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A multipath routing algorithm for satellite networksbased on service demand and traffic awareness

Frontiers of information Technology & Electronic Engineering 2023年第6期24卷 844-858页

作者： Ziyang XING Hui QI Xiaoqiang DI Jinyao LIU Rui XU Jing CHEN Ligang CONG Jilin Key Laboratory of Network and Information Security Changchun 130022China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Information Center Changchun University of Science and TechnologyChangchun 130022China

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in air-ground ***,due to the factors such as geographical location and people’s living habits,the differences in user’demand for multimedia data will result in unbalanced network traffic,which may lead to network congestion and affect data *** addition,in traditional satellite network transmission,the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner,which is not conducive to calculating optimal *** service quality requirements cannot be satisfied when multiple service requests are *** on the above,in this paper artificial intelligence technology is applied to the satellite network,and a software-defined network is used to obtain the global network information,perceive network traffic,develop comprehensive decisions online through reinforcement learning,and update the optimal routing strategy in real *** results show that the proposed reinforcement learning algorithm has good convergence performance and strong *** with traditional routing,the throughput is 8%higher,and the proposed method has load balancing characteristics.

关键词： Software-defined network(SDN) Quick user datagram protocol Internet connection(QUIC) Reinforcement learning Sketch Multi-service demand Satellite network

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GAN-based Symmetric Embedding Costs Adjustment for Enhancing Image Steganographic security 24

GAN-based Symmetric Embedding Costs Adjustment for Enhancing...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ye, Miaoxin Zhou, Saixing Luo, Weiqi Tan, Shunquan Huang, Jiwu Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou China Shenzhen Key Laboratory of Media Security College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798400706868

Designing embedding costs is pivotal in modern image steganography. Many studies have shown adjusting symmetric embedding costs to asymmetric ones can enhance steganographic security. However, most existing methods heavily depend on manually defined parameters or rules, limiting security performance improvements. To overcome this limitation, we introduce an advanced GAN-based framework that transitions symmetric costs to asymmetric ones without the need for the manual intervention seen in existing approaches, such as the detailed specification of cost modulation directions and magnitudes. In our framework, we firstly achieve symmetric costs for a cover image, which is randomly split into two sub-images, with part of the secret information embedded into one. Subsequently, we design a GAN model to adjust the embedding costs of the second sub-image to asymmetric, facilitating the secure embedding of the remaining secret information. To support our phased embedding approach, our GAN's discriminator incorporates two steganalyers with different tasks: distinguishing the generator's final output, i.e., the stego image, from both the input cover image and the partially embedded stego image, providing diverse guidance to the generator. In addition, we introduce a simple yet effective update strategy to ensure a stable training process. Comprehensive experiments demonstrate that our method significantly enhances security over existing symmetric steganography techniques, achieving state-of-the-art levels compared to other methods focused on embedding costs adjustments. Additionally, detailed ablation studies validate our approach's effectiveness. © 2024 ACM.

关键词： Steganography

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A Simple Semi-Supervised Joint Learning Framework for Few-shot Text Classification 5

A Simple Semi-Supervised Joint Learning Framework for Few-sh...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Lu, Shaoshuai Chen, Long Wang, Wenjing Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

The lack of labeled data is the bottleneck restricting deep text classification algorithm. State-of-the-art for most existing deep text classification methods follow the two-step transfer learning paradigm: pre-training a large model on an auxiliary task, and then fine-tuning the model on a labeled data. Their shortcoming is the high cost of training. To reduce training costs as well as alleviate the need for labeled data, we present a novel simple Semi-Supervised Joint Learning (SSJL) framework for few-shot text classification that captures the rich text semantics from large user-tagged data (referred to as weakly-labeled data) with noisy labels while also learning correct category distributions in small labeled data. We refine the contrastive loss function to better exploit inter-class contrastive patterns, making contrastive learning more applicable to the weakly-labeled setting. Besides, an appropriate temperature hyper-parameter can improve model robustness under label noise. The experimental results on four real-world datasets show that our approach outperforms the other baseline methods. Moreover, SSJL significantly boosts the deep models' performance with only 0.5% (i.e. 32 samples) of the labeled data, showing its robustness in the data sparsity scenario. © 2022 ACM.

关键词： Semantics

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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HRNet-U: Combining the Strengths of U-Net and HRNet for Efficient Pose Estimation 2

HRNet-U: Combining the Strengths of U-Net and HRNet for Effi...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Guo, Biao Guo, Fangmin He, Qian Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350390254

High-resolution network (HRNet) shows excellent performance in 2D multi-person pose estimation tasks. It can maintain high-resolution representation in the network and exchange information between multi-scale features, significantly improving the ability of key point prediction. However, this multiscale fusion strategy leads to computational redundancy and increases processing delays, limiting its application to resource-constrained edge devices. To solve this problem, this paper proposes a new lightweight network structure (HRNet-U), which combines the high resolution parallel advantage of HRNet and the computational efficiency of U-Net, and adopts the U-Net structure for multiscale feature fusion at each stage. Through this design, HRNet-U still maintains high resolution branch and multi-scale information, which is more suitable for the application scenarios of edge devices. This model achieves comparable or even better performance on the COCO and MPII validation sets. © 2024 IEEE.

