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AIAA science and technology Forum and Exposition, AIAA SciTech Forum 2021

作者： Qin, Decong Chen, Haibing Lin, Wei Tang, Peng Ma, Tielin Xu, Cheng School of Transportation Science and Engineering Beihang University Beijing China Aircraft/Engine Integrated System Safety Beijing Key Laboratory Beijing China Beijing System Design Institute of the Electro-mechanic Engineering Beijing China Institute of Unmanned System Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781624106095

Flexible air-breathing hypersonic vehicles show significant and complex aerodynamic, scramjet propulsion system, flight dynamic and elastic coupling characteristics, which usually are highly nonlinear. These result in huge uncertainties, parameter perturbation and internal disturbance, which can no longer be ignored, and therefore, extreme difficulties for flight control design. A unique parameter adaptive non-singular terminal sliding mode method is proposed for longitudinal control law design of a flexible coupling air-breathing hypersonic vehicle. This method uses adaptive reaching law gain instead of additional adaptive compensation term to handle the uncertainty to improve robustness. The stability of the close loop system is proved via a Lyapunov way. The longitudinal tracking control law for velocity and angle of attack is designed based on a rigid dynamic model of a flexible air-breathing hypersonic vehicle. A strong coupling model of the same vehicle, considering aerodynamic-scramjet engine-flight dynamic-elastic couplings, is established as the verification platform of control law designed. Simulation results obtained based on the coupling model demonstrate that the control law designed has good performance and acceptable robustness. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hypersonic vehicles

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Time series generator adversarial network with stochastic process for the degradation generation and prediction

Time series generator adversarial network with stochastic pr...

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Data Driven control and Learning systems (DDCLS)

作者： Anqi Shangguan Nan Feng Lingxia Mu Rong Fei Xinhong Hei Guo Xie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of computer science and Engineering Xi'an University of Technology Xi'an China

Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degradation to improve the accuracy of later reliability assessment. Therefore, a degradation generation and prediction method is proposed combining the time series generator adversarial network (TimeGAN) and stochastic process. Firstly, the input degradation is expanded by the sliding window to improve the later training accuracy; Then, the construction of the generator in TimeGAN is linked with the stochastic process to make the generation data more realistic. Finally, the results of degradation prediction by the Gated Recurrent Unit (GRU) can be obtained. Two datasets and different generation methods are adopted to evaluate the effectiveness of the proposed method. The results shows that the Kullback-Leibler(KL) divergence is the smallest, and the prediction error is the smallest compared with the other methods. So, the proposed method is proved that it is valid in the degradation generation and prediction, and can be used for the further reliability assessment of the product in the industrial system.

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Fast Vision Decoder: A Robust Automatic Target Recognition Model for Sar Images

SSRN

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SSRN 2022年

作者： Ayodeji, Abiodun Wang, Wenhai Su, Jianzhong Yuan, Jianquan Liu, Xinggao State Key Laboratory of Industrial Control Technology Control Department Zhejiang University Hangzhou310027 China Tianjin Jinhang Technical Physics Institute Tianjin300192 China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100074 China

The synthetic aperture radar (SAR) is the recent all-weather technology used to monitor areas with low to moderate penetration. However, the scarcity of SAR imagery, and the high-level noise in the images makes it difficult for real-time image interpretation. This work presents a novel fast vision decoder (FViD), a target recognition model with a frozen backbone and stack dilated autoencoder. The autoencoder architecture is introduced to learn the compressed latent representation. Moreover, a robust backbone selection method is introduced to select a task-specific optimal backbone. The proposed FViD is extensively evaluated using the Chinese TerraSAR-X ship images, and the US MSTAR datasets. Evaluation results show that the proposed method has significant improvement over the conventional models. The speed and accuracy of the proposed FViD are further compared with five state of the art models previously used for SAR target recognition tasks, and the comparison result shows that the FViD performs better. Moreover, an ablation study is performed to provide insights into the contribution of FViD architecture. The results of the ablation study as well as the performance improvement of FViD are discussed in this paper. © 2022, The Authors. All rights reserved.

