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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

IEEE Transactions on Mobile Computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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IIN-FFD:Intra-Inter Network for Face Forgery Detection

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Tsinghua science and technology 2024年第6期29卷 1839-1850页

作者： Qihua Zhou Zhili Zhou Zhipeng Bao Weina Niu Yuling Liu School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Artificial Intelligence Guangzhou UniversityGuangzhou 510006China School of Computer Science University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina School of Computer Science Hunan UniversityChangsha 410082HunanChina

Since different kinds of face forgeries leave similar forgery traces in videos,learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery ***,to accurately detect known forgeries while ensuring high generalization ability of detecting unknown forgeries,we propose an intra-inter network(IIN)for face forgery detection(FFD)in videos with continual *** proposed IIN mainly consists of three modules,i.e.,intra-module,inter-module,and forged trace masking module(FTMM).Specifically,the intra-module is trained for each kind of face forgeries by supervised learning to extract special features,while the inter-module is trained by self-supervised learning to extract the common *** a result,the common and special features of the different forgeries are decoupled by the two feature learning modules,and then the decoupled common features can be utlized to achieve high generalization ability for ***,the FTMM is deployed for contrastive learning to further improve detection *** experimental results on FaceForensic++dataset demonstrate that the proposed IIN outperforms the state-of-the-arts in ***,the generalization ability of the IIN verified on DFDC and Celeb-DF datasets demonstrates that the proposed IIN significantly improves the generalization ability for FFD.

关键词： deep learning information security image classfication neural networks face forgery face forgery detection

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Reinforcement learning based edge computing in B5G

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Digital Communications and Networks 2024年第1期10卷 1-6页

作者： Jiachen Yang Yiwen Sun Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Technology and Data Center ***Beijing100176China School of Data Science and Software Engineering Qingdao UniversityQingdao266101China

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology *** the same time,Beyond 5G will become one of the important supports for the development of edge computing *** paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge *** trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and *** results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

关键词： Reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian

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DOA-mono: Monocular Depth Estimation Focused On Dynamic Object Perception 4

DOA-mono: Monocular Depth Estimation Focused On Dynamic Obje...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Geng, Jun Yin, Hongchao Xiong, Wanqing Yi, Xiaoyun Wang, Siqi Li, Xiaoyu Xinjiang University The School of Software Urumqi China University of Electronic Science and Technology of China The School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331531225

Monocular depth estimation, as a fundamental task in the field of computer vision, has demonstrated significant application potential in areas such as autonomous driving and virtual reality (VR). Dynamic object perception refers to the model's ability to identify, locate, and effectively process the depth information of moving objects within a scene. Existing methods have shown excellent results in static scenes;however, when dealing with scenes containing moving objects, the rigid scene assumption is often violated due to the potential movement of fixed reference points in the scene. Existing methods lack the ability to perceive dynamic objects, resulting in reduced accuracy and low stability in depth estimation. To address the challenges posed by motion blur and dynamic scene changes caused by moving objects in monocular depth estimation, this paper proposes a monocular depth estimation network based on dense attention skip connections and a multi-scale feature fusion module. The proposed network enhances the model's dynamic object perception capability, improving depth estimation accuracy and robustness in complex dynamic scenes. © 2024 IEEE.

关键词： Object recognition

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Thermogalvanic hydrogel-based e-skin for self-powered on-body dual-modal temperature and strain sensing

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Microsystems & Nanoengineering 2024年第2期10卷 293-302页

作者： Zhaosu Wang Ning Li Xinru Yang Zhiyi Zhang Hulin Zhang Xiaojing Cui College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan030024China College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan030024China School of Physics and Information Engineering Shanxi Normal UniversityTaiyuan030031China

Sensing of both temperature and strain is crucial for various diagnostic and therapeutic ***,we present a novel hydrogel-based electronic skin(e-skin)capable of dual-mode sensing of temperature and *** thermocouple ion selected for this study is the iodine/triiodide(I−/I3−)redox couple,which is a common component in everyday *** leveraging the thermoelectric conversion in conjunction with the inherent piezoresistive effect of a gel electrolyte,self-powered sensing is achieved by utilizing the temperature difference between the human body and the external *** composite hydrogels synthesized from polyvinyl alcohol(PVA)monomers using a simple freeze‒thaw method exhibit remarkable flexibility,extensibility,and adaptability to human *** incorporation of zwitterions further augments the resistance of the hydrogel to dehydration and low temperatures,allowing maintenance of more than 90%of its weight after 48 h in the *** its robust thermal current response,the hydrogel was encapsulated and then integrated onto various areas of the human body,including the cheeks,fingers,and ***,the detection of the head-down state and the monitoring of foot movements demonstrate the promising application of the hydrogel in supervising the neck posture of sedentary office workers and the activity *** successful demonstration of self-powered on-body temperature and strain sensing opens up new possibilities for wearable intelligent electronics and robotics.

