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Review of X-ray pulsar spacecraft autonomous navigation

Chinese Journal of Aeronautics 2023年第10期36卷 44-63页

作者： Yidi WANG Wei ZHENG Shuangnan ZHANG Minyu GE Liansheng LI Kun JIANG Xiaoqian CHEN Xiang ZHANG Shijie ZHENG Fangjun LU College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijing 100049China Beijing Institute of Control Engineering Beijing 100080China Beijing Institute of Tracking and Communication Technology Beijing 100091China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100091China Academy of Military Science Beijing 100091China National Innovation Institute of Defense Technology Academy of Military ScienceBeijing 100091China

This article provides a review on X-ray pulsar-based navigation(XNAV).The review starts with the basic concept of XNAV,and briefly introduces the past,present and future projects concerning *** paper focuses on the advances of the key techniques supporting XNAV,including the navigation pulsar database,the X-ray detection system,and the pulse time of arrival ***,the methods to improve the estimation performance of XNAV are ***,some remarks on the future development of XNAV are provided.

关键词： Estimation of pulse time of arrival Spacecraft autonomous navigation X-ray detection X-ray pulsar-based navigation X-ray pulsar signal processing

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Correction to: An advanced reinforcement learning control method for quadruped robots in typical urban terrains (International Journal of Machine Learning and Cybernetics, (2024), 10.1007/s13042-024-02478-9)

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International Journal of Machine Learning and Cybernetics 2025年 1-2页

作者： Yan, Chi Wang, Ning Gao, Hongbo Wang, Xinmiao Tang, Chao Zhou, Lin Li, Yuehua Wang, Yue School of Information Science and Technology University of Science and Technology of China No. 96 Jinzhai Road Anhui Hefei230026 China School of Information and Security Chongqing College of Mobile Communication No. 36 Dengying Avenue Qijiang District Sichuan Chongqing401520 China Institute of Advanced Technology University of Science and Technology of China No. 5089 Wangjiang West Road Anhui Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Nanyang639798 Singapore Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Zhejiang Hangzhou311121 China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

In this article, the affiliation details for Authors "Ning Wang2, Hongbo Gao1,3,4, Yuehua Li5, Yue Wang6" were interchanged and it is corrected now. Incorrect affiliation: 2Institute of Advanced technology, University of Science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 3School of Electrical and Electronic engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 4Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 5college of control Science and engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China 6School of Information and Security, Chongqing college of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China Correct affiliation: 2School of Information and Security, Chongqing college of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China 3Institute of Advanced technology, University of Science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 4School of Electrical and Electronic engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 5Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 6college of control Science and engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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Defense of Signal Modulation Classification Attack Based on GAN 7th

Defense of Signal Modulation Classification Attack Based on ...

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7th China National Conference on Big Data and Social Computing, BDSC 2022

作者： Zhu, Jiawei Liu, Yuhang Xu, Dongwei Yang, Hao Xuan, Qi Wang, Shunling Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9789811975318

Although deep learning has been broadly used in all kinds of domains and has also achieved very good performance, it is obvious that deep learning is very vulnerable to be attacked shown by extensive researches (e.g. even a very small perturbation is added to the raw signal data, the result of the modulation classification of the signal by the deep neural network will be wrong), which will cause serious consequences. Thus, it is necessary to find some ways to defense this attack. As a new deep learning model in recent years, Generative Adversarial Network (GAN) has been applied to the field of radio communication, which can encrypt the signal data, so as to ensure the reliable transmission of the signal. In this paper, a defense method against signal modulation classification attack based on GAN is developed, which is used to utilize generator to reconstruct the signal sample to improve the classification accuracy of the model, while the defense ability of the model against adversarial samples is improved. It has been shown that the method achieved good results in experiments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A Time-Efficient Path Navigating Landmine Detection Robot

A Time-Efficient Path Navigating Landmine Detection Robot

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Intelligent Signal Processing and Effective communication Technologies (INSPECT), IEEE International Conference on

作者： Rituraj Bhattacharjee Subhradip Mukherjee Hardik Gururaj Manish Rajpurohit Instrumentation & Control Engineering Department Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Electronics and communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The goal of a landmine detection robot is to map the remaining area on a visual map with millimeter precision after covering as much ground as it can show landmines. The prototype land mine detection robot model described in this study offers a visual interface for mapping landmines, modifying PIDs, and aligning cameras. It is also reasonably powerful, accurate, and easy to operate with other sensors. The manual, semi-auto, and auto modes of controlling the differential drive robot are emphasized. Image processing determines the exact location of the robot and provides live reckoning feedback to its dead reckoning servo control. A beat metal detector is a kind of quiet sensor used to locate landmines. The overall objective of the system is to provide the user with something robust, affordable, and easily comprehensible for the user.

