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tracking the maneuvering spacecraft propelled by swing propulsion of constant magnitude

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Journal of Systems Engineering and Electronics 2020年第2期31卷 370-382页

作者： ZHAI Guang WANG Yanxin ZHAO Qi School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Systems Engineering Research Institute Beijing 100094China

This paper proposes partially norm-preserving filtering for a class of spacecraft in the presence of time-varying, but constant-magnitude maneuver. The augmented state Kalman filter(ASKF) is commonly used to track the maneuvering spacecraft with unknown constant propulsion;however, if the maneuver varies via time, the estimation performance will be degraded. To promote the tracking performance of the ASKF in case of time-invariant,constant-magnitude disturbance, the partially norm-preserving ASKF is developed by applying the norm constraint on the unknown maneuver. The proposed estimator, which is decomposed into two partial estimators and iteratively propagated in turns,projects the unconstrained maneuver estimation onto the Euclidian surface spanned by the norm constraint. The illustrative numerical example is provided to show the efficiency of the proposed method.

关键词： maneuvering spacecraft tracking augmented state norm-preserving Kalman filter

Practical tracking of MIMO uncertain stochastic systems driven by colored noises via active disturbance rejection control

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Science China(Information Sciences) 2022年第6期65卷 203-217页

作者： Chunwan LV Zhengyong OUYANG Zehao WU Feiqi DENG Mingqing XIAO School of Mathematics and Big Data Foshan University Systems Engineering Institute South China University of Technology Department of Mathematics Southern Illinois University

This study addresses the practical tracking problem for a class of multi-input multi-output(MIMO) uncertain stochastic systems driven by colored noises via the active disturbance rejection control approach. The extended state observer is designed to estimate in real time the unmeasurable states and the stochastic total disturbance of each subsystem, including unknown coupling system dynamics between subsystems, colored noises, and uncertainty caused by partly unknown control parameters. Active disturbance rejection controllers based on the timely estimation of the extended state observer and compensation are then designed, guaranteeing the mean square practical convergence of the tracking errors. Some numerical simulations are performed to validate the effectiveness of the proposed control strategy.

关键词： uncertain stochastic systems active disturbance rejection control extended state observer tracking colored noise

Output formation tracking for networked systems with limited energy and aperiodic silence

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Chinese Journal of Aeronautics 2022年第7期35卷 274-288页

作者： Jin JIN Junlong LI Donghao QIN Ning CAI School of Electrical Engineering Northwest Minzu UniversityLanzhou 730030China Key Laboratory of China’s Ethnic Languages and Information Technology for Ministry of Education Northwest Minzu UniversityLanzhou 730000China School of Missile Engineering Rocket Force University of EngineeringXi’an 710025China School of Artificial Intelligent Beijing University of Posts and TelecommunicationsBeijing 100876China

For leader-following networked systems with the topology switching and the aperiodic silence,limited-energy output formation tracking problems are investigated.Firstly,a new output formation tracking control protocol is proposed,which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents,respectively,and the aperiodic silence,the topology switching and the energy constraint index is introduced properly.Then,a two-step transformation method is presented to separate the whole dynamics of a networked system into the relative dynamics between the leader and tracking intelligent agents and the dynamics of the leader,and sufficient conditions for limited-energy output formation tracking for networked systems with limited energy and aperiodic silence are presented,which are extended into networked systems without the aperiodic silence.Especially,a partition checking algorithm is presented to check limitedenergy output formation tracking design criteria.Finally,a numerical example is illustrated to demonstrate the validness of theoretical results.

关键词： Energy calculations Formation Intermittent working Networked control system tracking

tracking Your Browser with High-Performance Browser Fingerprint Recognition Model

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China Communications 2020年第3期17卷 168-175页

作者： Wei Jiang Xiaoxi Wang Xinfang Song Qixu Liu Xiaofeng Liu Chinese Academy of Cyberspace Studies Beijing 100010China College of Computer Science Beijing University of TechnologyBeijing100124China Beijing Information Technology College Beijing100015China Institute of Information Engineering Chinese Academy of SciencesBeijing100093China University of Chinese Academy of Sciences Beijing100049China

