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15th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2024

作者： Liu, Yuanshuai Li, Wei Chao, Tao National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781643685892

This paper addresses the interception mission of hypersonic vehicles by weapon defense systems. A simulation model of the attack-defense confrontation process is constructed based on the "F2T2EA" kill chain model. Building upon this framework, a novel method combining TOPSIS, the entropy weight method, and fuzzy theory is proposed, guided by the concept of kill chain closure, to evaluate the interception effectiveness of defense systems against boost-glide hypersonic vehicles. The model's effectiveness is verified through practical examples, with consistent conclusions obtained through traditional grey correlation analysis, thus confirming the feasibility and reliability of both the model and the evaluation methodology. The results of this research offer reliable technical support for the design and combat application of defense systems and provide a reference for future optimization efforts. © 2025 The Authors.

关键词： boost-glide vehicle grey correlation performance evaluation TOPSIS

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Multi-Targets Visiting Orbit Design Based on Numerical Method

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Guidance,Navigation and Control 2025年第1期05卷 139-148页

作者： Zenan Zhong Zhengxiang Sun Songyan Wang Tao Chao Ming Yang Control and Simulation Center Harbin Institute of Technology National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Beijing Institute of Space Long March Vehicle

In this paper, for the two/three targets in the circle orbit with the same altitude, a numerical method is presented to design an orbit for a spacecraft to visit multiple targets without maneuvering. First,for the two targets' case, an equation is established by the relationship of the two angles that both are the function of the time, one of the angles is the angle between two targets' geocentric position vectors at two times, and another one is the spacecraft flight angle during the two times. The visit time can be solved by the assumption that one of the visit times is known in the equation, and the orbit element can be transferred from the position and velocity vector determined by the solved visit time. Then, for the three targets' case, the problem is decoupled into two cases of two targets, and there is a public target in each case. Based on the method for two targets, two different orbits can be obtained by determining the visit time of the public target. The difference between two orbits can be expressed by the difference in velocity vectors of the public target at different times. A sampling method is utilized to select two velocity vectors with relatively small differences as initial values, the Newton iteration method is used to obtain visit times with higher accuracy. simulation results illustrate the effectiveness of the orbit design methods.

关键词： Orbit design method multiple targets visit circle orbit non-maneuvering

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Optimal Deployment of Target Localization With Distance-Dependent Noise

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IEEE Internet of Things Journal 2025年

作者： Tian, Junxi Yang, Ming Chao, Tao Harbin Institute of Technology Control and Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China

The cooperative localization of radio-based distance measurement is one of the methods to effectively suppress the nonline of sight (NLOS) ranging errors and as a complement to visual/global navigation satellite system (GNSS) localization for unmanned aerial vehicle (UAV) in GNSS challenging or denied environments due to its motility. Target localization can be regarded as a special case of cooperative localization. Meanwhile, the UAV can be considered as a ranging node when the ranging terminal is rigidly mounted on the UAV. To improve the target localization accuracy, the optimal deployment of anchor UAVs is one of the factors we consider when the noise characteristics are different. In this paper, the range-dependent noise in a real flight environment is considered and modeled separately according to the difference in noise characteristics. Subsequently, based on this correlation model, the Fisher information matrix (FIM) and a cost function derived from the A-optimal criterion are provided. Finally, the optimal deployment conditions and the minimum mean square error (MMSE) can be derived by minimizing the cost function as the objective. Through simulation and localization experiments, the validity of the optimal deployment conditions and the corresponding MMSE are verified. Meanwhile, it is concluded that when the noise is range-dependent, it will not affect the optimal deployment result but will significantly change the value of MMSE. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Dynamic-Aware and Static Context Network for large-scale 3D place recognition

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Knowledge-Based systems 2025年 319卷

作者： Liao, Ming Di, Xiaoguang Liu, Maozhen Lv, Teng Zhang, Xiaofei Zhu, Runwen Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

3D point cloud-based place recognition enables robots to obtain precise global positions without GPS, correct trajectory drift in SLAM, and recover from the kidnapped robot problem. However, in outdoor environments, the presence of moving objects can cause occlusions in point clouds and introduce noise into the data, leading to localization failures. To address this issue, we propose a Dynamic-Aware and Static Context Network (DASC-Net) for large-scale 3D place recognition. Our approach leverages the spatio-temporal consistency of point cloud sequences to accurately segment dynamic objects while incorporating static point cloud context to compensate for feature loss caused by noise interference or occlusions from dynamic objects, thereby enhancing robustness and generalization. Specifically, DASC-Net adopts a two-stage strategy: first, it introduces a coarse-to-fine moving object segmentation method to effectively eliminate dynamic noise;second, it utilizes spatial context association and multi-scale feature aggregation to improve static feature representation and matching. Extensive experimental results demonstrate that DASC-Net outperforms existing place recognition approaches, particularly in dynamic scenes. © 2025

关键词： Image segmentation

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Multi-scale Dilated Attention-based Generative Adversarial Network for Infrared and Visible Image Fusion

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Current Optics and Photonics 2025年第2期9卷 120-129页

作者： Yang, Shunyuan Shi, Xiaoping Huo, Ju Sun, Kangjian Wang, Yu School of Astronautics Harbin Institute of Technology Harbin150001 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Shanghai Space Propulsion Technology Research Institute Shanghai201109 China