关键词： Computer vision

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Cooperative Search Strategy of Multiple UAVs Based on Matrix Game 19

Cooperative Search Strategy of Multiple UAVs Based on Matrix...

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19th International Bhurban Conference on Applied sciences and technology, IBCAST 2022

作者： Song, Dalei Wang, Xiaoqian Dai, Aoliang Ma, Hongjun Wei, Donghui Shenyang University of Technology School of Information Science and Engineering Shenyang China Northeastern University School of Information Science and Engineering Shenyang China South China University of Technology School of Information Science and Engineering Guangdong China Beijing Research Institute of Mechanical & Electrical Technology Key Laboratory of Complex System Control and Intelligent Collaboration Technology Beijing100074 China

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

For the regional search problem of multiple unmanned air vehicles (UAVs) ,a cooperative search strategy of multiple UAVs is proposed based on action matrix game (AMG) for intelligent moving targets on the ground in this paper. Based on the proposed AMG, the yaw angle and position information of the target is described in probability matrix, and the matrix is updated by Nash equilibrium and receding horizon control in every control cycle. Simulation results show that this strategy deduces the searching time for revisiting the reference target, and improves the success rate for moving targets searching. © 2022 IEEE.

关键词： Game theory

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Distributed Observer-Based Simultaneous Fault Detection and Formation control of Multi-UAVs

Distributed Observer-Based Simultaneous Fault Detection and ...

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Chinese control Conference (CCC)

作者： Xiaowan Su Mingrui Hao Ruohan Yang Keyuan Yue Zhichao Feng Northwestern Polytechnical University Xi'an P. R. China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing P. R. China The Rocket Force University of Engineering Xi'an P. R. China

This paper studies the formation control and simultaneous fault detection of multi-UAVs. Firstly, a distributed observer is constructed for the second-order particle model of each UAV. Secondly, a novel formation controller is designed based on the distributed observer. Then, the faulty UAV is detected by the residual signal. Finally, based on Lyapunov theory and Projection Lemma, sufficient conditions for UAVs formation are obtained. The effectiveness of the proposed method is verified by simulation.

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Single-Photon LiDAR Depth Estimation Based on Time Series Correlation

Single-Photon LiDAR Depth Estimation Based on Time Series Co...

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Academic International Symposium on Optoelectronics and Microelectronics technology (AISOMT)

作者： Lin Ruobin Sun Jianfeng Jiang Peng Ma Le Zhou Xin Ren Zhen Ding Ji National Key Laboratory of Laser Spatial Information Harbin Institute of Technology Harbin Heilongjiang China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou Henan China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Tianjin Jinhang Technical Physics Institute Tianjin China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

With advancements in science and technology, photon-counting LiDAR applications are growing due to long-range measurement, high-definition imaging, and accuracy. Geiger-mode avalanche photodiodes (Gm-APD) provide single-photon sensitivity and picosecond resolution, but noise in depth estimation affects imaging accuracy. This study introduces a novel depth estimation method using time series correlation to mitigate these noise issues, improving accuracy in LiDAR imaging. Utilizing a photon-counting LiDAR system and time-correlated single photon counting (TCSPC), this study capture target echo photon flight times to maintain depth information. The proposed denoising approach incorporates multiple time series windows and Bayesian estimation to enhance depth accuracy, alongside a pruning algorithm to optimize computational efficiency. Evaluation metrics show significant improvements: RSNR improved by 4% and MAE decreased by 43 % compared to traditional methods. The findings demonstrate the robustness of this method across various scenarios, enhancing depth estimation and noise mitigation in photon-counting LiDAR technology, thereby providing practical improvements for a range of applications.