关键词： Visualization Landmine detection Robot vision systems Prototypes Metals Detectors Manuals Signal processing Servosystems Intelligent sensors

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A Repetitive Motion Obstacle Avoidance Planning Scheme for Omnidirectional Mobile Manipulators with Noise Suppression Capability

A Repetitive Motion Obstacle Avoidance Planning Scheme for O...

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Chinese Automation Congress (CAC)

作者： Tang Shi-jun Hu Yun-feng Sun Zhong-bo Zhang Chong Fei Yu-zhe Xiao Xing-tian State Key Laboratory of Automotive Simulation and Control Jilin University College of Communication Engineering Jilin University Changchun China Department of Control Engineering Intelligent Rehabilitation Engineering Technology Laboratory Changchun University of Technology

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. To make OMRA accomplish repetitive motion, obstacle avoidance, and trajectory tracking tasks in complex dynamic environments with the presence of obstacles and external disturbances, a new noise-suppressed repetitive motion obstacle avoidance (NSRMOA) scheme is proposed. As established previously many OMRA motion planning assumes that the simulation is noise free. However, noise interference may prevent OMRA from accomplishing repetitive motion, obstacle avoidance, and trajectory tracking tasks, which may lead to task failure. The proposed the NSRMOA scheme with intrinsic noise suppression can solve external noise interference, so that the OMRA can accomplish the repetitive motion, obstacle avoidance and trajectory tracking tasks in the presence of external noise interference. Finally, the optimal solution of the NSRMOA scheme can be effectively obtained by using recurrent neural network (RNN). The effectiveness and accuracy of the scheme are verified by examples and comparisons.

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LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation

arXiv

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arXiv 2024年

作者： Sun, Jingyun Dai, Chengxiao Luo, Zhongze Chang, Yangbo Li, Yang College of Computer and Control Engineering Northeast Forestry University Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman College of Architecture and Arts Design Shijiazhuang Tiedao University

Legal Large Language Models (LLMs) have shown promise in providing legal consultations to non-experts. However, most existing Chinese legal consultation models are based on single-agent systems, which differ from real-world legal consultations, where multiple professionals collaborate to offer more tailored responses. To better simulate real consultations, we propose LawLuo, a multi-agent framework for multi-turn Chinese legal consultations. LawLuo includes four agents: the receptionist agent, which assesses user intent and selects a lawyer agent;the lawyer agent, which interacts with the user;the secretary agent, which organizes conversation records and generates consultation reports;and the boss agent, which evaluates the performance of the lawyer and secretary agents to ensure optimal results. These agents’ interactions mimic the operations of real law firms. To train them to follow different legal instructions, we developed distinct fine-tuning datasets. We also introduce a case graph-based RAG to help the lawyer agent address vague user inputs. Experimental results show that LawLuo outperforms baselines in generating more personalized and professional responses, handling ambiguous queries, and following legal instructions in multi-turn conversations. Our full code and constructed datasets will be open-sourced upon paper acceptance. © 2024, CC BY.

关键词： Modeling languages

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Cooperative control for Low-Voltage Ride-Through Based Improved Active Crowbar of Dfigm

SSRN

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SSRN 2024年

作者： Zhang, Yu Li, Kai Gao, Hanying Kang, Shouqiang Sun, Yutian Wang, Peng College of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin China College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China Harbin Large Motor Research Institute Harbin China College of Electrical and Electronic Engineering Nanyang Technological University Singapore

To solve the low voltage fault problem of doubly-fed induction generator-motor (DFIGM), this paper proposes a cooperative control strategy based on improved current tracking control (ICTC) and improved active crowbar topology. The strategy adopts ICTC in the case of slight dip fault. Under the constraints of rotor voltage and current, the tracking coefficient is optimized by particle swarm optimization (PSO) algorithm. The improved active crowbar is operated when severe dip fault occurs, which decreases the negative sequence component, reduces the total harmonic distortion (THD) of the point of common coupling (PCC) current. Taking a 10 MW DFIGM as a model, the magnetic flux variation characteristics are analyzed, and the cooperative control method in this paper is compared with the inductance emulating control (IEC) and the traditional crowbar in the case of symmetrical voltage dip fault and interphase voltage dip fault. The simulation experimental results verify the effectiveness and applicability of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Asynchronous generators

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A Passive Navigation Algorithm Based on Derivative Dynamic Time Regularization Geomagnetic Matching 2

A Passive Navigation Algorithm Based on Derivative Dynamic T...

