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检索条件"机构=Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology"

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects

Transductive Multi-Prototype Network for HRRP Recognition wi...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Mingchen Yuan Jiaqi Liu Xinyang Wang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Zhengzhou Academy Of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes.

关键词： Manifolds Training Sensitivity Accuracy Target recognition Prototypes Training data Radar Data processing

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Missing Modality Completion for Multi Frequency Radar HRRP Recognition Using GAN

Missing Modality Completion for Multi Frequency Radar HRRP R...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Qiang Zhou Bingqian Yu Yuanhao Meng Xin Zhang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profile (HRRP) is one of the commonly used methods in radar automatic target recognition (RATR). Recently, obtaining HRRP under different modalities, such as frequency and polarization, to improve the RATR performance has become an important development direction. However, in practical applications, sensor failures or interference may lead to modality loss during the model testing phase, resulting in decreased performance or even causing the model to fail to function properly. This paper primarily addresses the scenario of frequency modality loss and specifically proposes a modality completion and recognition network with generative adversarial network-embedded (MCRN-GAN) model. The model first generates and completes the missing modality features using the frequency feature transformation loop (FFTL) based on GAN. Subsequently, it fuses multi-modal features based on spatial and channel attention mechanisms to obtain a joint representation of dual-frequency features. Finally, a fully connected layer is used to complete the classification. Experiments on Ku and W frequency modality datasets demonstrates that the proposed method can effectively handle missing modality data after sufficient training without significant performance loss.

关键词： Training Frequency locked loops Target recognition Fuses Radar Interference Feature extraction Generative adversarial networks Data processing Testing

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Towards DDIs Identification by Knowledge Graph with BiRW and Back Aggregation

Towards DDIs Identification by Knowledge Graph with BiRW and...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Yin, Zhuang Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665468190

Effective identification of potential drug-drug interactions (DDIs) can prevent adverse effects caused by DDIs to a certain extent. This paper proposes the new hybrid method for predicting DDIs, which is called BiRW-KGBAN that combines bidirectional random walk and back aggregation based on knowledge graph. BiRW-KGBAN first constructs two knowledge graph KG-DrugBank and KG-KEGG that integrate data related to drugs. Then, an improved sampling method based on bidirectional random walk (BiRW) is used to sample the neighborhood information of drugs, including directly connected entities, related semantic relations and potential information. In addition, the path with drug as the center node is also extracted. Finally, two improved back aggregation model KGBAN1 and KGBAN2 are used to obtain the final embedded representation of the drugs. The experiments show that, compared with other existing methods, our method could obtain the higher-order topological information and the deeper potential neighborhood information of drugs, and has great improvements in DDIs prediction. The case study also shows that the proposed method has the potential for actual DDIs prediction. © 2022 IEEE.

关键词： Knowledge graph

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Acceleration method for target HRRP generation based on DDPM

Acceleration method for target HRRP generation based on DDPM

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xitai Shen Qiang Zhou Bingqian Yu Xin Zhang Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity.

关键词： Training Target recognition Noise reduction Radar Radar scattering Data collection Feature extraction Sampling methods Diffusion models Vectors

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Multi-Terrain Radar HRRP Target Detection Using Contrastive Learning

Multi-Terrain Radar HRRP Target Detection Using Contrastive ...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xinyang Wang Mingchen Yuan Jiaqi Liu Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As radar resolution improves, high-resolution radars have significantly enhanced target detection performance through the generation of high-resolution range profiles (HRRPs). However, when the range resolution is much smaller than the target, the target’s energy is dispersed across multiple range cells, leading to poor detection performance with traditional energy-based detection algorithms. Furthermore, existing detection algorithms are highly sensitive to clutter characteristics and exhibit poor adaptability to varying scenarios. To address these issues, this paper proposes a Constant False Alarm Rate (CFAR) HRRP detection algorithm based on contrastive learning for multiple terrains. The algorithm improves the contrastive learning loss function to achieve well-clustered representations of clutter samples during training. Test samples are evaluated by calculating the Mahalanobis distance between them and the training samples, and compared against detection thresholds to accomplish target detection. Testing results show that the proposed algorithm achieves superior detection performance across multiple terrains. Furthermore, to reach a 90% detection probability, the proposed method requires a signal-to-clutter ratio (SCR) reduction of 1 to 4 dB compared to the competing methods.

