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A novel subsidence monitoring technique based on space-surface bistatic differential interferometry using GNSS as transmitters

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Science China(information Sciences) 2015年第6期58卷 68-83页

作者： ZENG Tao ZHANG Tian TIAN WeiMing HU Cheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Laboratory School of Information and ElectronicsBeijing Institute of Technology

Synthetic Aperture Radar Differential Interferometry(DIn SAR) technique is an effective tool with large coverage and high spatial accuracy for subsidence monitoring. Nevertheless, the temporal resolution is usually poor so that rapid deformation cannot be measured due to the long revisit time of radar *** SAR Differential Interferometry technique using Global Navigation Satellite System(GNSS) as illuminator has a shorter revisit time, whereas the measurement accuracy is constrained by low signal power, narrow bandwidth and atmospheric delay error. To cope with these problems, in this paper, we propose a novel subsidence monitoring technique based on Space-Surface bistatic Differential Interferometry(SS-DI) with GNSS transmitters, where two stations consist of a reference one and a measurement one that are deployed on the ground. First, we applied a space differential processing between two stations to cancel identical errors such as tropospheric and ionospheric errors etc. Then we used a long time coherent integration to improve the signal noise ratio. Subsequently, we also utilized a time differential processing to construct double differential equations with respect to unknown deformation variables. Finally, we solved the equations to obtain a highly accurate estimation of three dimension deformation. Furthermore, an SS-DI experiment using Beidou-2 as transmitters was carried out to validate the proposed method, where a high accuracy(0.01 mm) device was utilized to simulate subsidence deformation. The experimental results reveal that the proposed method has better performance of spatial measurement accuracy of 0.53 mm compared with differential GPS method.

关键词： differential interferometry subsidence monitoring Beidou-2/Compass-2 space-surface bistatic radar differential GPS

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Impacts of ionospheric scintillation on geosynchronous SAR focusing: preliminary experiments and analysis

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Science China(information Sciences) 2015年第10期58卷 191-193页

作者： LI YuanHao HU Cheng DONG XiChao TIAN WeiMing LONG Teng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology

Dear editor,Geosynchronous Synthetic Aperture Radar(GEO SAR)has significant potential when applied to hazard and disaster monitoring because of its short revisit time of approximately 24 h and its wide coverage of app... 详细信息

关键词： SAR Impacts of ionospheric scintillation on geosynchronous SAR focusing GEO PSLR Figure preliminary experiments and analysis than

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Feasibility study of inclined geosynchronous SAR focusing using Beidou IGSO signals

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Science China Earth Sciences 2016年第12期59卷 240-242页

作者： Xichao DONG Cheng HU Weiming TIAN Tian ZHANG Yuanhao LI Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology

Dear editor,Geosynchronous synthetic aperture radar(GEO SAR)[1,2]has an inclined geosynchronous orbit of around 36000 km,which leads to its short revisit time of around 24 hours and a wide coverage of up to approximately one third of the Earth *** long integration time guarantees the fine ***,GEO SAR owns the promising capability of providing images with the high resolution,

关键词：倾斜地球同步轨道 SAR 北斗聚焦信号

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CMRM: Collaborative Multi-agent Reinforcement Learning for Multi-objective Traffic Signal Control

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IEEE Transactions on Consumer Electronics 2025年

作者： Nie, Lei Qi, Dandan Liu, Bingyi Li, Peng Bao, Haizhou He, Heng Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks. © 1975-2011 IEEE.

关键词： Traffic congestion

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Multi-scale object detection by top-down and bottom-up feature pyramid network

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Journal of Systems Engineering and Electronics 2019年第1期30卷 1-12页

作者： ZHAO Baojun ZHAO Boya TANG Linbo WANG Wenzheng WU Chen School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology

While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.

关键词： convolutional neural network (CNN) feature pyramid network (FPN) object detection deconvolution.

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An Anti-background Interference Object Detection Method for Complex Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Tang, Wei Zhao, Xidong Zhang, Qingjun Wang, Wenzheng Zhao, Baojun China Academy of Space Technology Shandong Institute of Space Electronic Technology Yantai 264003 China Changfeng Electromechanical Technology Research and Design Institute Beijing 100086 China Remote Sensing General Department of China Academy of Space Technology Beijing 100086 China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology School of Information and Electronics Beijing 100081 China

Object detection in remote sensing images differs from general object detection in that it faces numerous challenges such as false alarm interference. However, conventional object detection methods primarily focus on scale and orientation variations, insufficiently addressing issues such as object blurring and false alarms, which frequently occur in large-scale Earth observation scenarios. To address this issue, this paper proposes an anti-interference object-detection algorithm based on mask guidance and adaptive margin loss, termed MG2ADet. Specifically, we devise a mask guidance module that leverages pseudo-mask labels to guide network features towards focusing on foreground regions, thereby enhancing the discriminability of object features and suppressing background noise interference. Besides, to further differentiate object and background features, we designed an adaptive margin classification loss function that guides the detection model in learning strongly discriminative features. This classification loss adaptively adjusts the angular margin penalty based on the image quality score of samples, assigning varying weights to different difficult samples, and ultimately improving the detection model's ability to discriminate between blurred objects and similar distractors. Extensive experiments on public remote sensing datasets demonstrated the excellent detection performance of our algorithm in comparison with numerous existing detectors (both one-stage and two-stage detectors). © 2008-2012 IEEE.

