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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Wang, Bohui Wang, Jingcheng School of Control Science and Engineering Shandong University Jinan250061 China The School of Aerospace Science and Technology Xidian University Xi’an710071 China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

The distributed observer design problem holds significant importance in cases in which the output information of a system is decentralized across different subsystems. Each subsystem has a local observer and access to one part of the measurement outputs and information exchanged through communication networks. This paper focuses on the design of distributed observer with jointly connected directed switching networks. The problem presents challenges due to passive switching modes and the open-loop unboundedness that results from local observability. To overcome these challenges, we develop a network transformation mapping method whereby each local observer can classify itself into an independent subgraph based on independent judgment. Next, an observable decomposition and reorganization method is developed for the digraph case to ensure that each subgraph possesses independent dynamic properties. Asymptotic omniscience is then proven using a developed recursive proof method. This paper includes many previous results as special cases, because most are only suitable for undirected switching topologies or fast-switching cases. An adaptive coupling gain design is proposed to simplify the calculation and verification of conditions that guarantee asymptotic omniscience. Finally, simulation results with the power system show the validity of the developed theory. © 2024, CC BY.

关键词： Continuous time systems

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Sliding mode controller by using adaptive exponential reaching law based on nonlinear disturbance observer

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ISA Transactions 2025年

作者： Liu, Ying Li, Gongfa Du Jiang Yun, Juntong Tao, Bo Jiang, Guozhang Kong, Jianyi Chen, Baojia Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Longzhong Laboratory Xiangyang441000 China Hubei Key Laboratory of Hydroelectric Machinery Design& Maintenance China Three Gorges University Yichang443002 China

Aiming at the position tracking control problem of flexible jointed manipulator in the uncertainties and external disturbances, this paper proposes an integrated nonlinear disturbance observer (NDOB) and a non-singular fast terminal sliding mode (NFTSM) control method based on adaptive exponential reaching law (AERL). The NDOB is designed to ensure that the time-varying disturbance is suppressed in finite time. The AERL is designed to improve the track tracking speed and reduce the chattering of the control input. Simulation results show that compared with the optimal ARL, the proposed method reduces the trajectory tracking error by 1.3 %, improves the trajectory tracking convergence speed. Experimental results show that the proposed method reduces the trajectory tracking error by 0.545 %. © 2025 ISA

关键词： Sliding mode control

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Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset

arXiv

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arXiv 2024年

作者： Lian, Shijie Zhang, Ziyi Li, Hua Li, Wenjie Yang, Laurence Tianruo Kwong, Sam Cong, Runmin Hainan University China The Hong Kong University of Science and Technology Guangzhou China Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications Southeast University Ministry of Education China Huazhong University of Science and Technology China St. Francis Xavier University Canada LIngnan University School of Data Science Hong Kong Shandong University School of Control Science and Engineering China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. Datasets and codes are released on Github. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Optimal Configuration of Energy Storage Capacity in Wind-Storage Joint Frequency Regulation

Optimal Configuration of Energy Storage Capacity in Wind-Sto...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Ling Hao Fei Xu Lei Chen Qun Chen Yong Min Yi Gu Yiming Chang Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China Department of Engineering Mechanics Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Tsinghua University Beijing China State Grid Liaoning Electric Power Co. Ltd. Liaoning China

We propose combining energy storage control with pitch control of wind turbines to give wind farms a primary frequency regulation capability similar to thermal power units. Using chance-constrained programming to handle the relationship between reliability and operating cost, and considering the full life-cycle cost of energy storage, we establish a chance-constrained programming model with the objective of minimizing the wind-storage joint frequency regulation operating cost. This achieves a trade-off between operating cost and reliability of the wind-storage frequency regulation system. We use the ant-lion algorithm to solve the model and obtain the optimal configuration of energy storage power and capacity for the wind farm, and compare the optimization results of the ant-lion algorithm with those of the particle swarm algorithm and the genetic algorithm. A case study is presented to verify the effectiveness of the proposed model and algorithm.

