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Few-Shot Object Detection in SAR images via Gaussian Flow Class-Balanced Differential Aggregation

Few-Shot Object Detection in SAR images via Gaussian Flow Cl...

引用

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

作者： Po Zhao Jie Chen Chaochen Zhang Ke Ma Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Anhui University Hefei China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in SAR images to significant changes in orientation, make existing models sensitive to variations in novel class examples. This sensitivity leads to challenges in extracting class prototypes and accurately estimating class centers for novel classes. To solve the above issues, we propose a few-shot object detection algorithm for SAR images via Gaussian Flow Class-Balanced Differential Aggregation. Specifically, we first design a class-balanced difference feature aggregation module to solve the problems of difficult class distribution estimation and inaccurate class center estimation of novel classes in SAR images. Second, the contextual information perception module is designed to extract the most representative support features into fine-grained prototypes and aggregate them with the query features using the matching paradigm to solve the imbalance between foreground targets and background in SAR images. Finally, experimental results on MSAR-AIR dataset indicate that our model realizes the best detection properties on SAR images compared to existing models.

关键词： Representation learning Sensitivity Prototypes Estimation Object detection Interference Feature extraction Radar polarimetry Data models Synthetic aperture radar

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Optical performance monitoring of mode division multiplexing signal based on multi-task convolutional neural network 7

Optical performance monitoring of mode division multiplexing...

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7th Optics Young Scientist Summit, OYSS 2024

作者： He, Ting Chang, Huan Gao, Ran Wang, Fei Guo, Dong Beijing Institute of Technology No.5 Zhongguancun South Street Haidian District Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

Mode division multiplexing (MDM) optical fiber communication technology enhances bandwidth utilization and communication capacity to address the increasing demand for high-capacity data transmission in the era of rapid network traffic growth. For MDM optical fiber communication systems affected by various impairments, optical performance monitoring (OPM) can effectively characterize the impairments in received optical signals, directly reflecting the transmission signal state and channel conditions, and help ensure efficient and stable network operation. In this paper, we propose a multi-task convolutional neural network (MT-CNN) aided OPM method for MDM signals. The proposed MTCNN combines convolutional neural networks and a multi-task learning framework to extract the transmission link information and predict the optical signal-to-noise ratio (OSNR) of few-mode channels synchronously. Simulation results show that the proposed MT-CNN-based OPM scheme can achieve accurate OSNR performance evaluation for MDM fiber transmission signals. © 2025 SPIE.

关键词： Convolutional neural networks

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Three-Dimensional Fixed-Time Cooperative Guidance law with Impact time Constraints

Three-Dimensional Fixed-Time Cooperative Guidance law with I...

引用

2023 China Automation Congress, CAC 2023

作者： Li, Yajun Liu, Lei Fan, Huijin Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology Wuhan430074 China

ISBN: (纸本)9798350303759

In this article, we propose fixed-time cooperative guidance laws for achieving simultaneous attacks on a maneuvering target by multiple missiles at desired terminal angles. The 3-D guidance commands encompass radial acceleration along the line-of-sight (LOS) direction to enable coordinated attacks, as well as normal acceleration perpendicular to the LOS direction to ensure interception with terminal angle constraints. Firstly, the radial acceleration is designed from a nonlinear fixed-time cooperative protocol to achieve the fixed-time consensus of impact times for multiple missiles. Secondly, based on the fixed-time sliding manifold and fixed-time reaching law, the normal acceleration is designed for each missile to converge the LOS angle to the desired value within a fixed time. Furthermore, the unknown target acceleration is treated as a system disturbance, estimated by fixed-time observers, and compensated in the guidance commands, ensuring effective handling of uncertainties introduced by the target's acceleration. Finally, simulations are conducted to demonstrate the performance of the guidance law in the 3-D guidance scenario. © 2023 IEEE.

关键词： Acceleration

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Robust Ascent Trajectory Optimization for Hypersonic Vehicles Based on IGS-UMPSP

Robust Ascent Trajectory Optimization for Hypersonic Vehicl...

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Chinese intelligent Automation Conference, CIAC 2023

作者： Qi, Yuting Wang, Bo Liu, Lei Fan, Huijin Wang, Yongji National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819961863

This paper proposes a new approach for solving trajectory optimization problems with uncertain system parameters, which is the generalized quasi-spectral version of unscented model predictive static programming. The proposed method combines two existing techniques, the generalized quasi-spectral model predictive static programming and the unscented optimal control, to generate control solutions that can meet multiple constraints, while minimizing the covariance of the terminal output. The technique has shown good robustness in simulations and has been demonstrated to be effective in solving ascent trajectory optimization problems of hypersonic vehicles, providing a reference trajectory for guidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Hypersonic vehicles

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A Deep Knowledge Tracing Model Based on Multi-Head Self-Attention Mechanism

A Deep Knowledge Tracing Model Based on Multi-Head Self-Atte...

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2023 International Conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhou, Ya Wu, Fengzhen Huang, Guimin Lin, Jianxing Deng, Nanxiao Guo, Qingkai School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510671881

How to provide individualized instruction and personalized teaching for students and effectively track their knowledge status in a targeted manner is a key concern in the field of education. The target of knowledge tracing (KT) is to use the learner's behavior in historical learning activities to model the students' educational process and forcast the future performance of students. Recently, self-attention has been used several times in the KT field. Traditional models have the problem of losing information under long sequence data. Inspired by Transformer, In this paper, we present a deep knowledge tracing model (MA-DKT) based on the multi-head self-attention mechanism, which employs the LSTM model combined with the multi-head self-attention mechanism to implement a deep knowledge tracing model that is able to catch the data characteristics of the students at different temporal scales. Our model works well on publicly available datasets and achieves some improvement in AUC. © 2023 SPIE. All rights reserved.

