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Microplastic contamination in Chinese topsoil from 1980 to 2050

Science of the Total Environment 2024年 955卷 176918页

作者： Zhang, Shuyou Sun, Jianqiang Zhou, Qing Feng, Xudong Yang, Jie Zhao, Kankan Zhang, Anping Zhang, Songhe Yao, Yijun College of Environment Hohai University Nanjing210024 China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing210008 China University of Chinese Academy of Sciences Beijing100049 China International Joint Research Center for Persistent Toxic Substances College of Environment Zhejiang University of Technology Hangzhou310014 China Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of Technology Hangzhou310014 China

China's soil is experiencing significant microplastic contamination. We developed a machine-learning model to assess microplastic pollution from 1980 to 2050. Our results showed that the average abundance of microplastics in topsoil increased from 45 items per kilogram of soil in 1980 to 1156 items by 2018, primarily due to industrial growth (39 %), agricultural film usage (30 %), tire wear (17 %), and domestic waste (14 %). During the same period, microplastic levels in cropland rose from 98 to 2401 items per kilogram of soil, and exposure levels for the Chinese population increased from 808 to 3168 items per kilogram. By 2050, a reduction in the use of agricultural films is expected to decrease cropland contamination by half. However, overall levels are anticipated to remain steady due to other persistent sources, indicating a continued spread of microplastics into subterranean environments, water bodies, and human systems. This study highlights China's microplastic challenges and suggests potential global trends, emphasizing the need for increased awareness and intervention worldwide. © 2024 Elsevier B.V.

关键词： China Machine learning Microplastic contamination Random forest Soil

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： cyber attacks

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state-of-the-art on theories and applications of cable-driven parallel robots

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Frontiers of Mechanical Engineering 2022年第3期17卷 35-57页

作者： Zhaokun ZHANG Zhufeng SHAO Zheng YOU Xiaoqiang TANG Bin ZI Guilin YANG Clément GOSSELIN Stéphane CARO State Key Laboratory of Tribology Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Precision/Ultra-Precision Manufacturing Equipment and Control Tsinghua UniversityBeijing 100084China Department of Mechanical Engineering University of MichiganAnn ArborMI 48109USA Department of Precision Instrument Tsinghua UniversityBeijing 100084China School of Mechanical Engineering Hefei University of TechnologyHefei 230009China Ningbo Institute of Industrial Technology Chinese Academy of Sciences(CAS)Ningbo 315201China Department of Mechanical Engineering UniversitéLavalQuebec QC G1V 0A6Canada Laboratory of Digital Sciences of Nantes National Centre for Scientific ResearchNantes 44321France

Cable-driven parallel robot(CDPR)is a type of high-performance robot that integrates cable-driven kinematic chains and parallel mechanism *** inherits the high dynamics and heavy load capacities of the parallel mechanism and significantly improves the workspace,cost and energy efficiency *** a result,CDPRs have had irreplaceable roles in industrial and technological fields,such as astronomy,aerospace,logistics,simulators,and *** follow the cutting-edge trend of rigid-flexible fusion,reflect advanced lightweight design concepts,and have become a frontier topic in robotics *** paper summarizes the kernel theories and developments of CDPRs,covering configuration design,cable-force distribution,workspace and stiffness,performance evaluation,optimization,and motion *** modeling,workspace analysis,and cable-force solution are *** and dynamic modeling methods are *** further promote the development,researchers should strengthen the investigation in configuration innovation,rapid calculation of workspace,performance evaluation,stiffness control,and rigid-flexible coupling *** addition,engineering problems such as cable materials,reliability design,and a unified control framework require attention.

关键词： cable-driven parallel robot kinematics optimization dynamics control

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Material state Identification of the Conveyor Belt based on Inception-v4

Material State Identification of the Conveyor Belt based on ...