As the cyber security has attracted great attention in recent years,and with all kinds of tools’(such as Network Agent,VPN and so on)help,traditional methods of tracking users like log analysis and cookie have been not that effective.Especially for some privacy sensitive users who changed their browser configuration frequently to hide themselves.The Browser Fingerprinting technology proposed by Electronic Frontier Foundation(EFF)gives a new approach of tracking users,and then our team designed an enhanced fingerprint dealing solution based on browser fingerprinting technology.Our enhanced solution plays well in recognizing the similar fingerprints,but it is not that efficient.Nowadays we improve the algorithm and propose a high-performance,efficient Browser Fingerprint Recognition Model.Our new model reforms the fingerprint items set by EFF and propose a Fingerprint tracking Algorithm(FTA)to deal with collected data.It can associate users with some browser configuration changes in different periods of time quickly and precisely.Through testing with the experimental website built on the public network,we prove the high-performance and efficiency of our algorithm with a 20%time-consuming decrease than ever.

关键词： browser fingerprint AHP fingerprint tracking algorithm

Deep knowledge tracking algorithm based on forgetting law

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 17-27页

作者： Guo Xiangbo Wang Jian Huang Mengjie Wang Minghui Yang Jian Yu Yongtao School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Yunnan Key Laboratory of Smart City in Cyberspace Security Yuxi Normal UniversityYuxi 653100China

Knowledge tracking(KT)algorithm,which can model the cognitive level of learners,is a fundamental artificial intelligence approach to solve the personalized learning problem in the field of education.The recently presented separated self-attentive neural knowledge tracing(SAINT)algorithm has got a great improvement on predictingthe accuracy of students’answers in comparison with the present other methods.However there is still potential to enhance its performance for it fails to effectively utilize temporal features.In this paper,an optimization algorithm for SAINT based on Ebbinghaus’law of forgetting was proposed which took temporal features into account.The proposed algorithm used forgetting law-based data binning to discretize the time information sequences,so as to obtain the temporal featuresin accordance with people’s forgetting pattern.Then the temporal features were used as input in the decoder of SAINT model to improve its accuracy.Ablation experiments and comparison experiments were performed on the EdNet dataset in order to verify the effectiveness of the proposed model.Seen in the experimental results,it achieved higher area under curve(AUC)values than the other present representative knowledge tracing algorithms.It demonstrates that temporal featuresare necessary for KT algorithms if it can be properly dealt with.

关键词： knowledge tracking neural network attention mechanism personalized learning Ebbinghaus' law of forgetting

Fiber‑Optic Microfiber:tracking Activity Enhancement and Suppression of Heterogeneous Photocatalysts

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Advanced Fiber Materials 2023年第2期5卷 527-542页

作者： Yunyun Huang Jiaxuan Liang Pengwei Chen Jiaxin Wan Haotian Wu Xiaolan Wu Aoxiang Xiao Bai‑Ou Guan Jinan University Guangzhou 511400China Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics TechnologyJinan UniversityGuangzhou 511443China

Obtaining detailed insight into the photocatalytic performance of heterogeneous photocatalytic materials,is important for evaluating material properties as well as guiding material design.However,capture of the detailed matter changes on a photocatalyst surface in real time,and in situ during photocatalysis remains challenging.This work reports a promising optical microfiber sensor integrating a photocatalytic reaction monolayer on an optical microfiber surface to monitor reaction kinetics using Cu_(2)O-based heterogeneous photocatalysts,as an example.The evanescent field of microfiber is used to track the photocatalytic process in real time,through the interaction with the catalytic layer,by monitoring the surface refractive index changes caused by adsorption and degradation.Since the catalytic layer is less than 1µm thick,the typical high-power light source can be replaced by low-power light irradiation.This method successfully reveals that relative to the pristine Cu_(2)O microspheres,the photocatalytic activity is enhanced by the incorporation of Ti_(3)C_(2)T_(x) MXene into Cu_(2)O,whereas incorporation of CdS into Cu_(2)O suppresses the activity.Compared with the existing methods used for photocatalysis evalu-ation,this optical microfiber can be directly employed in real matrices to track local photocatalytic performance.It can also provide details about the different adsorption/degradation kinetics of photocatalysts.It is suitable for most photocatalytic processes and is not limited to pollutants with characteristic UV–visible absorption spectra.This study provides important inspiration for the future development of in situ,real-time reaction assessment.

