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An approach for formation design and flight performance prediction based on aerodynamic formation unit:Energy-saving considerations

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Chinese Journal of Aeronautics 2024年第3期37卷 77-91页

作者： Nanxuan QIAO Tielin MA Xiangsheng WANG Jie WANG Jingcheng FU Pu XUE School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Institute of Unmanned System Beihang UniversityBeijing 100191China Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System Ministry of Industry and Information TechnologyBeijing 100191China School of Transportation Science and Engineering Beihang UniversityBeijing 102206China Beijing Institute of Space Long March Vehicle Beijing 100076China

The performance improvement of swarm drones through aerodynamic shape optimization may be challenging due to folded size constraints imposed by the specific launch ***,fixed-wing aircraft swarms can benefit from formation flight in terms of energy *** study introduces the concept of the"aerodynamic formation unit",which consists of two or three aircraft that form an inseparable unit of the *** the Unmanned Aerial Vehicle(UAV)distribution and wingtip vortex interference in the formation,two typical aerodynamic formation units are optimized by the variable-fidelity aerodynamic optimization method based on space *** aerodynamic characteristics of the formation UAVs that affect flight performance,such as lift-to-drag ratio(L/D ratio)and static stability are analyzed by Computational Fluid Dynamics(CFD)*** L/D ratio(cruising condition)of the following aircraft can be increased by 22.8%and 57.5%in the optimal units that involve two and three aircraft ***,this study conducts several CFD simulations for multi-aircraft formations formed by the units,which show that the average L/D ratio of the formation can be improved by more than 19%.These results verify the feasibility and effectiveness of the"aerodynamic formation unit"concept and the optimization framework for formation parameters.

关键词： Formation flight CFD Aerodynamic formation unit Wingtip vortices Foldable tandem-wing aircraft

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement 24

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Liu, Hongli Zhu, Shaoxian Yang, Liu Li, Qiang Lin, Jingran University of Electronic Science and Technology of China School of Information and Communication Engineering China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China

ISBN: (纸本)9781665496261

The Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers' fingerprints are inevitably introduced, and different receivers affect emitters' fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers' effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters' features are disentangled from the receivers', through multi-receiver's training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination. © 2023 IEEE.

关键词： Deep learning

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Image Sample Generation of Stator Surface Defects Based on Layer Mask Blending Generative Adversarial Network 23

Image Sample Generation of Stator Surface Defects Based on L...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Li, Wenzheng Tian, Lihua Sun, Zhigang Xiao, Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450399029

In industrial production processes, defect inspection plays an important role in reducing the occurrence of failures and improving production efficiency. Data-driven algorithms represented by deep learning have made great progress in recent years, but need to face the problems of small quantity and poor quality of datasets when applied to industrial defect inspection. This paper proposes a layer mask blending-based generative adversarial network (LMBGAN) and optimizes the training process to generate high-quality surface defect samples. LMBGAN generates defect images and layer masks using the defect image decoder and layer mask decoder with the Pixel Shuffle operation. Inspired by the layer mask in computer painting, LMBGAN adopts the input image as the base layer and blends the defect foreground through the layer mask, giving it the ability to focus more on generating upper-layer defect images and reducing unnecessary background changes. LMBGAN additionally introduces adaptive discriminator augmentation and non-saturating logistic loss to promote model convergence under small datasets, effectively alleviating the problem of GAN training difficulties with limited data. The experiment results show that the proposed method can generate high-quality and diverse defect image samples through easily accessible normal samples, thus reducing the difficulty of obtaining rare defect image samples. © 2023 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Research on Fault Diagnosis Method for Rolling Bearings Based on Improved ConvNext V2

Research on Fault Diagnosis Method for Rolling Bearings Base...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Qin, Feifan Zhang, Chao Wang, Jianguo Wu, Le Wu, Yangbiao Ouyang, Bing Liu, Guiyi College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China

