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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Zhang, Xin Duan, Xuting Zhou, Jianshan Zhang, Long Opening Project of Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport PRC Beijing China School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

ISBN: (纸本)9789819711024

An enhanced approach utilizing APF(artificial potential field) method is introduced in this paper. By adopting this approach, the challenge of local minima that may occur when dealing with local path planning for unmanned vehicles is tackled. When the unmanned vehicle approaches an obstacle, a virtual attractive potential field is implemented, utilizing the deviation in the heading angle of the vehicle to control the steering angle. Consequently, the vehicle can avoid obstacles, resolving the issue of local minima and enabling its application in path planning for multi-unmanned vehicles. The ROS with Gazebo simulation platform is used to verify the enhanced algorithm’s effectiveness in multi-unmanned vehicle path planning in an unknown environment. © Beijing HIWING Scientific and Technological information Institute 2024.

关键词： Motion planning

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Research on NER model for coal mine safety hazards based on BERT-CNN-BiGRUs-CRF 5

Research on NER model for coal mine safety hazards based on ...

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5th International Conference on Artificial Intelligence and Computer science, AICS 2023

作者： Ma, Li Yang, Fan Dai, Xinguan Gao, Hangbiao Song, Shuang College of Communication and Information Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China Xi’an Key Laboratory of Heterogeneous Network Convergence Communication Xi’an710054 China Safety Supervision Division of Shaanxi Cuijiagou Energy Co. Ltd. Shaanxi Tongchuan727000 China College of Security Science and Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China

ISBN: (纸本)9781510668621

During the coal mine safety production process, a significant amount of text data containing information about coal mine safety hazards, such as working face, hazard location, hazard subject, and hazard problem description, is accumulated. The extraction of named entities from coal mine safety hazard text serves as the foundation for conducting a study on the early detection of coal mine safety hazards. Since the single modeling strategy is being used, the current named entity recognition (NER) model technique has a low recognition precision and ratio. Firstly, a character-level encoding of coal mine safety hazard text is performed by a BERT pre-training language model to generate word vectors based on contextual information, followed by local and global deep feature extraction of coal mine safety hazard word vectors by a convolutional neural network (CNN) with multi-layer bi-directional gated recurrent neural networks (BiGRUs), and finally decoding by conditional random fields (CRF) to generate global optimal label. On tasks of the NER for coal mine safety hazards, by comparing and analyzing with the mainstream deep learning entity recognition models, As shown by the outcomes that the precision of the NER model for coal mine safety hazards proposed in this paper reaches 91.74%, the recall reaches 93.20%, and the F1-measure reaches 92.45%, which shows a better performance. The NER task of precisely obtaining key information such as hazard location and hazard subject from unstructured coal mine safety hazards text data is achieved, which provides important information for hazard investigation and management. © 2023 SPIE.

关键词： Extraction

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Knowledge-Aided Covariance Matrix Estimation Algorithm Based on Geometric Mean in STAP for Airborne Radar

Knowledge-Aided Covariance Matrix Estimation Algorithm Based...

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IET International Radar Conference 2023, IRC 2023

作者： Jing, Yang Du, Xiaolin Cui, Guolong Zheng, Jibin Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781839539954

The problem of knowledge-aided (KA) covariance matrix estimation in airborne radar space-time adaptive processing (STAP) is studied in this paper. In the traditional KA covariance matrix estimation methods, the insufficient number of available samples and inaccurate prior knowledge can cause serious deterioration of STAP performance. To tackle this problem, a new KA covariance matrix estimation algorithm based on geometric mean is proposed. Specifically, by utilizing the persymmetric structure information as well as the geometric mean in the space of positive definite matrices, a geometric mean estimator is constructed, which is then linearly combined with the prior covariance matrix to obtain a new type of CCM estimators. Simulation results illustrate that the proposed algorithm has good STAP performance with small samples and inaccurate prior knowledge. © The Institution of Engineering & technology 2023.

关键词： Covariance matrix

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Protected Fair Secret Sharing Based Bivariate Asymmetric Polynomials in Satellite Network

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Computers, Materials & Continua 2022年第9期72卷 4789-4802页

作者： Yanyan Han Jiangping Yu Guangyu Hu Chenglei Pan Dingbang Xie Chao Guo Abdul Waheed Department of Electronics and Communication Engineering Beijing Electronics Science and Technology InstituteBeijing100070China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an710126China Department of Cryptography and Technology Beijing Electronics Science and Technology InstituteBeijing100070China Department of Cyberspace Security Beijing Electronic Science and Technology InstituteBeijing100070China School of Communication Engineering Xidian UniversityXi’an710126China Institute of Information Engineering Chinese Academy of SciencesBeijing100093China University of Management Technology Lahore-Pakistan55300Pakistan

