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第42届中国控制会议

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University School of Artificial Intelligence and Automation Huazhong University of Science and Technology Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators,a composite iterative learning control(ILC) scheme via generalized proportional integral observer(GPIO) is proposed.A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics,parameter uncertainties and load fluctuations.A GPIO is then designed to estimate the disturbances and high-order time *** introducing estimations into a PD-type ILC at each iteration,a composite control scheme is obtained,such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of *** this paper,the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to *** effectiveness of the proposed control scheme is finally verified by simulation results.

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Quick unsupervised hyperspectral dimensionality reduction for earth observation: A comparison

arXiv

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arXiv 2024年

作者： Lupu, Daniela Garrett, Joseph L. Johansen, Tor Arne Orlandic, Milica Necoara, Ion Automatic Control and System Engineering Department National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Center for Autonomous Marine Operations and Systems Norwegian University of Science and Technology Trondheim Norway Department of Engineering Cybernetics Norway Department of Electronic Systems Norway G. Mihoc-C. Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. Seven unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection. The experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline. © 2024, CC BY.

关键词： Remote sensing

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Size-Dependent Strength Superiority in Multi-Principal Element Alloys Versus Constituent Metals: Insights from Machine Learning Potential and Embedded-Atom Model

SSRN

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SSRN 2024年

作者： Shuang, Fei Ji, Yucheng Laurenti, Luca Dey, Poulumi Department of Materials Science and Engineering Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Beijing Advanced Innovation Center for Materials Genome Engineering National Materials Corrosion and Protection Data Center Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China Delft Centre of System and Control Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

Multi-principal element alloys (MPEAs) are renowned for their enhanced mechanical strength compared to their constituent metals, as evidenced by various experimental techniques such as tension/compression tests and instrumental indentation. Nevertheless, atomistic simulations sometimes produce conflicting results, casting doubt on the consistently superior mechanical properties of MPEAs. To address these discrepancies, we develop new machine-learning interatomic potentials (MLIPs) for body-centered cubic refractory MoNbTaW MPEAs, supported by a comprehensive dataset that encompasses extensive defects. We also benchmark the established embedded-atom model (EAM) potential against this dataset and our new MLIPs. Our simulations, covering diverse MoNbTaW compositions, confirm that both MLIPs and EAM accurately capture the critical strengthening mechanisms associated with plastic deformation in MPEAs. Utilizing the EAM potential, we perform reliable large-scale atomistic simulations to investigate various plastic mechanisms. Our findings suggest that generally, MPEAs exhibit superior mechanical strength compared to their constituent metals in macroscale specimens, primarily due to solid solution strengthening and dislocation motion. However, at the nanoscale where plasticity is mainly governed by dislocation nucleation and grain boundary deformation, the constituent metals may outperform MPEAs. This study identifies a critical length scale above which MPEAs demonstrate enhanced mechanical strength relative to their constituent elements. Below this scale, the advantage diminishes, underscoring a significant size-dependent effect that is crucial for optimizing MPEA applications, particularly at the nanoscale. © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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FPGA implementation of hog-based space target distance measurement 2

FPGA implementation of hog-based space target distance measu...

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2nd IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021

作者： Ma, Yue Yi, Jianjun Xu, Gengshu Chen, Yuanhao Wang, Danwei Zhao, Changchun East China University of Science and Technology School of Mechanical and Power Engineering Shanghai200237 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore Shanghai Aerospace Control Technology Institute Shanghai201109 China Intelligent Unmanned System Control Technology Joint Lab Shanghai201109 China

ISBN: (纸本)9781728190181

This paper focuses on space cooperative target identification and ranging using monocular CMV4000 CMOS sensor and Xilinx Zynq-7000 Soc based platform without the operating system that adopts Histograms of Oriented Gradients (HOG) descriptor and Support Vector Machine (SVM) for classification. With Vivado High-Level Synthesis to accelerate IP creation by enabling C specifications to be directly targeted into devices, this implementation allows for two high-resolution images (2048 times2048 pixels) processing per second. The hardware and software co-design of Xilinx Zynq7000 promises the accuracy of space target classification and distance measurement and thus meets the requirement of real-time space application. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Effect of Rainfall–Runoff Process on Sources and Transformation of Nitrate at the Urban Catchment Scale

SSRN

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SSRN 2023年

作者： Wu, Yali Jiang, Xia Yao, Yucheng Kang, Xiaoqi Niu, Yuan Wang, Kun State Environmental Protection Key Laboratory for Lake Pollution Control Chinese Research Academy of Environmental Sciences Beijing100012 China National Engineering Laboratory for Lake Pollution Control and Ecological Restoration Institute of Lake Ecology Environment Chinese Research Academy of Environmental Sciences Beijing100012 China Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin300072 China Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim Tianjin University Tianjin300072 China

Nonpoint source nitrogen pollution in urbanized regions poses a substantial challenge for comprehensive management of lake watersheds. Considerable uncertainty remains in identifying nitrate sources and elucidating nitrate transformation in urban regions coupled with lack of high-resolution sampling campaigns. In this study, high-frequency sampling was conducted in river and different typical functional areas and impervious underlying surfaces during a rainfall event, followed by nitrogen and oxygen isotopes and hydrochemical characteristics. Nitrate and major ions in urban river were attributable to similar sources linked to anthropogenic factors. The δ15N–NO3− and δ18O–NO3− values in the river ranged from 1.94‰ to 8.89‰ and from −1.16‰ to 4.22‰, respectively. Nitrate originated from the mixing process dominated by nitrification before and during the rainfall event, whereas it was derived mainly from the nitrification process in the river after the rainfall event. The Bayesian model results showed that rainfall runoff in the residential area contributed most to the nitrate in the river (65.4% ± 20.2%). Additionally, atmospheric deposition was the major source of NO3− in pavement (34.05% ± 2.3%) and roof (41.2% ± 4.24%) runoff. Nitrogen input from dry deposition associated with heavy automobile traffic in city should receive greater attention. © 2023, The Authors. All rights reserved.