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2021 2nd International Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2021

作者： Wang, Yiteng Bao, Jinhong Li, Zehua Science and Technology on Communication Information Security Control Laboratory China No.36 Research Institute of China Electronics Technology Group Corporation China College of Telecommunication Engineering Hangzhou Dianzi University Hangzhou310018 China

As satellite positioning has shortcomings such as susceptibility to multipath interference, we look forward to using a new algorithm to solve these problems. The method of derivative dynamic time regularization is used as the matching method of geomagnetic navigation. Due to the factors of the inertial device itself, a certain cumulative error will occur in the positioning solution of the inertial navigation (INS). Therefore, an outdoor error model needs to be established, and the positioning error is corrected by filtering technology. To solve this problem, the Unscented Kalman Filter is used to fuse the positioning results of the two to obtain a more accurate positioning, with an average accuracy within 1.5 meters. © Published under licence by IOP Publishing Ltd.

关键词： Kalman filters

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Acceleration of Formation control Based on Hessian Matrix of Artificial Potential Field

Acceleration of Formation Control Based on Hessian Matrix of...

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第42届中国控制会议

作者： Haoran Han Zhilong Xi Maolong Lv Jian Cheng School of Information and Communication Engineering University of Electronic Science and Technology of China Air Traffic Control and Navigation College Air Force Engineering University

The formation control of multi-agent system has become a hotspot in recent years. One of the most commonly used methods is to use artificial potential field(APF) to generate the attractive and repulsive force to organize the agents into a low-energy and stable formation. In this paper, we regard the APF as the difference in the desired position, and introduce the derivative of the gradient of the APF with respect to time, which is simplified as the Hessian matrix of the APF, into the formation control from the perspective of trajectory tracking. The energy-based Lyapunov function is selected to prove the stability and collision avoidance ability of the proposed control law. Moreover, we show that the maneuverability of the multi-agent system is enormously improved with the additional Hessian matrix term.

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Smart Supply Chain Management using Big Data Analysis and Machine Learning

Smart Supply Chain Management using Big Data Analysis and Ma...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Raviteja, Bhamidipati Pandya, Kartik A. Khan, Falak Khan, Zoheib Tufail Prajwal, R. Kekatpure, Atharva Department of Information Technolgy Gayatri Vidya Parishad College of Engineering Kommadi Visakhapatnam India Department of Mechanical Engineering Sardar Vallabhbhai Patel Institute of Technology Gujarat India Department of Business Administration Aligarh Muslim University Aligarh India Department of Mechanical Engineering Zakir Husain College of Engineering and Technology Aligarh India Department of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India Department of Instrumentation and Control Engineering Sarvajanik College of Engineering and Technology Surat India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Supply chain management (SCM) is concerned with the movement of products, services, and information from points of origin to consumers via a network of interconnected organizations and activities. It is believed that capacity, demand, and cost are well-known variables in typical SCM challenges. In practice, however, there are uncertainties brought on by changes in client demand, supply chain management, organizational risks, and lead times. Demand uncertainty in particular has a significant impact on SC performance with wide-ranging implications for scheduling production, planning inventories, and organizing transportation. In this regard, demand forecasting is a crucial strategy for tackling supply chain uncertainty. Forecasting supply chain demand using big data analytics has been published in the supervised and unsupervised learning categories. In supervised learning, the inputs and outputs are known because the data are tagged with labels. Given a fresh unlabeled dataset, the supervised learning algorithms attempt to map the inputs to matching outputs by identifying the underlying connections between the inputs and outputs. The main motive for the authors is to prove that supply chain can be managed through machine learning and to get a novel accuracy along with comparison of the machine learning models. © 2022 IEEE.

关键词： Supply chain management

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