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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Lyu, Teng Xu, Cheng Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811581540

Collaborative guidance of multiple UAVs with directional communication topologies for directional targets is being researched. First, a new multi-UAV time-cooperative guidance model is established based on the relative motion equation of the UAV target. Next, based on the cooperative control theory of multi-agent systems, a rigorously proven distributed time cooperative guidance method is designed. This ensures that the impact times of multiple UAVs in a directional topology reach asymptotic consistency. After reaching the impact time consensus, all unmanned aerial vehicles use traditional PN guidance techniques to ensure that the UAV accurately hits the target without impacting the impact time consensus. Finally, the simulation results prove the effectiveness of the designed collaborative guidance method. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Evaluating the Impact of DoS Attacks on Connected and Autonomous Vehicle Traffic Flow with Car-following Theory 8

Evaluating the Impact of DoS Attacks on Connected and Autono...

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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Jing, Penghao Zhu, Yongxin Li, Yongfu Huang, Xin School of Automation Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing Chongqing400065 China

ISBN: (纸本)9798331504892

This paper aims to assess the impact of denial-of-service (DoS) attacks on the traffic flow of connected and autono-mous vehicles (CAVs). With this aim, a novel car-following model is presented to depict the dynamics of CAVs to support a compre-hensive analysis of the CAV traffic performance. Particularly, the model uniquely integrates the information delay, the optimal ve-locity changes with memory and the malicious effect of manipu-lated information. Then, the linear stability of the proposed model is derived using the perturbation method. By introducing small perturbations into the system, the stability conditions for a platoon of CAVs are determined. Finally, extensive simulations are con-ducted to explore the impact of DoS attack on CAV traffic. Our experimental results demonstrate that the model accurately captures real traffic flow, particularly the behavior of a CAV platoon under a DoS network attack. © 2024 IEEE.

关键词： Autonomous vehicles

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A Novel Principal Component Analysis Algorithm for Fault Detection

A Novel Principal Component Analysis Algorithm for Fault Det...

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第43届中国控制会议

作者： Wang Shenhang Tian Kun Zheng Wei Liu Mengyu National Key Laboratory of Science and Technology on Aerospace Intelligent Control

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In modern industry,fault detection is very important in high-cost and safety-critical *** component analysis(PCA) is an effective data-driven approach which is widely used in this *** PCA is time-invariant,which often lead to false *** order to overcome the false alarms,recursive PCA algorithms have been proposed for adaptive ***,the amount of calculation of the algorithm is very great because of frequently updated *** this paper,a novel recursive PCA method is proposed for solving the question by introducing the standard residual *** standard residual method serves as an indicator of the level of consistency among data,which is used to decide whether or not to update the PCA *** proposed method is applied to fault detection and experiment results demonstrate the effectiveness of the method.

关键词： Principal component analysis fault detection standard residual method

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Bearing Fault Diagnosis Based on an Improved Morphological Filter

Bearing Fault Diagnosis Based on an Improved Morphological F...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ren, Xueping Guo, Liangjian Qiu, Fuqiang Liu, Tongtong School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

Extraction of repetitive shock signals from vibration signals is crucial in bearing fault detection. Morphological filtering can extract the signal components based on the geometric features of the signal. In this paper, we propose an improved morphological filter for extracting fault cycle pulses from vibration signals in the time domain. Morlet wavelets are used as the main waveforms of the structural elements, which guide the construction of the structural elements based on their correlation with the defect-induced pulse, the Teager energy operator (TEO) is used to reduce the modulated signal interference. The improved morphological filtering algorithm can obtain fault features from low signal-to-noise ratio signals. The processing results of one simulated signal and two sets of experimental signals verify the effectiveness and robustness of the method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wiener filtering

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Steady-state and dynamic simulation of gas phase polyethylene process

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Chinese Journal of Chemical Engineering 2024年第11期75卷 110-120页

作者： Xiaodong Hong Wanke Chen Zuwei Liao Xiaoqiang Fan Jingyuan Sun Yao Yang Chunhui Zhao Jingdai Wang Yongrong Yang State Key Laboratory of Chemical Engineering College of Chemical and Biological EngineeringZhejiang UniversityHangzhou 310027China Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province ZJU-Hangzhou Global Scientific and Technological Innovation CenterHangzhou 311215China Ningbo Research Institute Zhejiang UniversityNingbo 315100China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

Gas-phase polyethylene(PE)processes are among the most important methods for PE production.A deeper understanding of the process characteristics and dynamic behavior,such as properties of PE and reactor stability,holds substantial interest for both academic researchers and *** this study,both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform,which can be used to study a variety of operation tasks for commercial solution polyethylene processes,such as new product development,process control and real-time *** copolymerization kinetic parameters are fitted by industrial ***,a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed *** accuracy in predicting melt index and density of the polymer,and the dynamic behavior of the developed models are verified by real plant ***,the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments,since temperature control is important in industrial *** results reveal that all four schemes effectively track the setpoint ***,only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

关键词： Gas-phase polyethylene process Steady state Dynamic modeling control

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Grouping bi-chi-squared method for pulsar navigation experiment using observations of Rossi X-ray timing explorer

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Chinese Journal of Aeronautics 2023年第1期36卷 386-395页

作者： Haifeng SUN Jianyu SU Zhongwen DENG Lirong SHEN Weimin BAO Xiaoping LI Liansheng LI Zhe SU Wencong WANG School of Aerospace Science and Technology Xidian UniversityXi’an 710126China Shaanxi Key Laboratory of Space Extreme Detection Xi’an 710126China Peng Cheng Laboratory Shenzhen 518000China Academy of Advanced Interdisciplinary Research Xidian UniversityXi’an 710126China Beijing Institute of Control Engineering Beijing 100190China Institute of Space Electronic Information Technology Xi’an 710100China Shandong Institute of Aerospace Electronic Technology Yantai 264032China