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Historical Air Pollutant Emissions and Future Sustainable Pathways of Global Cement Plants

SSRN

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SSRN 2024年

作者： Yan, Xizhe Tong, Dan Cao, Zhi Chen, Cuihong Guo, Yaqin Xu, Ruochong Qin, Xinying Fu, Yujia Liu, Yang Zheng, Dongsheng He, Kebin Zhang, Qiang Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing100084 China College of Environmental Science and Engineering Nankai University Tianjin300350 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

Assessing historical emission trajectories and forecasting future changes at the unit level for the global cement industry are vital for summarizing past experiences in green management and designing a plant-by-plant transition roadmap. Here, we evaluate the evolution of major air pollutant emissions during 1990−2020 and explore mitigation pathways for future sustainable development, based on a developed unit-level global cement emissions database. Our findings indicate that strengthening emission standards is an effective strategy for decoupling air pollutant emissions from clinker production, particularly for emerging economies. Without stringent emission controls, these economies are poised to become major contributors to pollutant emissions due to growing demand. To promote environmental sustainability, emerging economies should enhance emission controls to reverse the emission growth driven by demand expansion, while China and developed countries should deepen their emission mitigation efforts. Our results highlight the importance of upgrading end-of-pipe controls to ensure sustainability within the global cement industry. © 2024, The Authors. All rights reserved.

关键词： Sustainable development

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Synergistic Optimization of Power & Capacity Configuration Considering control Strategy for Flywheel Energy Storage Participating in Primary Frequency Regulation

SSRN

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SSRN 2024年

作者： Liang, Lu Hong, Feng Ji, Weiming Jiang, Hui Hao, Junhong Fang, Fang Liu, Jizhen The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University Beijing102206 China

The intermittent and stochastic nature of renewable energy sources poses stern challenges to the frequency stability of new power system. The flywheel energy storage system (FESS), with its rapid response and high power density, plays a pivotal role in mitigating frequency fluctuations. To improve the performance of the FESS assisting thermal power unit (TPU), a collaborative optimization methodology of the capacity configuration and control strategy for FESS is presented in this paper. Aiming to enhance the primary frequency control (PFC) performance and reduce the life-cycle cost of the FESS, this paper established a capacity configuration model based on a dynamic adaptive control strategy, in which FESS compensates for the lack of TPU frequency regulation capability and recovery SOC actively. A case study demonstrated that the optimization model proposed is suitable for different frequency scenarios. It has been verified that the control strategy which is based on the SOC of FESS and frequency regulation capability of the TPU is economically more attractive compared to traditional strategies. In addition, this study revealed that a FESS with a charge/discharge rate of 7.5 C and rated power of 7.6 MW was appropriate for assisting a 600MW TPU in PFC by the optimal capacity configuration and control strategy. © 2024, The Authors. All rights reserved.

关键词： Wheels

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Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking

arXiv

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arXiv 2022年

作者： Wang, Guangming Peng, Chensheng Zhang, Jinpeng Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China XLAB The Second Academy of China Aerospace Science And Industry Corporation Beijing100854 China

Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected by LiDAR, or based on images captured from cameras. However, relying on one single sensor is not robust enough, because it might fail during the tracking process. On the other hand, feature fusion from multiple modalities contributes to the improvement of accuracy. As a result, new techniques based on different sensors integrating features from multiple modalities are being developed. Texture information from RGB cameras and 3D structure information from Lidar have respective advantages under different circumstances. However, it’s not easy to achieve effective feature fusion because of completely distinct information modalities. Previous fusion methods usually fuse the top-level features after the backbones extract the features from different modalities. In this paper, we first introduce PointNet++ to obtain multi-scale deep representations of point cloud to make it adaptive to our proposed Interactive Feature Fusion between multi-scale features of images and point clouds. Specifically, through multi-scale interactive query and fusion between pixel-level and point-level features, our method, can obtain more distinguishing features to improve the performance of multiple object tracking. Besides, we explore the effectiveness of pre-training on each single modality and fine-tuning on the fusion-based model. The experimental results demonstrate that our method can achieve good performance on the KITTI benchmark and outperform other approaches without using multi-scale feature fusion. Moreover, the ablation studies indicates the effectiveness of multi-scale feature fusion and pre-training on single modality. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

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Data Security Risk Assessment Method of Intelligent and Connected Vehicles Based on Data Security Classification and Grading

Data Security Risk Assessment Method of Intelligent and Conn...