关键词： hydrogel modal utilizing

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A Novel Knowledge-Based Neural Network Approach to the Small-Signal Modeling of Packaged Diodes

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Progress in Electromagnetics Research C 2024年 149卷 9-14页

作者： Liu, Wenyuan Yang, Ningning Yan, Shuxia Xu, Yanlin School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an710021 China School of Electronics and Information Engineering Tiangong University Tianjin300387 China School of Electronic Science and Technology National University of Defense Technology Changsha410073 China

This paper proposes a novel knowledge-based neural network approach that, in the absence of specific device SPICE models, can utilize the measured data of actual diode devices to map the existing diode coarse model to a more accurate packaged model through neural network mapping techniques, thereby achieving precise and efficient modeling of the small-signal characteristics of diode devices. A knowledge-based neural network model for packaged diodes is proposed, which enhances modeling accuracy by learning the discrepancies between the diode coarse model and the actual device data. A training method for rapid parameter adjustment is suggested, where the neural networks within the input and output package modules automatically learn and adjust, continuously optimizing their internal parameters to enhance modeling efficiency. Modeling experiments conducted on the measurement data of the MA4AGFCP910 diode show that the proposed packaged diode model can effectively and accurately match the small-signal characteristic data of the diode device. © 2024, Electromagnetics Academy. All rights reserved.

关键词： PIN diodes

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Universal Adversarial Attack for Trustworthy LiDAR based Object Detection in Embedded Applications 4

Universal Adversarial Attack for Trustworthy LiDAR based Obj...

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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Cai, Mumuxin Wang, Xupeng Sohel, Ferdous Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Information Technology Murdoch University Murdoch Australia

ISBN: (纸本)9798331506292

3D object detection based on deep neural networks (DNNs) has widely been adopted in the field of embedded applications, such as autonomous driving. Nonetheless, recent studies have demonstrated that LiDAR data tends to exhibit intense corruptions, resulting in the failure of 3D object detection tasks. Considering the vulnerability of existing DNNs and the wide applications of 3D object detection to safety-critical scenarios, the robustness of deep 3D detection models under adversarial attacks is investigated in this work. The proposed universal adversarial attack is encoded into a perturbation voxel, which adds point-wise perturbations to benign LiDAR scenes. The detector-level perturbation voxel is generated by suppressing the detector's predictions on training samples, which covers the entire perceptual range of the detector. The designed perturbation voxels can be applied to the entire scene to simulate the global perturbation inherent in LiDAR. And it can adapt to other detectors with various point cloud representations, which makes attack universal. To test the effectiveness of the proposed attack, it was launched against a number of deep 3D detectors using several datasets. The results demonstrate its superiority over existing art. © 2024 IEEE.

关键词： 3D object detection LiDAR sensors Point cloud Security of embedded applications Universal adversarial attack

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Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation

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Chinese Physics B 2024年第3期33卷 112-117页

作者：程晓昱解晨雪刘宇伦白瑞雪肖南海任琰博张喜林马惠蒋崇云 College of Electronic Information and Optical Engineering Nankai UniversityTianjin 300350China School of Physical Science and Technology Tiangong UniversityTianjin 300387China

Mechanically cleaved two-dimensional materials are random in size and *** atomically thin flakes by human experts is inefficient and unsuitable for scalable *** learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training *** we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the ***3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual *** sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer *** feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.

关键词： two-dimensional materials deep learning data augmentation generating adversarial networks

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Excitonic devices based on two-dimensional transition metal dichalcogenides van der Waals heterostructures

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Frontiers of Chemical science and engineering 2024年第2期18卷 41-67页

作者： Yulun Liu Yaojie Zhu Zuowei Yan Ruixue Bai Xilin Zhang Yanbo Ren Xiaoyu Cheng Hui Ma Chongyun Jiang College of Electronic Information and Optical Engineering Nankai UniversityTianjin 300350China School of Physical Science and Technology Tiangong UniversityTianjin 300387China

Excitonic devices are an emerging class of technology that utilizes excitons as carriers for encoding, transmitting, and storing information. Van der Waals heterostructures based on transition metal dichalcogenides often exhibit a type II band alignment, which facilitates the generation of interlayer excitons. As a bonded pair of electrons and holes in the separation layer, interlayer excitons offer the chance to investigate exciton transport due to their intrinsic out-of-plane dipole moment and extended exciton lifetime. Furthermore, interlayer excitons can potentially analyze other encoding strategies for information processing beyond the conventional utilization of spin and charge. The review provided valuable insights and recommendations for researchers studying interlayer excitonic devices within van der Waals heterostructures based on transition metal dichalcogenides. Firstly, we provide an overview of the essential attributes of transition metal dichalcogenide materials, focusing on their fundamental properties, excitonic effects, and the distinctive features exhibited by interlayer excitons in van der Waals heterostructures. Subsequently, this discourse emphasizes the recent advancements in interlayer excitonic devices founded on van der Waals heterostructures, with specific attention is given to the utilization of valley electronics for information processing, employing the valley index. In conclusion, this paper examines the potential and current challenges associated with excitonic devices.

关键词： excitonic devices van der Waals heterostructures transition metal dichalcogenides interlayer excitons valley-Hall effect optoelectronics

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