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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Song, Ting Zhang, Xianliang Ning, Lei Pan, Xiao Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Space Intelligent Control Technology Shanghai201109 China Northwestern Polytechnical University Xi’an710068 China

ISBN: (纸本)9789811966125

A novel spacecraft attitude coordination control strategy for rapid construction of laser link in the gravitational wave detection mission was proposed in this paper. Uncertainty cone caused by the orbital navigation error would be reduced by adjusting the installation angle between the Star Tracker and Telescope with the method. Therefore, the number of laser scans will be reduced and the laser link build time will be shortened. Numerical results are presented to quantify the number of laser scans and to demonstrate the effectiveness of the control strategy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Signal detection

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Prescribed-Time Stabilization of Singularly Perturbed Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 569-571页

作者： Yan Lei Yan-Wu Wang Xiao-Kang Liu Wu Yang the School of Artificial Intelligence and Automation Huazhong University of Science and Technology(HUST)Wuhan 430074 Key Laboratory of Image Processing and Intelligent Control Ministry of EducationWuhan 430074China the School of Electrical Engineering Guangxi UniversityNanning 530004China

Dear Editor, This letter investigates the prescribed-time stabilization of linear singularly perturbed systems. Due to the numerical issues caused by the small perturbation parameter, the off-the-shelf control design techniques for the prescribed-time stabilization of regular linear systems are typically not suitable here. To solve the problem, the decoupling transformation techniques for time-varying singularly perturbed systems are combined with linear time-varying high gain feedback design techniques.

关键词： prescribed stabilization perturbed

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Shallow Sleep control Management for Low-Power Rockets 9

Shallow Sleep Control Management for Low-Power Rockets

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9th International Conference on Advances in Machinery, Materials science and Engineering Applications, MMSE 2023

作者： Zhang, Ying Sun, Kai Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China

ISBN: (纸本)9781643684383

In this paper, a control method for shallow sleep management of low-power rockets is studied, and three sleep modes are verified for shallow sleep, and the optimal adaptation of shallow sleep of rockets with minimum power consumption of 16 watts is confirmed In different application environments, multi-mode operation with low power consumption is realized, so as to achieve the goal of long life under the requirements of long-term power up. The low-power mode can make the system run more stably, help reduce system losses, effectively reduce the volume and weight of power supply equipment, and improve the carrying capacity of the rocket. In order to achieve the purpose of low power consumption, based on the principle of 'high and low matching', the performance matching of chips of different performance levels at their optimal working points is analyzed, and the low-power chip architecture design under different computing power requirements is realized, and experimental verification is carried out. © 2023 The Authors.

关键词： Electric power supplies to apparatus

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MMLN: Multi-directional and Multi-constraint Learning Network for Remote Sensing Imagery Semantic Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 1-16页

作者： Sun, Hang Xie, Yongchang Ren, Dong Wen, Fangqing Tong, Lyuyang Chang, Laibin Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive Technology Shiyan China School of Computer Science Wuhan University Wuhan China

Recent advances in satellite remote sensing technology and computer technology have significantly impacted practical applications in remote sensing image segmentation. However, the prevalent hybrid segmentation models that combine Convolutional Neural Networks (CNNs) and Transformers, often overlook the critical exploration of local and global feature correlations across various scales. This exploration is essential for learning more representative features and strengthening context modeling capabilities. Additionally, the decoding layers of these models do not effectively exploit the pixel-level semantic relationships within cross-layer feature maps, thereby limiting the models' ability to discern small object features. To address these challenges, this paper introduces a Multi-directional and Multi-constraint Learning Network (MMLN) designed for semantic segmentation of remote sensing imagery. This network features a Multi-directional Dynamic Complement Decoder (MDCD), which enhances the interaction between local and global features in the feature space, and subsequently improves the feature discrimination within the segmentation network. Moreover, a Multi-constraint Saliency Boundary-adaptive Module (MSBM) is implemented to reinforce the spatial constraints on saliency at the edge regions and ensure semantic consistency along the mask boundaries. This, in turn, augments the segmentation model's capability to detect small objects. The evaluation on four datasets reveals that the MMLN outperforms the existing state-of-the-art methods in remote sensing imagery segmentation. The code is available at https://***/zhongyas/MMLN. Authors