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IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Liyong Wang Long Jin Haojie Ji Jingyan Wang Yupeng Chen Junzhe Fang State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China State Key Laboratory of Intelligent Vehicle Safety Chongqing China China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

The rapid development of intelligent and connected vehicle (ICV) has brought increasing communication data and valuable data assets. However, the diversity and complexity of ICV data make it hard to carry out data security risk assessment. Aiming at the problem, this paper proposes an effective analytical method for the data security risk assessment method of ICVs. Considering the typical scenario for the generated data of ICVs, a data security classification and grading framework is constructed from the aspects of in-vehicle data, personal data, and external environment data. The data security risk assessment model is built for ICVs and includes functional modules such as asset identification, threat identification, impact assessment, vulnerability identification, and risk assessment. This work provides methods and security management and control strategy support for ICV data security risk assessment.

关键词： Industries Connected vehicles Data security Security management Transportation Data models complexity theory Risk management

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FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection

arXiv

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Wu, Jinxing Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China The Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China

Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds. However, there still present many challenges for efficient and robust feature fusion in the field of 3D object detection. In this paper, first, unstructured 3D point clouds are filled in the 2D plane and 3D point cloud features are extracted faster using projection-aware convolution layers. Further, the corresponding indexes between different sensor signals are established in advance in the data preprocessing, which enables faster cross-modal feature fusion. To address LiDAR points and image pixels misalignment problems, two new plug- and-play fusion modules, LiCamFuse and BiLiCamFuse, are proposed. In LiCamFuse, soft query weights with perceiving the Euclidean distance of bimodal features are proposed. In BiLiCamFuse, the fusion module with dual attention is proposed to deeply correlate the geometric and textural features of the scene. The quantitative results on the KITTI dataset demonstrate that the proposed method achieves better feature-level fusion. In addition, the proposed network shows a shorter running time compared to existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Cascade of pressure-driven phase transitions in the topological nodal-line superconductor PbTaSe2

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Physical Review B 2022年第6期106卷 L060501-L060501页

作者： Tahir Murtaza Haiyang Yang Jiajia Feng Yi Shen Yongheng Ge Yi Liu Chunqiang Xu Wenhe Jiao Yaokang Lv Christopher J. Ridley Craig L. Bull Pabitra K. Biswas Raman Sankar Wei Zhou Bin Qian Xuefan Jiang Zhenjie Feng Yonghui Zhou Ziming Zhu Zhaorong Yang Xiaofeng Xu Key Laboratory of Quantum Precision Measurement of Zhejiang Province Department of Applied Physics Zhejiang University of Technology Hangzhou 310023 China College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory Chinese Academy of Sciences Hefei 10 230031 China School of Physics and Key Laboratory of MEMS of the Ministry of Education Southeast University Nanjing 211189 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China College of Chemical Engineering Zhejiang University of Technology Hangzhou 310014 China ISIS Pulsed Neutron and Muon Source STFC Rutherford Appleton Laboratory Harwell Campus Didcot Oxfordshire OX11 0QX United Kingdom School of Chemistry University of Edinburgh David Brewster Road Edinburgh EH9 3FJ United Kingdom Institute of Physics Academia Sinica Nankang Taipei 11529 Taiwan Department of Physics Changshu Institute of Technology Changshu 215500 China Materials Genome Institute Shanghai University Shanghai 200444 China

We report a succession of pressure-tuned structural transitions in the topological nodal-line superconductor PbTaSe2, evidenced from synchrotron x-ray diffraction, elastic neutron scattering, Raman spectroscopy, and electrical transport measurements up to 56 GPa, accompanied by first-principles calculations to uncover the evolution of the underlying electronic structure. In contrast to the previously proposed small shift of the Pb Wyckoff coordinate in the sub-GPa regime, our study reveals that it is rather a transition from P6¯m2 (α phase) to P63mc (β phase), subsequently followed by a transition to P6/mmm (γ phase) at ∼7.5 GPa and to Pmmm (δ phase) at ∼44 GPa. In addition, the first-principles calculations unambiguously demonstrate the multiple types of topological fermions associated with these different pressure-driven structures. Collectively, our results not only present the intriguing structural transitions in this topological PbTaSe2 superconductor, they also provide the impetus to study topological phase transitions and their physical consequences in a broader class of topological materials.

关键词： Phase transitions by order Superconducting phase transition Superconductivity Topological superconductors

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