear Vector Autoregression 7

An Online Strategy for Nonlinear Vector Autoregression

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7th International Conference on Robotics, control and Automation Engineering, RCAE 2024

作者： Fang, Gang Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Nonlinear vector autoregression (NVAR), as an effective alternative to traditional reservoir computing (RC), has garnered significant attention in the field of time series prediction in recent years. Unlike conventional RC, NVAR does not require the use of random sampling matrices to define the underlying recurrent neural network, and it features fewer hyperparameters. However, NVAR is an offline model, making it challenging to meet the real-time monitoring requirements of industrial processes. Inspired by the Woodbury matrix identity, this paper proposes an online NVAR that can update the model using data arriving in blocks. Subsequently, this study constructs a soft sensing model based on the online NVAR to perform real-time monitoring of industrial processes. Finally, the effectiveness of the proposed method is validated using a real-world dataset from a wastewater treatment plant. © 2024 IEEE.

关键词： Wastewater treatment

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ES-MSCNN: An Echo State Multi-Scale Convolutional Neural network for Multi-Step Prediction of Wastewater Quality Variables 7

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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7th International Conference on Robotics, control and Automation Engineering, RCAE 2024

作者： Sun, Yue Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo State Multi-Scale Convolutional Neural network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output. © 2024 IEEE.

关键词： Convolutional neural networks

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