引用

2021 China Automation Congress, CAC 2021

作者： Tang, Jinliang Cai, Zhenyu Xu, Chao Chen, Tehuan School of Mechanical Engineering and Mechanics Ningbo University Ningbo China System Integration Center Huzhou Institute of Zhejiang University Huzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665426473

For the uneven phenomenon of materials during material transportation on the factory assembly line, this paper proposes an improved model based on Inception-v4 to realize the identification and classification of three material states (more materials, normal materials, and fewer materials). In the image processing stage, the Farneback algorithm is used to capture the moving area, which makes the training of the model focus on effective information. In addition, considering the color bias of materials, the RGB enhancement strategy is proposed. By processing the image data in RGB channels, different weights are given to realize the enhancement of image features. In the network training and testing stage, we use Inception-v4 as the baseline model. The output of the model is modified for the classification task, and a new loss function is designed. In addition, considering the limited sample size, we pre-trained the model on ImageNet. Through experimental comparison, the improved algorithm proposed in this paper has better performance. © 2021 IEEE

关键词： Optical flows

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Efficient Planar Pose Estimation via UWB Measurements

arXiv

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arXiv 2022年

作者： Jiang, Haodong Wang, Wentao Shen, Yuan Li, Xinghan Ren, Xiaoqiang Mu, Biqiang Wu, Junfeng China School of Data Science The Chinese University of HongKong Shenzhen China College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

state estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： state estimation

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Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadrotor with Unicycle-Driven and Rotor-Assisted Turning

Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadroto...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhi Zheng Jin Wang Yuze Wu Qifeng Cai Huan Yu Ruibin Zhang Jie Tu Jun Meng Guodong Lu Fei Gao The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Institute of Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor config-uration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to roll through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely roll through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient rolling through challenging terrains while conserving energy.

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Physics-Informed Neural Network Combined with Characteristic-Based Split for Solving Forward and Inverse Problems Involving Navier-Stokes Equations

SSRN

引用

SSRN 2023年

作者： Hu, Shuang Liu, Meiqin Zhang, Senlin Dong, Shanling Zheng, Ronghao College of Electrical Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

This paper presents a novel approach for solving the shallow-water transport equation using a physics-informed neural network (PINN) combined with characteristic-based split (CBS). Our method separates the variables and only backpropagates gradients through a subset of them, resulting in a significant reduction in computation time compared to traditional PINN. Furthermore, we propose a boundary condition that accounts for the second-order partial derivative term, which is more appropriate for solving the diffusion equation with open domains than the commonly used assumption of zero boundary values. Our numerical results demonstrate that this boundary condition leads to improved convergence of the network. In addition, we introduce a parameter estimation method that requires information from the field at only two different times, yet yields accurate parameter estimates. We observe that excessive participation of variables in gradient backpropagation can lead to neural networks getting trapped in local optima. We use PINN combined with CBS method to solve 3-D incompressible flow. As the number of variables involved in gradient backpropagation increases, the accuracy of the solution decreases, which can partially support our viewpoint. The source codes for the numerical examples in this work are available at https://***/double110/PINN-cbs-.git. © 2023, The Authors. All rights reserved.

关键词： Incompressible flow

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Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots

arXiv

引用

arXiv 2023年

作者： Xu, Yang Zheng, Ronghao Zhang, Senlin Liu, Meiqin The College of Electrical Engineering Zhejiang University Hangzhou310027 China The Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

This paper concerns realizing highly efficient information-theoretic robot exploration with desired performance in complex scenes. We build a continuous lightweight inference model to predict the mutual information (MI) and the associated prediction confidence of the robot's candidate actions which have not been evaluated explicitly. This allows the decision-making stage in robot exploration to run with a logarithmic complexity approximately, this will also benefit online exploration in large unstructured, and cluttered places that need more spatial samples to assess and decide. We also develop an objective function to balance the local optimal action with highest MI value and the global choice with high prediction variance. Extensive numerical and dataset simulations show the desired efficiency of our proposed method without losing exploration performance in different environments. We also provide our open-source implementation codes released on GitHub for the robot community. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

arXiv

引用

arXiv 2022年

作者： Zhang, Zhuqing Song, Yang Huang, Shoudong Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou China Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

This paper focuses on designing a consistent and efficient filter for map-based visual-inertial localization. First, we propose a new Lie group with its algebra, based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of the map information, the proposed invariant EKF can naturally maintain the correct observability properties of the system. To consider the uncertainty of the map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of the map information can be taken into consideration to avoid over-confident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of the map information. Finally, we validate our proposed system’s high consistency, accuracy, and efficiency via extensive simulations and real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Lie groups

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Data-Driven Discovery of Stochastic Differential Equations

引用

Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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