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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Guo, Xiaoyun Wu, Chengfeng Fang, Xueli Xu, Cheng Li, Hui Beijing Institute of Mechanical and Electrical Engineering Beijing China Beijing Institute of Mechanical and Electrical Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Institute of Mechanical and Electrical Engineering Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Cross-domain synergy and intelligence will become important features of future warfare. Cross-domain guidance is one of the typical forms of cross-domain synergy. It can significantly improve the ability to respond quickly to random targets and time-sensitive targets and enhance the anti-jamming ability in the case of confrontation. This article introduces the concept and winning mechanism of cross-domain guidance, analyzes the research progress of cross-domain guidance at home and abroad, combs the key technologies of cross-domain guidance, summarizes the current challenges and problems in cross-domain guidance research, and proposes development suggestions. © 2022 SPIE

关键词： Optical engineering

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Thermospheric Density Observations from APOD Satellites and Comparison with New Empirical Model 73

Thermospheric Density Observations from APOD Satellites and ...

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73rd International Astronautical Congress, IAC 2022

作者： Chen, Guangming Li, Xie Gao, Hong He, Maosheng Pan, Zhengxu Liu, Shushi Man, Haijun Science and Technology on Aerospace Flight Dynamics Laboratory Beijing Aerospace Control Center Beijing100094 China State Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing100190 China Kühlungsborn Germany School of Aeronautics Northwestern Polytechnical University Xi'an710072 China

The Chinese APOD (Atmospheric density detection and Precise Orbit Determination) satellites were launched in September, 2015. One of the main purposes of the mission is to observe thermospheric density around the terminator. The APOD-A satellite carries an atmospheric density detector, which can perform in-situ detection of atmospheric density with high temporal resolution. The latitudinal variation of thermospheric density has been broadly studied, which, however, mostly focused on the daytime and nighttime. This paper presents the latitudinal variation of thermospheric density at dawn and dusk, using observations from APOD for low solar activity conditions. We compare the APOD observations with the semi-empirical atmospheric model MSIS (Mass Spectrometer Incoherent Scatter Radar) 2.0 results. The density from the APOD around the terminator maximizes meridionally in the Polar Regions year-roundly, while the MSIS density maximizes in the subpolar regions at dusk for low solar activity conditions. © 2022 International Astronautical Federation, IAF. All rights reserved.

关键词： Satellites

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Power-Hardware-in-the-Loop Motor Emulator under Four-Quadrant Operation Condition

Power-Hardware-in-the-Loop Motor Emulator under Four-Quadran...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Gengchen Zhang Hong Guo Jinquan Xu Gaoxiang Wang Li Zhang Wenbo Jin Xiaopeng Zhao Ying Liu School of Automation Science and Electrical Engineering Beihang University Bejing China Southwest China Research Institute of Electronic Equipment Chengdu China Xian Flight Automatic Control Research Institute Xi’an China National Key Laboratory of Science and Technology on Aircraft Control Xi’an China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

In order to solve the problems of long cycle and the difficulty of test, this paper proposes a method of power-in-the-loop motor simulator. Firstly, the topology of the motor simulator is proposed, composed of an interface filtering unit, a motor simulation unit and an energy management unit. Secondly, a discretization method for permanent magnet synchronous motor(PMSM) is designed for DSP control. Thirdly, this paper proposes a method combining quasi-proportional resonance control(QPR) and direct current control to ensure the control accuracy. Finally, the motor simulator is verified by experiments.

关键词： Current control Accuracy Filtering Motors Resonance Permanent magnets Power electronics

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances

GPIO Based Iterative Learning Control for Manipulators with ...

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第42届中国控制会议

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University School of Artificial Intelligence and Automation Huazhong University of Science and Technology Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators,a composite iterative learning control(ILC) scheme via generalized proportional integral observer(GPIO) is proposed.A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics,parameter uncertainties and load fluctuations.A GPIO is then designed to estimate the disturbances and high-order time *** introducing estimations into a PD-type ILC at each iteration,a composite control scheme is obtained,such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of *** this paper,the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to *** effectiveness of the proposed control scheme is finally verified by simulation results.