ISBN: (纸本)9783031734069

Aiming to address issues such as insensitivity to fault characteristics, inadequate feature extraction, and susceptibility to external environmental interference in traditional bearing fault diagnosis methods, an improved approach to rolling bearing fault diagnosis based on ConvNext V2 is proposed. Firstly, leveraging the advantages of wavelet time-frequency analysis, one-dimensional vibration signals collected from the experimental setup are transformed into two-dimensional images containing abundant time-frequency information using continuous wavelet transform. Secondly, the next-generation convolutional neural network ConvNext V2 is introduced as the underlying network for rolling bearing fault diagnosis, and the Efficient Channel Attention (ECA) network is incorporated to enhance the feature extraction capability of the Block module in the base network. Subsequently, the wavelet time-frequency images are used as feature map inputs to the improved ConvNext V2 network for training, enabling the diagnosis and recognition of rolling bearing faults. Finally, experimental results demonstrate that the proposed method exhibits good fault recognition accuracy and stability in laboratory data, with high generalization capability, thereby offering a new solution for rolling bearing fault diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Roller bearings

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Modeling of Fluid Catalytic Cracking System Based on Improved ELM-Hammerstein Model

Modeling of Fluid Catalytic Cracking System Based on Improve...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Enhao Zhu, Haibo Zhou, Fan Zhao, Jun Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350303759

The complex process mechanism of fluid catalytic cracking (FCC) and the strong nonlinearity of multi-variable coupling bring significant challenges to the model predictive control (MPC) of FCC production process models. This study presents a modeling method of FCC system based on improved extreme learning machine Hammerstein (IELM-Hammerstein). Aiming at the nonlinear characteristics of FCC process, the Hammerstein model's static nonlinear subsystem is constructed using an extreme learning machine (ELM). Considering the instability of the algorithm caused by random weights and thresholds in the ELM, the sparrow search algorithm (SSA) is adopted to calculate the parameters, then one recursive least square (RLS) method is derived from calculating the linear subsystem regression parameters of Hammerstein model. To prove the effectiveness of this method, the comparative experiments with the traditional identification method are carried out, and this method is applied to modeling and control of the fractionating system in a petroleum refinery. The experimental results indicates that the proposed method guarantees the modeling accuracy of the fractionating system, and improves the tracking control performance. © 2023 IEEE.

关键词： Fluid catalytic cracking

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A Novel Principal Component Analysis Algorithm for Fault Detection

A Novel Principal Component Analysis Algorithm for Fault Det...

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第43届中国控制会议

作者： Wang Shenhang Tian Kun Zheng Wei Liu Mengyu National Key Laboratory of Science and Technology on Aerospace Intelligent Control

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In modern industry,fault detection is very important in high-cost and safety-critical *** component analysis(PCA) is an effective data-driven approach which is widely used in this *** PCA is time-invariant,which often lead to false *** order to overcome the false alarms,recursive PCA algorithms have been proposed for adaptive ***,the amount of calculation of the algorithm is very great because of frequently updated *** this paper,a novel recursive PCA method is proposed for solving the question by introducing the standard residual *** standard residual method serves as an indicator of the level of consistency among data,which is used to decide whether or not to update the PCA *** proposed method is applied to fault detection and experiment results demonstrate the effectiveness of the method.

关键词： Principal component analysis fault detection standard residual method

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Bearing Fault Diagnosis Based on an Improved Morphological Filter

Bearing Fault Diagnosis Based on an Improved Morphological F...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ren, Xueping Guo, Liangjian Qiu, Fuqiang Liu, Tongtong School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

Extraction of repetitive shock signals from vibration signals is crucial in bearing fault detection. Morphological filtering can extract the signal components based on the geometric features of the signal. In this paper, we propose an improved morphological filter for extracting fault cycle pulses from vibration signals in the time domain. Morlet wavelets are used as the main waveforms of the structural elements, which guide the construction of the structural elements based on their correlation with the defect-induced pulse, the Teager energy operator (TEO) is used to reduce the modulated signal interference. The improved morphological filtering algorithm can obtain fault features from low signal-to-noise ratio signals. The processing results of one simulated signal and two sets of experimental signals verify the effectiveness and robustness of the method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wiener filtering

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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