关键词： Feature extraction

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DMCPose: A Binocular 3D Pose Estimation network Integrating Disparity and Multi-View Consistency via Attention

DMCPose: A Binocular 3D Pose Estimation Network Integrating ...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Zhang, Jialun Guo, Biao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China

ISBN: (纸本)9798331509873

In 3D human pose estimation, binocular vision typically relies on stereo matching to obtain depth information and calculates 3D keypoints using the disparity principle. However, the high computational cost of stereo matching limits its application in real-time scenarios. Alternatively, multi-view consistency can estimate 3D keypoints across multiple views, but with only two views, the constraints are insufficient, often resulting in suboptimal keypoint accuracy. To address these challenges, this paper proposes a binocular 3D pose estimation network based on attention mechanisms, which effectively integrates the advantages of multi-view consistency and disparity methods. Unlike traditional binocular stereo vision systems, our approach leverages a high-precision 2D pose estimation network and dynamically fuses multi-source information via attention mechanisms, thus avoiding the complexity of stereo matching and significantly reducing computational overhead. Additionally, by utilizing multi-view disparity information, we further optimize the estimation of 3D keypoints, improving the accuracy of pose reconstruction. Experiments on the MADS dataset show that DMCPose achieves an average 3D MPJPE of 102.4 mm, outperforming several existing methods, validating its advantages in both accuracy and efficiency. © 2024 IEEE.

关键词： Binoculars

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Research and Implementation of Electric Power Line Loss Analysis Technology Based on Artificial Intelligence Algorithm 12

Research and Implementation of Electric Power Line Loss Anal...

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12th International Conference of information and Communication Technology, ICTech 2023

作者： Qiao, Junfeng Zhou, Aihua Peng, Lin Zhu, Lipeng Pan, Sen Yang, Pei Security of State Grid Smart Grid Research Institute Co. Ltd State Grid Key Laboratory of Information & Network Jiangsu Nanjing210003 China

ISBN: (纸本)9798350332919

Electric power supply line loss is a very important economic indicator of the State Grid Corporation of China, which is a comprehensive reflection of many important indicators. For companies of electric supply, the loss of supply of electric power lines is not only related to the operating income and economic benefits of enterprises of electric power supply, but also related to the cost and welfare of people's livelihood. Up to now, the State Grid Corporation of China (SGCC) is responding to the policy of energy conservation and emission reduction proposed in the 13th Five Year Plan, and has begun to carry out refined management of power supply line loss, so as to promote the reduction of power consumption, save energy consumption, and achieve stable and sustainable economic and social development and operation. In the past, the management of power supply line loss mainly relied on manual calculation of the loss. This method is inefficient, error prone and has great side effects. At the same time, the complexity of line loss calculation for different voltage levels of distribution network is different, which will inevitably lead to error of line loss data due to negligence and affect the accuracy. In view of the above situation, this article aims to find out all the causes of power supply line loss, and then sort out the correlation between these factors to accurately locate the line loss, in order to improve the refined ability of management on power enterprises and enhance enterprise benefits. © 2023 IEEE.

关键词： Energy utilization

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Multi-dimensional ability diagnosis for machine learning algorithms

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Science China(information Sciences) 2024年第12期67卷 321-322页

作者： Qi LIU Zheng GONG Zhenya HUANG Chuanren LIU Hengshu ZHU Zhi LI Enhong CHEN Hui XIONG State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Business Analytics and Statistics The University of Tennessee Computer Network Information Center Chinese Academy of Sciences Shenzhen International Graduate School Tsinghua University Hong Kong University of Science and Technology (Guangzhou)

A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired by highly organized human education systems, i.e., training the algorithms with easy samples first and gradually transforming to the hard examples can contribute to faster convergence and lower generalization error.

关键词： Adversarial machine learning

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Random mating mayfly algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 40-50页

作者： Xie Xiaode Zheng Jiali Lin Zihan He Siyi Feng Minyu School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was ***,RMMA introduced the mechanism of random mating into the mayfly algorithm(MA),which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage,and find a better solution to the RFID nework planning(RNP)***,in RNP,tags are usually placed near the boundaries of the working space,so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction,as to enhance the ability of searching near the ***,in order to measure the performance of RMMA,the algorithm is then benchmarked on three well-known classic test functions,and the results are verified by a comparative study with particle swarm optimization(PSO),grey wolf optimization(GWO),and *** results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics,RMMA is also applied to solve RNP *** performance evaluation shows that RMMA achieves higher coverage than the other three *** the number of readers is the same,RMMA can obtain lower interference and get a better load balance in each instance compared with other *** can also solve RNP problem stably and efficiently when the number and position of tags change over time.

关键词： radio frequency identification(RFID) RFID network planning(RNP) reader deployment mayfly algorithm(MA) random mating

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RFID network planning based on improved brain storm optimization algorithm

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 30-39页

作者： Lin Zihan Zheng Jiali Xie Xiaode Feng Minyu He Siyi School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.

关键词： radio frequency identification(RFID) RFID network planning(RNP) brain storm optimization golden sine operator

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