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Taxi Time Prediction by Using Data Driven Approach:A New Perspective

SSRN

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SSRN 2022年

作者： Jiao, Qingyu Li, Nan Zheng, Zhigang Feng, Zan Qiang, Yigeng Chen, XinFeng China Academy of Civil Aviation Science and Technology China Civil Aviation Flight Wide Area Surveillance and Safety Control Technology Key Laboratory Civil Aviation University of China China College of Air Traffic Management Civil Aviation University of China China Northwest Air Traffic Administration of Civil Aviation of China China

Taxi time, as one of the key indicators to evaluate the airport operation efficiency, will not only have a direct impact on the takeoff and landing sequence of the runway, but also affect the operational efficiency of the airport. Nevertheless, previous research for taxi time prediction based on data-driven approach was mainly leveraged by regression algorithm, which rarely analyze the impact of uncertainty parameters on the accuracy of prediction, resulting in low utilization when the environment of the airport (such as traffic flow, runway configuration, etc.) changes. Hence, we proposed a deep learning model to predict taxi time based on dynamic parameters. First, according to the airport operation characteristics, parameters were divided into static parameters (aircraft type, taxiing distance, etc.) and dynamic parameters (airport traffic flow). Second, in order to capture the trend of the dynamic parameters, an attention mechanism based long-short-term memory combined with a deep neural network (D-LSTM) method is proposed to predict airport traffic flow. Last, a nested deep learning model that combined D-LSTM is established to predict the taxi time. We compare our new method with the state-of-the-art algorithms by using Hong Kong International Airport data, the result showed that the prediction accuracy of taxi-out time under nested deep learning model is 0.86 and the prediction accuracy of taxi-in is 0.90. In both cases, the prediction accuracy is significantly better than other algorithms. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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RASTE: A Relation-Guided Joint Method for Aspect Sentiment Triplet Extraction

RASTE: A Relation-Guided Joint Method for Aspect Sentiment T...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Linyang Zhong Jiaxing Shang Linjiang Zheng Fei Hao Weiwei Cao Hong Sun Key Laboratory of Dependable Service Computing in Cyber Physical Society College of Computer Science Chongqing University Chongqing China School of Computer Science Shaanxi Normal University Xian China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China Flight Technology and Flight Safety Research Base Civil Aviation Flight University of China Guanghan China

Aspect Sentiment Triplet Extraction (ASTE) is an important subtask of aspect-based sentiment analysis. It aims to extract aspect terms, corresponding sentiment polarities and related opinion terms from a sentence. The most advanced research efforts solve this problem in an end-to-end manner. However, they usually use one classifier to handle both relation detection and sentiment polarity classification tasks for aspectopinion pairs, which affects the prediction performance of the classifier. Moreover, the previous works treat subtasks equally, ignoring the differences between subtasks. In this paper, we propose a relation-guided method that models the relation detection and sentiment polarity classification subtasks differently to extract aspect sentiment triplets more effectively. Further, we capture the corresponding contextual features for each subtask separately to improve the overall performance of the model. We conducted experiments on two group benchmark datasets. Our model turns out to be one of the state-of-the-art models with effectiveness and superiority according to the experimental results.

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A Method for Reconstructing the Shape of Wing Panels Based on Fiber Optic Sensors and Tomographic Imaging Principles

A Method for Reconstructing the Shape of Wing Panels Based o...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Jun Zhan Jie Zeng Chao Xiang Jinpeng Cui Cheng Zeng Linfeng Zhu Mingyu Lu State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China Aviation key laboratory of science and technology on aero electromechanical system integration Nanjing engineering institute of aircraft system jincheng AVIC Nanjing China Changping District Xiaotangshan Future Science and Technology City COMAC Beijing Aircraft Technology Research Institute Beijing China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

The aircraft wing structure shape reconstruction method based on discrete strain information not only provides data support for the digital collaborative health monitoring and prediction technology of wing structure but also helps to improve the aerodynamic shape and flight quality of the aircraft, achieve structural stealth, and enhance the pointing accuracy of the on-board conformal antenna. As result, a wing skin panel shape reconstruction method based on the principle of tomographic imaging and strain information perception is proposed. According to the idea of tomographic imaging, a “non-intrusive” layout form that only requires sensors to be arranged on the periphery of the monitoring area is designed, breaking through the limitation that sensors can not be arranged in the monitoring area under special working conditions. A method for constructing the deflection shape function of the wing skin panel based on the concept of synthetic curvature is proposed, solving the problem of error accumulation and elimination of deformation inversion at non-monitoring points of the measured structure caused by the uncertainty of the limited discrete layout of sensors and the choice of interpolation fitting method. On this basis, a model deformation monitoring and inversion system for wing skin panel based on fiber optic grating sensors is constructed. The experimental results show that under the single-end fixed bending and torsion working condition, the average relative error of the panel model shape reconstruction is 4.17%. Under the double-end fixed double-concentrated load working condition, the average relative error of the shape reconstruction is 8.07%. The wing deformation distribution information reconstructed by the method proposed in this paper can be mapped onto the virtual twin model of the wing, providing technical support for the safety assessment, early warning, and on-condition maintenance of the entire service life of key structures of the aircraft.