关键词： Photocatalysis around microfiber tracking Heterogeneous photocatalysts Activity enhancement Suppression

Deep Learning Enabled Object Detection and tracking Model for Big Data Environment

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Computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time application.Presently,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,etc.This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data environment.The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT model.The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast R-CNN.In addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature map.Lastly,softmax layer is applied to perform classification task.The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

Heptanuclear brucite disk with cyanide bridges in a cocrystal and tracking its pyrolysis to an efficient oxygen evolution electrode

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Science Bulletin 2019年第22期64卷 1667-1674页

作者： Jian-Qiang Zhao Dandan Cai Jun Dai Mohamedally Kurmoo Xu Peng Ming-Hua Zeng Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources Department of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China Hubei Collaborative Innovation Center for Advanced Organic Chemical Materials Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional MoleculesCollege of Chemistry and Chemical EngineeringHubei UniversityWuhan 430062China Institut de Chimie de Strasbourg CNRS-UMR7177Universite de Strasbourg67070 Strasbourg CedexFrance

The development of efficient oxygen evolution reaction(OER)catalysts is still lacking in exploration of the mechanism of controlled pyrolysis of precursors among new material platforms.Here,a novel Co-based coordination molecular cluster has been first introduced as precursor to obtain metallic cobalt core shelled by N-doped carbon(Co@NC)structure which operates as an oxygen evolution electrode.Specifically,a new cocrystal compound,[Co7II(l3-CN)6(mmimp)6][CoIICl3N(CN)2]á3CH3OH(Co7+1,mmimp=2-methoxy-6-((methylimino)-methyl)phenol),was isolated consisting of Brucite disks of cobalt where the usual bridging l3-OH is replaced by l3-CN produced by the in-situ decomposition of dicyanamide(NC-N-CNà).The cobalt atoms are bonded through the nitrogen atom of the cyanide.Remarkably,time dependent thermogravimetric-mass spectrometry(TG-MS)analysis was utilized to track its pyrolysis process.It allowed us to propose a possible formation process of the Co@NC structure from Co7+1.Interestingly,an extremely superior OER electrode is optimized for Co@NC-600 having the lowest overpotential of257 m V at 10 m A/cm2in 1 mol/L KOH solution.The present study pins down the importance of clusters of transition metals on realizing distinct nanostructures operating as highly efficient OER electrocatalyst.

关键词： Cobalt disk clusters Thermogravimetry-mass spectrometry Pyrolysis tracking Core-shell nanostructure Oxygen evolution reaction

Design and implementation of unmanned aerial vehicle localization and light tracking system

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 67-74页

作者： Xu Ming Dong Chen Li Hanlu Luo Qiming Wang Lizi Zhang Wanru Liu Huixin School of Digital Media and Design Arts Beijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Unmanned aerial vehicles(UAV)are applied widely and profoundly in various fields.Moreover,high-precision positioning and tracking in multiple scenarios are the core requirements for UAV usage.To ensure stable communication of UAVs in denial environments with substantial electromagnetic interference,a systematic solution is proposed based on a deep learning algorithm for target detection and visible light for UAV tracking.Considering the cost and computational power limitations on the hardware,the you only look once(YOLO)v4-Tiny model is used for static target detection of the UAV model.For UAV tracking,and a light tracker that can adjust the angle of emitted light and focus it on the target is used for dynamic tracking processing.Thus,achieving the primary conditions of UAV optical communication with good secrecy is also suitable for dynamic situations.The UAV tracker positions the UAV model by returning the coordinates and calculating the time delay,and then controls the spotlight to target the UAV.In order to facilitate the deployment of deep learning models on hardware devices,the lighter and more efficient model is selected after comparison.The trained model can achieve 99.25%accuracy on the test set.The dynamic target detection can reach 20 frames per second(FPS)on a computer with an MX520 graphics processing unit(GPU)and 6 GB of random access memory(RAM).Dynamic target detection on a Jetson Nano can reach 5.4 FPS.

关键词： unmanned aerial vehicles deep learning light tracking target detection unmanned aerial vehicles location unmanned aerial vehicles tracking