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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yang, Ran Deng, Chunhua School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (纸本)9798350337020

It is difficult to train a trustworthy transformer model on a small image classification dataset. This research proposes a sophisticated structured knowledge distillation algorithm that uses CNNs as Transformer's sophisticated teachers, significantly lowering the number of training datasets needed. To better to develop the potential for CNN tutors, this research configures a public data set for CNN teaching as an enlightenment textbook to guide Transformer's training and avoid falling into local optimization prematurely. The distillation process then employs a 'learn-digest-self-distillation' learning strategy to enable the Transformer to assimilate CNN knowledge in a structured manner. Sufficient experiments show that the proposed method is significantly better than the direct training Transformer under the condition of limited data sets. Moreover, in order to show the practical application value, this research contributed a practical data set for the classification of smoking and calling. The corresponding code and dataset will be released at https://***/wustdch/surpass-teacher if this paper is accepted. © 2023 IEEE.

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AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR SHIP DETECTION IN SAR IMAGES

AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR S...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Fan Shi, Hao Zhao, Liangbo Mao, Yongfei Pan, Hongxin Chen, Liang Radar Research Lab Beijing Institute of Technology Beijing China General Department of Remote Sensing Satellites China Academy of Space Technology China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Since entering the era of deep learning, the single-stage detection algorithm represented by YOLO has achieved some progress in the detection of ship targets by synthetic aperture radar (SAR). However, the accuracy of single-stage detection is lower than that of two-stage detection, especially for small target detection. To this end, this paper proposes an improved YOLOv5 detection method based on convolutional bolck attention module CBAM) and WPAN. At the same time, the focal efficient intersection over union (Focal EIoU) is introduced to optimize the calculation of bounding box regression loss. The experiment is verified on SAR ship detection dataset(SSDD). The results show that the proposed improved YOLOv5 algorithm can improve the false alarm and missing detection problems in multi-scale target detection, and improve the detection accuracy consequently. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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Wind farm combination forecasting model based on dynamic graph attention 8

Wind farm combination forecasting model based on dynamic gra...

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8th International Conference on Electronic technology and information Science, ICETIS 2023

作者： Liao, Xuechao Cheng, Yiqun School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Hubei Wuhan430065 China

ISBN: (纸本)9781510666535

In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance. © 2023 SPIE.

关键词： Convolution

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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DESIGN AND IMPLEMENTATION OF A CONFIGURABLE PARALLEL FFT PROCESSOR IN ONBOARD SAR IMAGING SYSTEM BASED ON FPGA

DESIGN AND IMPLEMENTATION OF A CONFIGURABLE PARALLEL FFT PRO...

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IET International Radar Conference 2023, IRC 2023

作者： Xu, Ming Zhang, Jiawei Li, Yongrui Yin, Yifei Zhang, Ao Dong, Heng Chen, Liang Shi, Hao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai201109 China Sichuan TianFu New Area Beijing Institute of Technology Innovation Equipment Research Institute Chengdu610299 China

ISBN: (纸本)9781839539954

Spaceborne Synthetic Aperture Radar (SAR) plays an important role in the field of emergency disaster reduction. In order to meet the stringent timeliness requirements in onboard SAR data processing, this paper proposes a configurable four-channel parallel FFT processor, which serves as the core of SAR imaging processing. This processor is capable of supporting up to 128K-point FFT operations. Additionally, it supports radix-2 time decimation FFT concatenation processing, radix-4 time decimation FFT concatenation processing, radix-2 frequency decimation FFT concatenation processing, and radix-4 frequency decimation FFT concatenation processing, all of which can be flexibly configured based on data structure. This processor is implemented on the Xilinx Virtex-7 XC7VX690T FPGA and can achieve a maximum FFT processing capability of 128K points at a frequency of 300MHz. The evaluation and analysis results demonstrate that the processor reaches a higher processing rate, effectively meeting the demanding requirements for efficient spaceborne processing. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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Multi-Temporal Images Generation for Building Change Detection Performance Promotion

Multi-Temporal Images Generation for Building Change Detecti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yute Li Wei Li Nan Wang Chenzhong Gao Yin Zhuang He Chen Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The changes in building are important basis for urban monitoring. However, due to the rarity and sparsity of the occurrence of changes in buildings, collecting effective bitemporal image pairs is challenging, as it requires long-term observation over several months or even years. Additionally, annotating large-scale change detection datasets is time-consuming and labor-intensive. Consequently, data scarcity issues lead to insufficient training of building change detection models. To address this, we propose a data generation method Building Generation GAN (BG-GAN). Different from other GANs, the BG-GAN is trained based on adversarial consistency loss, enabling the model to generate new bi-temporal image pairs with various types of building changes. To verify the effectiveness of the proposed methods on change detection task, BG-GAN is utilized to perform building change samples generation on two building change detection datasets (LEVIR-CD and WHU-CD). The experimental results demonstrate that the proposed method can improve the robustness and generalization of change detection model to detect pseudo changes.

关键词： Training Accuracy Image synthesis Buildings Geoscience and remote sensing Generative adversarial networks Data models

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder 2

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method. © 2024 IEEE.

关键词： Wavelet decomposition

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Detecting DTI Using Graph Embedding and Multi-Head Attention Mechanism

Detecting DTI Using Graph Embedding and Multi-Head Attention...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Chen, Yaoyu Yang, Wenli Yu, HaiPing Wu, Zimo 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 China Graphics Society School of Information Wuhan Vocational College Professional Committee of Graphics and Big Data Wuhan China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction has an important role in drug discovery, significantly expediting the drug design and development process. This paper proposes a new model, ERW-BiAN, that seamlessly integrates graph embedding representations with deep learning network. Specifically, we utilize an improved graph embedding model, ERW, to generate comprehensive feature vectors for each node within the knowledge graph. Subsequently, these feature vectors are fed into the BiAN model, which leverages an attention mechanism to assign precise weights to sequences. Experimental results demonstrate the superior accuracy and predictive prowess of ERW-BiAN in DTI prediction tasks. Furthermore, the case about COVID-19 shows that ERW-BiAN has the better application potential for predicting drug-target interactions. © 2024 IEEE.

关键词： Knowledge graph

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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Tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline 2

Tomographic imaging method of GB-ArcSAR with arc-shaped inte...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Zhou, Hanpu Yan, Kai Wang, Jian Li, Xiaoxiao Tian, Weiming Deng, Yunkai Bejing Institute of Technology Radar Technology Research Institute Beijing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Building Research Institute Corporation Limited Cscec Beijing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9798331515669

When observing building scenarios, there exists the layover problem. Synthetic aperture radar (SAR) tomography can use multiple two-dimensional (2D) SAR images acquired from different heights to inverse the range-azimuth-height 3D information of the targets. Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can obtain a series of SAR images at different heights by performing azimuthal scanning on horizontal planes with different pitch angles, which makes it has tomography capabilities. However, the baseline of GB-ArcSAR is arc-shaped, which makes the existing tomographic imaging methods suitable for linear tomographic baselines cannot be directly applied to GB-ArcSAR. This paper proposes a tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline. Through Taylor expansion of the distance between the target and the sampling position, a mapping relationship between the target pitch angle and interferometric phase was established. Based on the mapping relationship, the measurement matrix was constructed. Validation of the proposed method is achieved through simulations and measured data. © 2024 IEEE.

关键词： Radar imaging

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