关键词： Semantics

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Portrait Matting via Semantic and Detail Guidance 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Deming Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Portrait matting is a challenging computer vision task that aims to estimate the per-pixel opacity of the foreground human regions. To produce high-quality alpha mattes, the majority of available methods employ a user-supplied trimap as an auxiliary input. However, obtaining trimap is difficult, which restricts the application of these methods. Recently, trimap-free approaches have been developed, but the alpha quality is significantly lower than that of trimap-based methods due to the lack of guidance. In this paper, we propose a portrait matting framework via semantic and detail guidance. We use the mask obtained from portrait segmentation as semantic guidance to improve semantic understanding of portrait matting, and we present a detail encoder block to capture the low-level feature around the subject as detail guidance, which helps to refine alpha details, such as hair. The proposed approach achieves remarkable results on both composition and real-world benchmarks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

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science China(Information sciences) 2024年第8期67卷 130-146页

作者： Yingjian LIU Jiang LI Xiaoping WANG Zhigang ZENG School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Institute of Artificial Intelligence Huazhong University of Science and Technology

Emotion recognition in conversation(ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention(IMMHA), dialogue-based gated recurrent unit(DiaGRU), and skip-chain conditional random field(SkipCRF).Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while Dia GRU is utilized to extract speaker-and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark *** ablation studies confirm that our modules can effectively model emotional inertia and contagion.

关键词： emotion recognition in conversation emotional inertia and contagion multi-head attention gated recurrent unit conditional random field

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Feedback Generative Adversarial Network With Channel-space Attention for Image-Based Optimal Path Search Planning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4293-4307页

作者： Sun, Tao Li, Jian-Sheng Zhang, Yi-Fan Ru, Xin-Feng Wang, Ke Tsinghua University Department of Automation Beijing100084 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tongji University School of Economics and Management Shanghai200092 China

Path planning and seeking is one of the most challenging and interesting problems in the field of artificial intelligence. In this article, we propose a new image-based path planning algorithm to overcome traditional design concepts based on complex topological networks. Specifically, a novel feedback generative adversarial network learning model with channel-space attention (CSA-FGAN) is developed to search and plan an optimal path. It is worth noting that the input and output of the proposed feedback learning model are RGB images with complex spatial obstacle environment maps and RGB images with feasible paths, respectively. In particular, in order to further enhance the feature extraction ability and path prediction accuracy of image data, the feedback attention mechanism controls and improves the depth of the network by forming a closed loop of the proposed learning model. Final, three application scenario image examples are conducted to verify the superiority of the proposed path planning policy. © 2020 IEEE.

关键词： Motion planning

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Reinforcement Learning control for Hypersonic Morphing Flight Vehicle with Identification of Dynamic Parameter

Reinforcement Learning Control for Hypersonic Morphing Fligh...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Nie, Ying Zhang, Yuan Bai, Wenyan Cao, Yuteng Yu, Chunmei Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China

ISBN: (纸本)9789811966125

A reinforcement learning (RL) controller with identification of the dynamic parameter of hypersonic morphing flight vehicle (HMFV) is proposed in this paper, successfully realizing the end-to-end control of attack angle in the longitudinal plane. The following improvements are made in this paper: Firstly, the dynamic parameter (rudder efficiency coefficient) of the flight vehicle is added into the state vector, so that the RL controller can understand the control ability of the rudder and generates the optimal control commands in the current state. Secondly, five instead of only one consecutive attack angle deviations are used to jointly generate the state vector, which enables the RL controller to use the model state information of the previous period of time and improve the control stability. Three simulations are set up in this paper. The simulation results show that the RL controller proposed in this paper can achieve stable and high precision attack angle control under large-scale environmental deviations and has strong generalization under different guidance commands. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Generation of Sub-Nanosecond Microwave Pulse with High Compression Factor Using Reflection Structure

Generation of Sub-Nanosecond Microwave Pulse with High Compr...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Zhang, Zhiyuan Pan, Ruoyang Wang, Weijie Wu, Zhiqiang Liu, Guo Yao, Yelei Jiang, Wei Wu, Zeiwei Wang, Jianxun Luo, Yong School of Integrated Circuit Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (纸本)9798350375909

This paper presents a method of generating sub-nanosecond microwave pulses with a high compression factor using a reflection structure. This method can reduce the length of the compressor and has a high compression factor, which can further enhance the power level of broadband vacuum electron amplifiers. In the experiment, a microwave pulse with a duration of 0.257 ns and a high compression factor of 43.3 is generated. The experiment is in good agreement with our theory and simulation. © 2024 IEEE.

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Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

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Chinese Journal of Aeronautics 2022年第9期35卷 314-332页

作者： Yanhua MA Xian DU Ximing SUN School of Microelectronics Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.

关键词： Hybrid optimization Long short-term memory Model modification and testing Sensitivity analysis Turbofan engines

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