关键词： Nitrates

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Two-step Optimal Allocation of Stationary and Mobile Energy Storage systems in Resilient Distribution Networks

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Journal of Modern Power systems and Clean Energy 2021年第4期9卷 788-799页

作者： Xinyi Jiang Jian Chen Qiuwei Wu Wen Zhang Yicheng Zhang Jie Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250061China Center for Electric Power and Energy(CEE) Department of Electrical EngineeringTechnical University of Denmark(DTU)2800 Kongens LyngbyDenmark Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)Singapore State Grid Yantai Power Supply Company YantaiChina

Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.

关键词： Resilient distribution network stationary energy storage system mobile energy storage system optimal allocation

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engineering grain boundaries in monolayer molybdenum disulfide for efficient water-ion separation

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science (New York, N.Y.) 2025年第6735期387卷 776-782页

作者： Shen, Jie Aljarb, Areej Cai, Yichen Liu, Xing Min, Jiacheng Wang, Yingge Wang, Qingxiao Zhang, Chenhui Chen, Cailing Hakami, Mariam Fu, Jui-Han Zhang, Hui Li, Guanxing Wang, Xiaoqian Chen, Zhuo Li, Jiaqiang Dong, Xinglong Shih, Kaimin Huang, Kuo-Wei Tung, Vincent Shi, Guosheng Pinnau, Ingo Li, Lain-Jong Han, Yu Physical Sciences and Engineering Division (PSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore Department of Physics King Abdulaziz University Jeddah Saudi Arabia KAUST Catalysis Center King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China Shanghai Applied Radiation Institute Shanghai Key Laboratory of Atomic Control and Application of Inorganic 2D Supermaterials Shanghai University Shanghai China Department of Mechanical Engineering University of Hong Kong Pok Fu Lam Road Hong Kong China Department of Civil Engineering University of Hong Kong Pok Fu Lam Road Hong Kong China Imaging and Characterization Core Lab King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Chemical System Engineering School of Engineering University of Tokyo Tokyo Japan Center for Electron Microscopy South China University of Technology Guangzhou China School of Emergent Soft Matter South China University of Technology Guangzhou China Catalyst Center of Excellence (CCoE) Research and Development Center Saudi Aramco Dhahran Saudi Arabia Center for Sustainability and Energy Technologies Chang Gung University Taoyuan Taiwan Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources Qinghai Institute of Salt Lakes Chinese Academy of Sciences Xining Qinghai China University of Chinese Academy of Sciences Wenzhou China

Two-dimensional (2D) materials have long been considered as ideal platforms for developing separation membranes. However, it is difficult to generate uniform subnanometer pores over large areas on 2D materials. We report that the well-defined eight-membered ring (8-MR) pores, typically formed at the boundaries of two antiparallel grains of monolayer molybdenum disulfide (MoS2), can serve as molecular sieves for efficient water-ion separation. The density of grain boundaries and, consequently, the number of 8-MR pores can be tuned by regulating the grain size. Optimized MoS2 membranes outperformed the state-of-the-art membranes in forward osmosis tests by demonstrating both ultrahigh water/sodium chloride selectivity and exceptional water permeance. Creating precise pore structures on atomically thin films through grain boundary engineering presents a promising route for producing membranes suitable for various applications.

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 753-765页

作者： Jun Ye Spandan Roy Milinko Godjevac Vasso Reppa Simone Baldi Department of Maritime and Transport Technology Delft University of TechnologyDelft 2628 CDThe Netherlands Robotics Research Center International Institute of Information Technology HyderabadHyderabad-500032India Future Proof Shipping Delft 3013 ALThe Netherlands School of Mathematics and School of Cyber Science and Engineering Southeast UniversityNanjing 210096China the Delft Center for System and Control Delft University of TechnologyDelft 2600 AA

Construction crane vessels make use of dynamic positioning(DP)systems during the installation and removal of offshore structures to maintain the vessel’s *** have reported cases of instability of DP systems during offshore operation caused by uncertainties,such as mooring ***"robustification"for heavy lift operations,i.e.,handling such uncertainties systematically and with stability guarantees,is a long-standing challenge in DP design.A new DP method,composed by an observer and a controller,is proposed to address this challenge,with stability guarantees in the presence of *** test the proposed method on an integrated cranevessel simulation environment,where the integration of several subsystems(winch dynamics,crane forces,thruster dynamics,fuel injection system etc.)allow a realistic validation under a wide set of uncertainties.

关键词： Construction crane vessels dynamic positioning system offshore robust control uncertainty

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MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial...

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

作者： Hekai Yao Xuetao Zhang Gang Sun Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China College of Artificial Intelligence The Institute of Robotics and Automatic Information System (IRAIS) Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

关键词： Laser radar Accuracy Uncertainty Probabilistic logic Distortion Robustness Sensors Odometry Distortion measurement State estimation

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