关键词： Aerial image Complex Scenes Deep learning Object Detection

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AIDetectVul: Software Vulnerability Detection Method Based on Feature Fusion of Pre-trained Models

AIDetectVul: Software Vulnerability Detection Method Based o...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Shiying Xue Lin Li Tao Li Haodong Chen Jiapan Li Yangqing Qin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real time Industrial Systems Wuhan University of Science and Technology Wuhan China Wuhan Jiaoyun Huizhi Information Technology Co. Ltd. Wuhan China Guotou Securities Co. Ltd. Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Data-driven deep learning models are constrained by the scale and diversity of training data, making them vulnerable to data bias. While large language models (LLMs) exhibit superior generalization in vulnerability detection, their low inference efficiency and high computational costs hinder practical deployment in industrial settings. To address these limitations, we propose AIDetectVul, a novel vulnerability detection framework leveraging feature fusion from pre-trained models. Our approach concurrently utilizes encoder-only and decoder-only architectures to extract complementary code embeddings, with feature fusion enhancing semantic diversity. These enriched representations are then processed by a Transformer model, where the self-attention mechanism effectively captures long-range code dependencies, ultimately improving both detection accuracy and generalization capability. Comprehensive evaluations on proprietary enterprise datasets and open-source benchmarks demonstrate that AIDetectVul achieves comparable detection accuracy to the state-of-the-art LineVul model while demonstrating measurable improvements in generalization performance. Compared to LLM-based approaches, our solution maintains significantly lower computational overhead and training costs, making it particularly suitable for industrial applications.

关键词： Training Accuracy Codes Computational modeling Large language models Training data Computer architecture Feature extraction Transformers Vectors

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Mdct-Unet: A Dual-Encoder Network Combining Multi-Scale Dilated Convolutions with Transformer for Medical Image Segmentation

SSRN

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SSRN 2025年

作者： Chen, Zhengshi He, Juanjuan Liu, Xiaoming Tang, Jinshan College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Health Informatics Department of Health Administration Policy George Mason University FairfaxVA22030 United States

Precise medical image segmentation is crucial in clinical diagnosis and pathological analysis. Most segmentation methods are based on U-shaped convolutional neural networks (U-Net). Although U-Net performs well in medical image segmentation, as a method based on CNN, its main drawback lies in the difficulty of establishing long-range pixels dependencies and has a constrained receptive field, which restricts segmentation accuracy. Many models address this issue by incorporating Transformer models into U-Net architectures to better capture long-range dependencies. However, these methods often suffer from simple feature fusion techniques and limited receptive fields for local features. To address these challenges, we propose a dual-encoder framework, named MDCT-Unet, which combines Swin-Transformer and CNN for enhanced medical image segmentation. This framework introduces a novel dynamic feature fusion module to better integrate of local and global features. By combining channel and spatial attention mechanisms and inducing competition between them, we enhance the coupling of these two types of features, ensuring richer information representation. Moreover, we supplement the fused features into the decoder via skip connections to recover details lost during encoding. In addition, to better extract multi-scale local features from images, we design a dilated convolution encoder (DCE) as the CNN branch of our model. We conducted extensive experiments on four datasets: Synapse, ISIC2018, CHASEDB1, and MMWHS. The experimental results show that our method outperforms most current medical image segmentation methods © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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UCS: A Universal Model for Curvilinear Structure Segmentation

arXiv

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arXiv 2025年

作者： Li, Dianshuo Chen, Li Cao, Yunxiang Zhu, Kai Cheng, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China 1 Fusionopolis Way #21-01 Connexis South Tower Singapore138632 Singapore

Curvilinear structure segmentation (CSS) is vital in various domains, including medical imaging, landscape analysis, industrial surface inspection, and plant analysis. While existing methods achieve high performance within specific domains, their generalizability is limited. On the other hand, large-scale models such as Segment Anything Model (SAM) exhibit strong generalization but are not optimized for curvilinear structures. Existing adaptations of SAM primarily focus on general object segmentation and lack specialized design for CSS tasks. To bridge this gap, we propose the Universal Curvilinear structure Segmentation (UCS) model, which adapts SAM to CSS tasks while enhancing its generalization. UCS features a novel encoder architecture integrating a pretrained SAM encoder with two innovations: a Sparse Adapter, strategically inserted to inherit the pre-trained SAM encoder’s generalization capability while minimizing the number of fine-tuning parameters, and a Prompt Generation module, which leverages Fast Fourier Transform with a high-pass filter to generate curve-specific prompts. Furthermore, the UCS incorporates a mask decoder that eliminates reliance on manual interaction through a dual-compression module: a Hierarchical Feature Compression module, which aggregates the outputs of the sampled encoder to enhance detail preservation, and a Guidance Feature Compression module, which extracts and compresses image-driven guidance features. Evaluated on a comprehensive multi-domain dataset, including an in-house dataset covering eight natural curvilinear structures, UCS demonstrates state-of-the-art generalization and open-set segmentation performance across medical, engineering, natural, and plant imagery, establishing a new benchmark for universal CSS. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

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IEEE Internet of Things Journal 2025年

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

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