In the field of optics, infrared and visible images are often required for use in different situations. In particular, in the field of optical sensors, infrared and visible sensors are mainly used to obtain different band images, which are applied to improve the comprehensive information content of images and to improve the quality and availability of images. In existing infrared and visible image fusion methods, the focus is often placed on retaining the background information from visible images and the salient targets from infrared images. To address this issue, we introduce a multi-scale dilated attention module into the encoder-decoder structure of the generator. By applying dilated convolution and selfattention mechanisms, this module improves the perceptual capability of the model, thereby improving performance without increasing network complexity. This design emphasizes gradient information and detailed features in visible images. Experimental results on the public TNO dataset demonstrate that our method achieves superior visual quality and preserves the most abundant image information. Moreover, experiments on spacecraft images validate the robustness and applicability of our approach. Simultaneously, our method also provides significant technical support for the optical field. © 2025 Current Optics and Photonics.

关键词： Image coding

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A PARALLEL DOMAIN DECOMPOSITION METHOD FOR SOLVING ELLIPTIC EQUATIONS ON MANIFOLDS

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SIAM Journal on Scientific Computing 2025年第2期47卷 A916-A942页

作者： Qin, Lizhen Wang, Feng Wang, Yun School of Mathematics Nanjing University Nanjing210093 China Key Laboratory of Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Ministry of Education Nanjing210023 China Jiangsu International Joint Laboratory of Big Data Modeling Computing and Application Nanjing Normal University Nanjing210023 China School of Mathematical Sciences Nanjing Normal University Nanjing210023 China School of Mathematical Sciences Center for Dynamical Systems and Differential Equations Soochow University Suzhou215006 China

We propose a new numerical domain decomposition method for solving elliptic equations on compact Riemannian manifolds. One advantage of this method is its ability to bypass the need for global triangulations or grids on the manifolds. Additionally, it features a highly parallel iterative scheme. To verify its efficacy, we conduct numerical experiments on some four-dimensional manifolds without and with boundary. © 2025 Society for Industrial and Applied Mathematics Publications. All rights reserved.

关键词： Domain decomposition methods

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Enhancing realism in LiDAR scene generation with CSPA-DFN and linear cross-attention via Diffusion Transformer model

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Neural Networks 2025年 189卷 107503页

作者： Ye, Shaoxun Di, Xiaoguang Liao, Ming Li, Ximing Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primarily arises from the loss of detailed information during the decoding process from latent space and the lack of guidance from global 3D structural information in the point cloud generation process, leading to distortions and artifacts in LiDAR scene samples. In this paper, we propose a novel LiDAR Diffusion Transformer Model that integrates Channel-Spatial Parallel Attention and Dilation Fusion Network (CSPA-DFN) with a linear cross-attention post-processing module to refine the generated LiDAR scene samples. Specifically, CSPA-DFN is designed to simultaneously emphasize detailed features across different channels and spatial locations in parallel, leveraging multi-scale dilated convolutions and channel grouping to preserve and enhance these detailed features. In order to provide global 3D structural information and balance performance and efficiency, we design a post-processing module that fuses voxelized features and range images using a linear ReLU cross-attention mechanism. Our approach is evaluated on the unconditional generation task using the KITTI-360 and nuScenes datasets, achieving the state-of-the-art results in LiDAR scene's generation quality. Furthermore, by incorporating semantic labels and camera views into the latent space, in addition to enhancing the model's semantic understanding capability for LiDAR scenes, our method also demonstrates additional performance improvements compared to previous works in terms of LiDAR scene's visual quality. The code implementation has been released on https://***/HITysx/LiDAR-Scene-Generation. © 2025 Elsevier Ltd

关键词： Photointerpretation

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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Parallax-tolerant unmanned aerial vehicle remote sensing image stitching based on optimal seams

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Journal of Electronic Imaging 2025年第2期34卷

作者： Tang, Xiyu Huo, Ju Li, Wei Liu, Qi Cai, Chen Harbin Institute of Technology School of Astronautics Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin China China Academy of Railway Sciences Signal and Communication Research Institute Beijing China

Low-altitude remote sensing technology using unmanned aerial vehicles (UAVs) has gained wide application. However, compared with satellites, UAVs have a smaller field of view and require image-stitching techniques. Existing stitching algorithms are generally only effective when processing images collected by drones at fixed positions but have poor performance on images collected by drones at different locations. This poses challenges in stitching due to large parallax caused by the movement of foreground objects, resulting in issues such as ghosts and misalignment. To address this, an algorithm based on deep features and optimal seams is proposed for large-parallax UAV remote sensing image stitching. In image registration, the algorithm uses LightGlue to implement feature point extraction and matching. On the basis of accurate image registration, image pre-alignment is achieved using a homography matrix. We construct an energy function using color and structural features and then use dynamic programming to search for the optimal seam in the overlapping area that minimizes the energy function. Finally, we achieve stitching based on the optimal seam. In terms of evaluation metrics, in addition to peak signal-to-noise ratio and structural similarity index measure, we introduce an evaluation metric based on pixel patches for seams, making the quantitative evaluation more objective. © 2025 SPIE and IS&T.

关键词： image stitching optimal seam remote sensing unmanned aerial vehicle image

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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