关键词： Training Energy resolution Radar detection Object detection Contrastive learning Radar Detection algorithms Clutter Signal to noise ratio Testing

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Research on low energy task allocation and scheduling algorithm based on imprecise heterogeneous multi-core technology

Research on low energy task allocation and scheduling algori...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Gu, Haonan Wu, Jing Hu, Wei Ma, Tianao Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In heterogeneous embedded systems, we try to find a new non real-time scheduling algorithm, which can not only meet the reliability requirements, but also achieve low energy consumption. 'Resource reservation time deterministic cyclic scheduling (RRTDCS)' algorithm is a kind of scheduling for offline cache pre-allocation. This scheduling algorithm combines time reservation and priority strategy. The algorithm can dynamically adjust according to different task sets. It can also adjust its own scheduling process to adapt to different scheduling environments. The periodic cyclic strategy enables low power consumption in task allocation. In the experimental part, we compared the algorithm proposed in this paper with the current mainstream real-time scheduling scheme. Experimental results show that RRTDCS algorithm has better performance. © 2022 IEEE.

关键词： Scheduling

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An improved method for rockfall detection and tracking based on video stream

An improved method for rockfall detection and tracking based...

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IET International Radar Conference (IRC 2023)

作者： Longyue Wang Songge Wang Xin Xie Yunkai Deng Weiming Tian Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing People's Republic of China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

Rockfall events occur frequently in mountainous areas. To address the problems of missed detection, false detection, and trajectory interruption when using the deep learning-based online multiple object tracking methods to detect rockfalls, this paper proposes a rockfall detection and tracking method based on video streams. In the detection stage, three-frame difference method is utilized to obtain the moving targets from the video streams, and they are combined with the detection results of the rock detector obtained by the offline-trained YOLOX model. In the tracking stage, data association is firstly performed based on the rockfall detection results. For the existing trajectories that are not matched at the current moment, re-matching is performed by combining the moving object detection results to achieve accurate tracking of rockfalls. Simulations and field experiments prove that the detection method proposed in this paper can effectively separate the rockfalls in the video, and the detected rockfalls have high precision. Besides, it significantly improves the accuracy of rockfall tracking, effectively suppressing phenomena such as trajectory interruption during tracking.

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Space-surface Bistatic SAR Tomography: A Promising Approach for Accurate Local 3D Reconstruction

Space-surface Bistatic SAR Tomography: A Promising Approach ...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Zhiyang Li, Yuanhao Chen, Xinyan Hu, Cheng Datcu, Mihai Monti-Guarnieri, Andrea Virgilio Hobbs, Stephen School of Information and Electronics Beijing Institute of Technology Beijing100084 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100084 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China CEOSpaceTech University Politehnica of Bucharest Bucharest050663 Romania The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan20133 Italy School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom

ISBN: (纸本)9781839539954

With a flexibly deployed receiver, space-surface bistatic Synthetic Aperture Radar (SS-BiSAR) can increase the observation sensitivity in the north-south direction, which is a promising method for local high-precision three-dimensional deformation retrieval. However, in urban areas, the layover effect hinders the determination of the dominant scatterer in each resolution cell. Multi-baseline SAR tomography is an effective approach to solving overlaid scatterers. This paper presents the first experiment result of SS-BiSAR tomography. By estimating and removing the interferometric fringes, the repeat-track orbit errors are effectively corrected. The experiment was conducted based on the first L-band spaceborne twin SAR - the Chinese Lutan-1 (LT-1). A total of 11 repeat-track SAR images were acquired, and three-dimensional point clouds of a building area was recovered, with an accuracy of approximately 4 meters. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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Marine Ship Detection Method for SAR Image Based on Improved Faster RCNN

Marine Ship Detection Method for SAR Image Based on Improved...

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Chai, Bingqian Chen, Liang Shi, Hao He, Cheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665401234

Due to the multi-view imaging principle of synthetic aperture radar (SAR), its imaging process is not limited by any time or any bad weather. The detection of marine ship for SAR image is a very important application in both military and private applications. A marine ship detection method in SAR image about an improved Faster RCNN-based approach is proposed. First, the deepest semantic features of SAR image extracted by Faster RCNN contain less target information and small targets may be ignored, so that we fuse the deepest feature and shallower features in the feature extraction network;Secondly, because the redundant information in the features will produce false alarms, we embed the Convolutional Block Attention Module (CBAM) in the feature extraction network to extract more effective features;Finally, we use bilinear interpolation to obtain floating-point coordinates to optimize RoI pooling which uses rounding quantization, so the error mapping caused by quantization will be reduced. The algorithm of this paper is tested on the SSDD public dataset, and the AP is increased from 0.79 to 0.89. The results demonstrate that the algorithm introduced in this paper has a very significant performance improvement for detecting ship-like targets from SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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