关键词： Employee welfare Shape Deformation Layout Imaging Sensors Aircraft Monitoring Image reconstruction Strain

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Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint

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science China(Information sciences) 2020年第11期63卷 195-207页

作者： Yuyan GUO Bin XU Weixin HAN Shuai LI Yueping WANG Yu ZHANG School of Automation Northwestern Polytechnical University Department of Computing The Hong Kong Polytechnic University Flight Automatic Control Research Institute State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

This paper investigates the state-constrained controller design of a hypersonic flight vehicle(HFV) based on an asymmetric barrier Lyapunov function(ABLF). The robust adaptive back-stepping controller with integral terms is applied for the HFV longitudinal dynamics. Considering the asymmetric angle of attack(AOA) constraint caused by the unique structure and scramjet, the controller is modified by constructing an ABLF, where the asymmetric constraint on AOA tracking error is introduced. Combined with the constraint on virtual control, the AOA is restricted to a predefined asymmetric interval. The system stability and the AOA constraint are guaranteed via Lyapunov analysis. Simulation results verify that the AOA can be kept in the given asymmetric interval while the altitude reference signal is tracked.

关键词： hypersonic flight vehicle HFV integral back-stepping control adaptive control asymmetric barrier Lyapunov function ABLF angle of attack (AOA) constraint

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A Super-Long-Term Prediction Method of Earth Polar Motion Based on Spectrum Analysis 12th

A Super-Long-Term Prediction Method of Earth Polar Motion Ba...

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12th China Satellite Navigation Conference, CSNC 2021

作者： Lu, Weitao Chen, Lue Zhou, Zhijin Han, Songtao Ren, Tianpeng National Key Laboratory of Science and Technology on Aerospace Flight Dynamics Beijing Aerospace Control Center No. 120 Box 5130 Haidian District Beijing China

ISBN: (纸本)9789811631375

The super-long-term prediction technology of Earth Orientation Parameter (EOP) is a key foundation in autonomous orbit determination for earth-orbit spacecraft. Currently the classical prediction method of EOP is LS + AR, while the accuracy of periodic components and the corresponding frequency of EOP in the LS model is to affect the prediction performance. This paper focuses on the super-long-term prediction method of the polar motion. Firstly, the spectrum analysis of the 14C04 data series released by IERS is done to confirm the periodic components. Secondly, the periods of Chandler motion and annual motion are estimated by using spectral centroid method, which are about 433.75 days and 365 days respectively. Furthermore, the fence effect of FFT and prediction error are used to testify the correctness of the periods. Finally, the 180-day, 365-day and 730-day prediction of polar motion are conducted and compared with the observed value released by IERS, showing that the corresponding prediction error are 12 mas, 20 mas, and 55 mas. The results of the paper can be used as a good reference for long-term prediction of EOP. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrum analysis

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A Study for the Effectiveness of the Deep Feature of Eog in Emotion Recognition

SSRN

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SSRN 2024年

作者： Wu, Minchao Li, Ping Lv, Zhao Fan, Cunhang Pei, Shengbing Gao, Xiangping Li, Fan Liang, Wen Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Civil Aviation Flight University of China Chuanghan 618307 China Google Research CA94043 United States

Background: Emotion recognition is the essential problem for building the human-computer interface (HCI) system which make the HCI more intelligent. To date, the emotion recognition is mainly achieved by decoding electroencephalogram (EEG), speach, etc, while hardly by electro-oculography (EOG).New method: In this study, we proposed a deep learning model, VCLSTM, which is combined with variational mode decomposition (VMD), convolution neural network (CNN), and long-short term memory (LSTM), to perform the EOG-based emotion recognotion. More concretely, we employed VMD to calculate the intrinsic mode functions (IMF) of EOG signals and emphysize the different frequency information. Furthermore, we sequentially utilized CNN and LSTM to extract the deep temporal features and frequency-sequential features from IMFs, ***: Two public avalible datasets, i.e., DEAP and OVPD-II, were employed to evaluate the performance of the VCLSTM. For DEAP, we achieved mean accuracy of 92.09% for four classes (high/low valence/arousal);For OVPD-II which consisted of traditional video pattern and olfactory-enhanced pattern, we achieved mean accuracies of 93.74% and 92.09% for positive, neutral, and negative emotions, ***: We found that the vertical EOG generally outperformed the horizontal EOG. Besides, the experimental results also indicated that olfaction could affect the ability for vision to percepect emotional information, especially for negative emotions. © 2024, The Authors. All rights reserved.

关键词： Variational mode decomposition

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