关键词： Correlation Laser radar Accuracy Filtering Depth measurement Time series analysis Noise reduction Imaging Bayes methods Photonics

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A belief rule-based fault diagnosis method for Multi-UAVs under missing attribute data

A belief rule-based fault diagnosis method for Multi-UAVs un...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： ErKai Zhao MingRui Hao RuoHan Yang KeYuan Yue DeLin Yu School of Computer Science and Information Engineering Harbin Normal University Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Electronics and Information Northwestern Polytechnical University Xi’an China School of Mathematics and Statistics Northwestern Polytechnical University Xi’an China

To reasonably improve the missing attribute data and effectively integrate sample data and uncertain expert knowledge, this paper proposes a new fault diagnosis method based on a belief rule base (BRB). In the case of missing attribute data, the maximum likelihood estimation (MLE) method is used to complete the missing attribute data, and the fault diagnosis method of the multi-UAVs is constructed by using other complete attribute data as the input information of BRB. Finally, the effectiveness of the proposed method is verified by experimental simulation.

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Fast Electromagnetic Scattering Modeling of complex Scenes Based on Multi-scale Coherent Computation 21st

Fast Electromagnetic Scattering Modeling of Complex Scenes B...

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21st Asia Simulation Conference, AsiaSim 2022

作者： Ningbo, Gong Guijie, Diao Hui, Chen Hong, Ni Xin, Du Zhe, Liu Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China South-East University Nanjing210096 China

ISBN: (纸本)9789811991943

Modeling of scattering characteristics between ground/offing targets and environment is one of the necessary conditions to realize the internal field reconstruction of complex electromagnetic scenes. According to the high-frequency approximation assumption of electromagnetic scattering localization, the coupling scattering electromagnetic modeling method of composite targets and ground/offing scenes is studied in the framework of the bouncing ray method. Based on the curvature adaptive bin separation algorithm and the principle of multi-scale coherent superposition, an integrated scattering modeling method for multi-scale targets and complex environments is developed. The scattering of typical ship target, background and the coupling scattering between ground/offing artificial targets and environment are calculated respectively. Theoretical analysis and simulation result show the correctness and effectiveness of the algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational electromagnetics

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Aircraft Autonomous Positioning Based on a Coarse to Fine Multi-modal Image Registration Scheme

Aircraft Autonomous Positioning Based on a Coarse to Fine Mu...

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2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021

作者： Sun, Fangde Fu, Wenxing Lu, Xiaohui Dong, Jing Han, Songlai Central South University School of Aeronautics and Astronautics Changsha China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing China People's Liberation Army 91039th Unit Beijing China National University of Defense Technology Aerospace Science and Engineering Changsha China

ISBN: (纸本)9781728190037

In this paper, an autonomous positioning method based on coarse-to-fine multi-modal image matching is proposed for UAV navigation in GPS denied environment. Coarse image matching refers to roughly determining the approximate position of the real-time image in the reference image. Fine image matching refers to iterative matching around the position obtained by coarse image matching to achieve more accurate matching results. Besides, RANSAC is adopted to eliminate the outliers of image matching. After getting the result of image matching, we use PNP to estimate the position of the aircraft. The proposed position method has the advantages of high accuracy and reliability. The experimental results show that the average error of the position of the proposed method is 59%. © 2021 IEEE.

关键词： Image matching

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WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Low-Light Enhancement

WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Lo...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Qiangqiang Wang Peiliang Huang Lei Li Longfei Han AHU-IAI AI Joint Laboratory Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Automation Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Technology and Business University Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal illumination. Currently, deep neural networks have become the mainstream approach for image processing. However, recent works have devoted considerable efforts to designing high-performance models, which often come with high computational complexity and inference time, making real-time processing unfeasible. We observed that some convolutional methods are due to the need for deep layers which results in a large number of parameters. Moreover, enhancing details and removing noise in low-light images remains an open challenge. In order to solve the above problems, we propose a lightweight baseline that combines CNN and sparse grid attention transformer blocks to enable the model to capture a global receptive field at an early stage. Specifically, we propose a High-Frequency Wavelet-aware Block(HFWB) that focuses on processing high-frequency information in the wavelet domain to refine details and suppress noise. With a processing time of only 10.6ms, the performance of our model outperforms that of the current state-of-the-art lightweight models on benchmark low-light datasets. Compared to state-of-the-art models in the LOL dataset, our model achieves a reduction in inference time of over 90% and requires only about 1% of the FLOPS.

关键词： Wavelet transforms Visualization Wavelet domain Noise Lighting Benchmark testing Transformers

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