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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Leyao Chang, Jiansong Guo, Jianxin Beijing Institute of Control Engineering Beijing100094 China Science and Technology On Space Intelligent Control Laboratory Beijing100094 China

ISBN: (纸本)9789819969272

Global Navigation Satellite system (GNSS) is initially designed to provide services to objects on the earth surfaces, or in the earth-orbits with altitude below 3000 km. However, previous researches show that GNSS receivers mounted on GEO satellites can receive GNSS signals and generate positioning and navigation results as well. Considering that in some harsh situations, such as bad observation geometry, weak signal acquisition, etc., the on-board GNSS receivers can only transfer pseudo-range data to GEO satellites, this paper proposed an autonomous navigation method in which the information from GNSS receiver, optical attitude sensors, and inter-satellite links are fused together: at first, the extended state equation and measurement equation of a GEO satellite with optical sensor’s errors are deduced;then, an extended Kalman filter is designed to evaluate and compensate optical sensor’s errors via constructed earth-centered vector bias;later, a sequential correction is carried out using inter-satellite link measurements;finally, the performance of proposed method is analyzed through Monte-Carlo simulation. It shows that the average distance and velocity evaluation errors of proposed method are around 0.2 km and 0.05 m/s individually;the performance can meet the requirements of survivability of a class of high-orbit, long-life and high-value satellites. © 2024, Aerospace Information Research Institute.

关键词： Errors

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A Multi-constraint Autonomous Orbit control Scheme for Formation Flight 11th

A Multi-constraint Autonomous Orbit Control Scheme for Forma...

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11th International Conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Liu, Jie Xu, Yang Chang, Luonan Guo, Zixi Gong, Ligang Gao, Yong Beijing Institute of Control Engineering Beijing100190 China Science and Technology on Space Intelligent Control Laboratory Beijing100190 China

ISBN: (纸本)9789819721153

In order to improve the autonomous operation capability of the long-life satellite with dual satellite autonomous formation flight function requirements during its lifetime, a multi constraint autonomous orbit control scheme for formation flight is proposed. First, the thruster optimization sequence is determined according to the satellite centroid change and thruster installation. Second, real-time hydrazine amount of each thruster is calculated and thus the thruster and target attitude for this orbit control task is able to be settled. Third, both real-time thrust and post-control thrust are estimated by the pressure sensor, followed by thrust coefficient calibration via the measurement of semi-major axis. Experimental results have been verified in orbit that our method can realize autonomous orbit control tasks in various working conditions such as accompanying flight, flying around, and holding point maintenance. Hence, this control scheme can be extended to future satellite networking, which is of great significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Calibration

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Semisupervised Transfer Boosting (SS-TrBoosting)

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第7期5卷 3431-3444页

作者： Deng, Lingfei Zhao, Changming Du, Zhenbang Xia, Kun Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China and also with Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518063 China

Semisupervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused on learning transferable representations across domains. However, it is difficult to find a feature space where the source and target domains share the same conditional probability distribution. Additionally, there is no flexible and effective strategy extending existing unsupervised domain adaptation (UDA) approaches to SSDA settings. In order to solve the above two challenges, we propose a novel fine-tuning framework, semisupervised transfer boosting (SS-TrBoosting). Given a well-trained deep learning-based UDA or SSDA model, we use it as the initial model, generate additional base learners by boosting, and then use all of them as an ensemble. More specifically, half of the base learners are generated by supervised domain adaptation, and half by semisupervised learning. Furthermore, for more efficient data transmission and better data privacy protection, we propose a source data generation approach to extend SS-TrBoosting to semisupervised source-free domain adaptation (SS-SFDA). Extensive experiments showed that SS-TrBoosting can be applied to a variety of existing UDA, SSDA, and SFDA approaches to further improve their performance. © 2020 IEEE.

关键词： Supervised learning

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年第7期47卷 5350-5367页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Layout Three-dimensional displays Image reconstruction Estimation Cameras Floors Cognition Transforms Training Semantics

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Distributed robust iterative learning control for AC microgrids with external disturbances

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science China(Information sciences) 2025年

作者： Hongyu SU Jingang LAI School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of Education Huazhong University of Science and Technology

This study proposes a distributed secondary control scheme based on distributed robust iterative learning control(DRILC) for islanded alternating current(AC) microgrids subjected to external disturbances. By employing the decoupled sliding mode consensus approach, voltage regulation, frequency restoration, and accurate active power sharing can be achieved within a finite time on the proposed novel integral terminal sliding mode(ITSM) manifold. Furthermore, the appropriate iterative update law in the ITSM-based controller is utilized to learn and eliminate external disturbances as effectively as possible. In the proposed control scheme, the iterative learning control and sliding mode control are designed to function in a complementary manner, which enhances the performance of the secondary control scheme for multi-objective regulations. The stability criteria and robustness to external disturbances of the closed-loop microgrid system in the iteration and time domains are also rigorously derived with the help of the Lyapunov direct method. Finally, the effectiveness of the proposed secondary control scheme is validated by case studies of an islanded AC microgrid test system in the MATLAB/SimPowerSystems software environment.

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng 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: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 547-555页

作者： Kun Zhu Chengpu Yu Yiming Wan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output *** objective is to enhance parameter estimation performance under non-persistent *** proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is *** proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite ***,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed *** simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

关键词： Non-persistent excitation oblique projection recursive least squares(RLS) variable-direction forgetting(VDF)

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Ground-based investigations on phase-moving phenomenon with space sublimation cooling for lunar exploration missions

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Chinese Journal of Aeronautics 2022年第8期35卷 65-74页

作者： Enhui LI Yunze LI Jixiang WANG Man YUAN Jingyan XIE Yuehang SUN Lizhu YANG Xianwen NING School of Aeronautic Science and Engineering Beihang UniversityBeijing 100083China Beijing Key Laboratory of Space Thermal Control Technology China Academy of Space TechnologyBeijing 100094China

The lunar surface is a typical vacuum environment,and its harsh heat rejection conditions bring great challenges to the thermal control technology of the exploration *** addition to the radiator,the sublimator is recommended as one of the promising options for heat *** sublimator makes use of water to freeze and sublimate in a porous medium,rejecting heat to the vacuum *** complex heat and mass transfer process involves many physical phenomena such as the freezing and sublimation phase change of water in the porous medium and the movement of the phase-change *** this paper,the visualized ground-based experimental approaches of space sublimation cooling were presented to reveal the moving law of threephase point and the growth phenomenon of ice-peak and icicle in microchannels under vacuum *** visualized experiments and results prove that the freezing ice is divided into the porous ice-peak and the transparent *** the sublimation progresses,the phase-change interface moves downward steadily,the length of the ice-peak increases,but the icicle *** visualized experiments of space sublimation cooling in the capillary have guiding significance to reveal the sublimation cooling mechanism of water in the sublimator for lunar exploration missions.

关键词： Lunar vacuum environment Phase-change flow space sublimation cooling Unsteady phase-moving phenomenon Visualized ground-based experimental approaches

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Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5805-5820页

作者： Qi, Biqing Gao, Junqi Chen, Xinquan Li, Dong Liu, Jianxing Wu, Ligang Zhou, Bowen Tsinghua University Department of Electronic Engineering Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Harbin Institute of Technology Department of Control Science and Engineering Harbin China Harbin Institute of Technology School of Mathematics Harbin China

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods. © 1991-2012 IEEE.

关键词： Prototypes Adaptation models Power capacitors Feature extraction Data models Accuracy Vectors Training Incremental learning Overfitting

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification From Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20 000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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