Verifiable secret sharing mainly solves the cheating behavior between malicious participants and the ground control center in the satellite *** verification stage can verify the effectiveness of secret shares issued by the ground control center to each participant and verify the effectiveness of secret shares shown by *** use a lot of difficult assumptions based on mathematical problems in the verification stage,such as solving the difficult problem of the discrete logarithm,large integer prime factorization,and so *** with other verifiable secret sharing schemes designed for difficult problems under the same security,the verifiable secret sharing scheme based on the Elliptic Curve Cryptography(ECC)system has the advantages of less computational overhead and shorter *** present,the binary polynomial is a single secret scheme and cannot provide effective ***,based on a Protected Verifiable Synchronous Multi Secret Sharing(PVS-MSS)scheme,this paper is designed based on bivariate asymmetric *** advanced verifiable attribute is introduced into the Protected Secret Sharing(PSS)*** paper extends the protected synchronous multi-secret sharing scheme based on bivariate polynomial *** ECC system constructs the security channel between the ground control center and participants and constructs the verification *** the verification algorithm,any participant can verify the consistency and effectiveness of the secret shadow and secret share received from other participants or presented by the secret distribution ***,no additional key agreement protocol is required;participants do not need to negotiate the session key for encryption;the secret share polynomial can generate the session key between participants and speed up the secret reconstruction *** verification stage has lower computational complexity than the verifiable scheme constructed by Rivest Shamir Adleman (RS

关键词： Multi-secret sharing binary asymmetric verifiable synchronization protected satellite network

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Review of X-ray pulsar spacecraft autonomous navigation

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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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Blind identification of MIMO Space-Time Block Codes Based on Convolutional Neural Network 21

Blind identification of MIMO Space-Time Block Codes Based on...

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Proceedings of the 7th International Conference on communication and information Processing

作者： Conglin Pan Si Chen Huijie Zhu Wei Wu Jiachuan Qian Lijun Wang Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC China and School of Electronic and Optical Engineering Nanjing University of Science and Technology China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC China

ISBN: (纸本)9781450385190

Aiming at the blind identification of space-time block codes (STBC) in multiple input multiple output (MIMO) system, this paper proposes a new convolutional neural network (CNN-N) to realize the blind identification. Compared to traditional algorithms using statistical features of received signal, CNN-N can reduce the computation with a higher correct identification rate. Consist of multiple layers with special functions, CNN-N has good generalization ability in complex MIMO channels. The network in this paper can recognize 6 STBC codes include spatial multiplexing signal (SM) and some OSTBC codes. The simulation result shows that this new convolutional neural network can finish STBC identification with a high correct rate even in low SNR by consuming moderate amounts of time.

关键词： Generalization Ability Convolutional Neural Network Blind Identification STBC MIMO

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Research on Secure communication Methods for High-Speed Cross-Network Data Exchange 4

Research on Secure Communication Methods for High-Speed Cros...

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4th International Conference on Electronic information Engineering and Computer communication, EIECC 2024

作者： Zhang, Yao Chen, Yitao Fei, Jiujiang Peng, Guojun Su, Rong Ministry of Education School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Hubei Wuhan430072 China Wuhan Maritime Communication Research Institute Hubei Wuhan430205 China Wuhan Railway Vocational College of Technology Hubei Wuhan430200 China

ISBN: (纸本)9798331534622

This paper introduces the application requirements for high-speed secure communication across networks, the fundamental principles of cross-network data exchange, and its implementation. Based on high-speed switch technology and proprietary protocols, a hardware-software co-design solution is proposed to achieve secure communication for cross-network data exchange. The solution eliminates potential security risks associated with unauthorized access, intrusion attacks, and information leakage in heterogeneous network interconnections. It establishes robust defense capabilities against illegal network intrusions, attacks, and failures, meeting the security communication requirements for controlled interconnection and cross-network data exchange between specialized networks. © 2024 IEEE.

关键词： Network intrusion

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes 4

Seamless Decision-Making in the Big Data Era: A Modular Appr...

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4th International Conference on Electrical, communication and Computer Engineering, ICECCE 2023

作者： Zemmouri, Mohamed Laalam, Fatma Zohra Kazar, Okba Himeur, Yassine Oulefki, Adel Toumi, Chahrazad Mansoor, Wathiq Attala, Shadi Artificial intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla30000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Mohamed Khider University Department of Computer Science LINFI Laboratory Biskra07000 Algeria United Arab Emirate University Department of Information Systems and Security College of Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369694

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source. © 2023 IEEE.

关键词： Big data

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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Mainlobe DRFM Jamming Suppression via Blind Source Separation Using Tensor Decomposition

Mainlobe DRFM Jamming Suppression via Blind Source Separatio...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Luxin Dong Bing Wang Bunian Pan Yukai Kong Xianxiang Yu Guolong Cui School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Electronic Information Control Laboratory Chengdu China

This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppression method resorting to blind source separation based on tensor decomposition (TD-BSS) is proposed to separate the target echo signal and jamming signal. Specifically, the received signals are reconstructed into higher-order tensor form. Further, the separating matrix optimization problem relying on tensor Canonical Polyadic (CP) decomposition principle is formulated and then solved by an enhanced line search alternating least square (ELS-ALS) algorithm. Finally, the performance of the proposed method is demonstrated through numerical simulations, showing its superior to suppress mainlobe DRFM jamming over the exiting method in terms of interrupted sampling and cyclic repeater jamming (ISCRJ) and chopping and interval (C&I) jamming.

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