X-ray pulsar-based navigation is a revolutionary technology which is capable of providing the required navigation information in the solar system. Performing as an important pulsar-based navigation technique, the Significance Enhancement of Pulse-profile with Orbit-dynamics(SEPO)can estimate orbital elements by using the distortion of pulse profile. Based on the SEPO technique,we propose a grouping bi-chi-squared technique and adopt the observations of Rossi X-ray Timing Explorer(RXTE) to carry out experimental verification. The pulsar timing is refined to determine spin parameters of the Crab pulsar(PSR B0531+21) during the observation periods, and a short-term dynamic model for RXTE satellite is established by considering the effects of diverse *** results suggest that the position and velocity errors are 16.3 km(3σ) and 13.3 m/s(3σ) with two adjacent observations split by one day(exposure time of 1.5 ks), outperforming those of the POLAR experiment whose results are 19.2 km(3σ) and 432 m/s(3σ). The approach provided is particularly applicable to the estimation of orbital elements via using high-flux observations.

关键词： Model buildings Navigation analysis Navigation testing Pulsars RXTE observations

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Fault Diagnosis of Rolling Bearing in Broad Learning System Based on Multi-domain Feature Selection

Fault Diagnosis of Rolling Bearing in Broad Learning System ...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ouyang, Bing Zhang, Chao Wu, Le Liu, Guiyi Wu, Yangbiao Xu, Shuai School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

This paper presents a novel solution for bearing fault diagnosis based on a Broad Learning System with multi-domain feature selection. Currently, the diagnosis of bearing faults is beset with numerous challenges, including operation under complex and fluctuating conditions, the subtlety of early fault indicators, noise interference in measurement signals, variations in operational conditions, and a broad spectrum of potential fault types. These factors collectively contribute to the complexity of accurately identifying and classifying bearing faults. The Broad Learning System introduced in this paper, by integrating multi-domain feature selection, enhances the model's capability to extract and utilize relevant features from noisy or incomplete data, potentially increasing diagnostic accuracy in suboptimal sensor environments. By leveraging features across multiple domains, the method based on the Broad Learning System can capture a wider array of fault characteristics, thereby broadening its applicability across various types of bearing faults and operational conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Roller bearings

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Research on Intent Recognition Method for Non-Cooperative Satellite Based on LSTM Network

Research on Intent Recognition Method for Non-Cooperative Sa...

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2023 China Automation Congress, CAC 2023

作者： Yihan, Liu Dong, Sun Han, Yan Quanrun, Mu Xiaochao, Liu Pengyuan, Qi School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Institute of Control Engineering National Key Laboratory of Space Intelligent Control Beijing100094 China Beijing Sun Wise Space Technology Ltd. Beijing100190 China Research Institute for Frontier Science Beihang University Beijing China

ISBN: (纸本)9798350303759

With the rapid advancements in space offensive and defensive countermeasure technologies, the scope of international confrontation has expanded beyond the traditional domains of sea, land, and air, reaching into the realm of space. In modern military warfare, the focus has shifted from solely physical and informational aspects to include the cognitive domain. Within this domain, intent recognition emerges as a crucial technology, offering tactical insights and decision-making guidance for future wars. This paper presents an intention recognition algorithm for space target behaviors, utilizing a long short-term memory (LSTM) network. By incorporating time series motion parameters and the relative relationships between enemy and friendly satellite, the algorithm identifies the tactical intentions behind space target orbital behaviors. Additionally, a space target attack and defense scenario simulation environment is established using MATLAB and C-W equations, enabling the creation of a dataset for space target recognition. Through the proposed method, the simulation effectively discerns the orbital behavior intentions of space targets, providing valuable guidance for battlefield decision-making. © 2023 IEEE.

关键词： Long short-term memory

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Integrity monitoring of cycle slip detection in observation domain for carrier phase-based differenced positioning

Integrity monitoring of cycle slip detection in observation ...

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2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Lin, Zhao Yang, Li Chun, Jia Liang, Li Shufan, Yang Jiachang, Jiang College of Intelligent Systems Science and Engineering Harbin Engineering University China Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation China

ISBN: (纸本)9781713871378

Successful resolution of cycle ambiguities plays a crucial to high accuracy and high integrity navigation by carrier phase differenced positioning architecture. However, a cycle slip caused by loss-of-lock of signal changes the fixed ambiguity, which will affect the cycle resolution integrity. It is, therefore, necessary for detecting cycle slip and further satisfying its integrity. In general, dual-frequency measurements are deployed to construct the carrier phase differenced positioning architecture for satisfying the stringent accuracy and integrity performance for safety-critical applications, such as automatic shipboard landing. Thus, dual-frequency-based cycle slip detection methodologies can be introduced in this positioning architecture. The geometry-free (GF) modeling uses only dual-frequency phase measurements to construct the statistical residuals, which are sensitive to small cycle slips that should be detected in high-integrity applications. At present, the threshold of the GF modeling is generally designed as an empirical constant, such as 5 cm, lacking theoretical derivation to quantify its integrity risk. Therefore, this paper proposes an integrity monitoring method for GF modeling to satisfy the integrity performance. Determining the threshold value of the cycle slip statistical test is designed as a function of a required false alarm probability and the quality of raw phase measurements. A required missed detection probability can be used to construct the minimal detectable error (MDB), which can evaluate the availability of the integrity monitoring method to control the missed detection probability. As noted above, the threshold and the MDB are jointly deployed to provide the high-integrity requirements. This paper collects simulated and real-world dual-frequency GNSS datasets to verify the performance of the proposed method. The results show that this method can dynamically adjust the detection threshold to control the false alarm probability and

关键词： Errors

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