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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system

arXiv

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arXiv 2024年

作者： Dong, Li Jiang, Feibo Wang, Minjie Peng, Yubo Li, Xiaolong School of Computer Science Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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REMARKS ON "SCHWARZ-TYPE LEMMA, LANDAU-TYPE THEOREM, AND LIPSCHITZ-TYPE SPACE OF SOLUTIONS TO INHOMOGENEOUS BIHARMONIC EQUATIONS"

arXiv

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arXiv 2025年

作者： Chen, Shaolin Hamada, Hidetaka College of Mathematics And Statistics Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing And Application 421002 China Faculty of Science and Engineering Kyushu Sangyo University 3-1 Matsukadai 2-Chome Higashi-Ku Fukuoka813-8503 Japan

Let φ, ψ ∈ C(T), g ∈ C(ⅅ), where ⅅ and T denote the unit disk and the unit circle, respectively. Suppose that f ∈ C4(D) satisfies the following: (1) the inhomogeneous biharmonic equation ∆(∆f(z)) = g(z) for z ∈ ⅅ, (2) the Dirichlet boundary conditions ∂zf(ζ) = φ(ζ) and f(ζ) = ψ(ζ) for ζ ∈ T. Recently, the authors in [J. Geom. Anal. 29: 2469-2491, 2019] showed that if ω is a majorant with lim supt→0+ (ω(t)/t) ω(T), where φ1(eit) = φ(eit)e−it for t ∈ [0, 2π], then f ∈ Lω(ⅅ). The purpose of this paper is to improve and generalize this result. We not only prove that the condition "lim supt→0+ (ω(t)/t) 1 ∈ Lω(T)" can be replaced by weaker *** Codes 31A30, 35J40 Copyright © 2025, The Authors. All rights reserved.

关键词： Boundary conditions

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Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

arXiv

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arXiv 2025年

作者： Dong, Li Jiang, Feibo Peng, Yubo School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Intelligent Software and Engineering Nanjing University Suzhou215163 China

Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Antennas

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Laser tracking leader-follower automatic cooperative navigation system for UAVs

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

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Image Repair Methods Based on Deep Residual Networks

Image Repair Methods Based on Deep Residual Networks

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作者： Hongwei Deng Ziyu Lin Jinxia Li Ming Yao Taozhi Wang Hongkang Luo Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

In recent years, deep learning has shown significant advantages in image restoration. Compared with the traditional repair method, the image repair method based on deep learning can better solve the problem of image missing blur,but it will also cause the problem of local color difference of the repair *** this paper, an image repair model based on deep residual network is proposed,which is divided into repair module and mitigation module. The repair module uses part of the convolution network to repair the missing area of image blur, the mitigation module uses the deep residual network to adjust the color difference of the repaired image, and the two modules coordinate with each other to make the image repair effect closer to the real image. The experimental results show that the method proposed in this paper has better effect on image repair.

关键词： Depth residual network Image repair Image missing blur Local chromatic aberration of the image

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Design Principle and Method of Lightweight Block Cipher Diffusion Layer

Design Principle and Method of Lightweight Block Cipher Diff...

引用

作者： Junxia Zhao Lang Li Qiuping Li Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

Recently, due to the development and popularization of communication and computing technologies, new technologies have emerged one after *** Internet of Things(Io T) is a state-of-the-art technology that is applicable to all fields. The rapid development of the IoT seriously threatens the security of restricted devices. Lightweight block ciphers, as the main security measures for restricted devices, have attracted the attention of more and more researchers. The diffusion layer is an important part of the lightweight block cipher. Its design not only affects the security of lightweight block ciphers, but also has an important impact on its efficiency in hardware and software implementation. In this paper,we research and analyze the design principle and method of the lightweight block cipher diffusion layer. In addition, combined with the characteristics of lightweight cipher and its application environment, we point out that the diffusion layer of lightweight cipher is worthy of further research in the future.

关键词： Internet of Things Lightweight block cipher Diffusion layer

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Fixed-Parameter Tractability for Book Drawing with Bounded Number of Crossings per Edge 15th

Fixed-Parameter Tractability for Book Drawing with Bounded...

引用

15th International Conference on Algorithmic Aspects in information and Management, AAIM 2021

作者： Liu, Yunlong Li, Yixuan Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Hunan Xiangjiang Artificial Intelligence Academy Changsha China

ISBN: (纸本)9783030931759

The problem book drawing with a bounded number of crossings per edge asks, given a graph G= (V, E) and two integers k, b, whether there is a k-page book drawing of G such that the maximum number of crossings per edge is at most b. In this paper, we study this problem from a parameterized complexity viewpoint. Specifically, we investigate the problem parameterized by both the maximum number b of crossings per edge and the vertex cover number τ of G, and show that this parameterized problem admits a kernel of size (3 b+ 1 ) · 2O(τ) and admits a fixed-parameter tractable algorithm running in time O((3b+1)·2(3b+2)·2O(τ)+τ·|V| ). Together with our previous result for fixed-order book drawing with a bounded number of crossings per edge (COCOA 2020), our result provides a more complete answer to a question posed by Bhore et al. (J. Graph Alg. Appl. 2020). © 2021, Springer Nature Switzerland AG.

关键词： Parameterization

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Research on Differential Power Analysis of Lightweight Block Cipher LED 10th

Research on Differential Power Analysis of Lightweight Block...

引用

10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Zou, Yi Li, Lang Zhao, Hui-huang Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Human Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

LED algorithm is a new lightweight encryption algorithm proposed in CHES 2011, which is used for IOT to protect the communication security of RFID tags and smart cards. It has been found that it is possible to retrieve secret key of algorithm by appropriate analysis of the devicess power consumption, and the differential power analysis is the most powerful attack. This paper proposed a method of differential power analysis on LED algorithm. Combined with hardware circuit, it designed and implemented differential power analysis of the LED encryption system. The experimental results show that method which is proposed in this paper was cracked the 64 bits key of LED. Thus, without protection, the cipher LED would be difficult to resist differential power analysis. and according this, We provide a general analysis method of differential power analysis on other lightweight cryptographic algorithms. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Light emitting diodes

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Xiang Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781665401265

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries. © 2021 IEEE.

关键词： Tumors

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