关键词： Students

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Orthogonal Universal Kernel Non-Negative Matrix Factorization with Total Variation Regularization

Orthogonal Universal Kernel Non-Negative Matrix Factorizatio...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Zhou, Rong Chen, Wen-Sheng Pan, Binbin College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

Kernel-based non-negative matrix factorization (KNMF) is a promising data analysis method for nonlinear feature extraction. It has achieved better performance in pattern classification. However, KNMF algorithms suffer from problems including kernel function restriction, inaccurate pre-image learning, and sensitivity to noise. This paper proposes a novel framework of orthogonal universal KNMF with total variation regularization (OUKNMF-TV) to address these issues. The model contains three objective functions subject to TV and orthogonal constraints. The optimization problems of the OUKNMF-TV model are solved using the gradient descent method, and its updated rules are the same in the case of using different kernel functions. Convergence of the OUKNMF-TV algorithm is guaranteed using auxiliary function techniques. The experiments on non-noisy and contaminated datasets are implemented, and the results confirm the effectiveness and robustness of the proposed algorithm. © 2023 IEEE.

关键词： Face recognition

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Remote Sensing Few-shot Object Detection Using Adaptive Feature Modification and Gaussian Dynamic Dilated Convolution 16

Remote Sensing Few-shot Object Detection Using Adaptive Feat...

引用

2024 16th International Conference on Graphics and Image processing, ICGIP 2024

作者： Zhiwei, Zhang Jiangtao, Wang Penglei, Chen Cheng, He School of Physics and Electronic Information Huaibei Normal University Anhui Province Huaibei City China Anhui Province Key Laboratory of Intelligent Computing and Applications Anhui Province Huaibei City China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Object detection in remote sensing images relies on a large amount of annotated data for training, but adequately annotating novel classes is difficult. Few-shot object detection(FSOD) address this problem by fine-tuning on finitely annotated novel classes, this results in its within-class variation being limited, making it difficult to adequately fit the within-class distribution. In order to alleviate this problem, this paper proposed a few-shot object detection method in remote sensing based on adaptive feature modification and guided by Gaussian dynamic dilated convolution. Firstly, in view of the difficulty of novel classes recognition, an adaptive feature modification algorithm is proposed. By using the characteristics of the two-branch network architecture of meta-learning itself, the support features are integrated into the query feature extraction network as convolutional bias to adaptively modify the query features. Secondly, in order to simulate intra-class variation of noise (feature perturbation caused by noise in the foreground), the backbone network weights were reparameterized to make them fit Gaussian distribution variables during fine-tuning, and the feature perturbation was achieved with parameter variation. Finally, in order to simulate intra-class scale variation (different foreground sizes), the convolution is changed into a dynamic dilation convolution, and different dilation rates are used to replace feature scale variation. Extensive experiments on two widely used remote sensing datasets (NWPU.V2, dior) demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Gaussian distribution

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Enhanced BERT with Graph and Topic information for Short Text Classification 35

Enhanced BERT with Graph and Topic Information for Short Tex...

引用

35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Tong Tang, Ailing Yan, Rong College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

Short text classification is an important natural language processing task due to the prevalence of short text on the internet and social media platforms. In this paper, we propose a novel graph-based short text classification method named GBBM (Graph-BERT-BTM Model) that leverages the powerful representation ability of graph data to capture the structural features of short text. In this work, we incorporate topic information to enrich and expand the feature space for the short text and compare our proposed method on five publicly available short text datasets with five existing models. Experimental results indicate the superiority of our proposed method. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graphic methods

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Improved Kalman Filtering Algorithm Based Multi-Sensor Integration for Silicon Single Crystal Growth Process 7

Improved Kalman Filtering Algorithm Based Multi-Sensor Integ...

引用

7th International Symposium on Autonomous Systems, ISAS 2024

作者： Xie, Ruidong Zhu, Pei Ma, Wenle Zhang, Zheqi Yi, Yingmin Guo, Zheng Xi'an University of Technology Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an710048 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9798350363173

The study primarily delves into addressing the data filtering challenge inherent in the silicon single crystal growth process by proposing a multi-sensor integrated detection approach. This approach is designed to alleviate the diminished filtering accuracy resulting from the reliance on a single sensor and the conventional Kalman filtering algorithm. By utilizing an enhanced adaptive Kalman filtering algorithm and the maximum likelihood estimation criterion to incorporate the innovation variance directly into the gain calculation of the Kalman filter, dynamic adjustment of the estimation model is achieved while interference from system noise and measurement noise is reduced. Finally, simulation-based comparative experiments between the adaptive and conventional Kalman filtering algorithms are performed. The results show that the performance of the adaptive Kalman filtering algorithm is superior to that of the conventional Kalman filtering algorithm in terms of accuracy and stability when detecting signals from multiple sensors. The research findings furnish dependable data support for refining the decision-making process in crystal growth technology, augmenting the precision and scientific integrity of crystal safety production scheduling and decision-making endeavors. © 2024 IEEE.

关键词： Kalman filters

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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