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Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

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GraphConvNet: A Dual Network Utilizing Local Features Coupled with Structural Information for Predicting Knee Osteoarthritis

GraphConvNet: A Dual Network Utilizing Local Features Couple...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Pengju Dai, Dawei Zou, Kai Yang, Xionghui Wang, Guoyin Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China

ISBN: (纸本)9798350386226

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor's clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet. © 2024 IEEE.

关键词： Joints (anatomy)

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A Feature Fusion Network with Multiscale Adaptively Attentional for Object Detection in Complex Traffic Scenes

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年

作者： Cai, Fei Qu, Zhong Yin, Xuehui Chongqing Key Laboratory of Computational Intelligence The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education The Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Currently, small object detection in complex traffic scenarios remains a major challenge due to variations in object shape, scales, and external environment, as well as a high inter-class similarity. Although the latest algorithms achieve high accuracy, the results for small objects are still unsatisfactory. To obtain satisfactory small object detection results, the YOLOv8 algorithm is used to construct a feature fusion network with multiscale adaptively attentional (MAAFFNet). Firstly, the C2f module is modified to replace the simple summation operation with Multiscale Iterative Attentional Feature Fusion (MSIAFF), which enhances the ability of multi-scale aggregation of the C2f module and suppresses the interference of irrelevant information, resulting in a richer semantic feature map. Secondly, the adaptive spatial feature fusion (ASFF) module is introduced to deal with the feature maps of different scales, allocate the weights of spatial features of different levels, and enhance the consistency of semantic information between each layer of the feature maps, so as to improve the detail richness of the network. Finally, to achieve more accurate classification of small objects, a Slide loss function is introduced to achieve object classification and localization. Our proposed algorithm has been extensively experimented on KITTI, COCO-traffic and VOC 2012 datasets, respectively, comparing the proposed method to popular two-stage and single-stage object detection algorithms, the proposed method performs well in terms of accuracy. © 2024 IEEE.

关键词： Object recognition

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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OSFS-Vague: Online streaming feature selection algorithm based on vague set

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

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DFGR: Deep Feature Graph Representation for Predicting Knee Osteoarthritis 4

DFGR: Deep Feature Graph Representation for Predicting Knee ...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xu, Tao Dai, Dawei Wang, Yuqi Li, Gen Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China Shanghai Jiaotong University School of Medicine Department of Orthopedics Ruijin Hospital Shanghai China

ISBN: (纸本)9798350394375

Knee osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is evaluated by observing the X-ray images of the knee joint, which is highly dependent on the subjective experience of the doctor. In this study, we developed a novel deep CNN model that integrates structural information processing to predict KOA severity automatically. Specifically, (1) The knee joint regions of the original X-ray images are segmented using automatic detection;(2) We employed a pre-trained deep CNN models to extract deep features of key regions, and construct their corresponding graph representations;(3) A graph attention network (GAT) was designed as a fine-tuning module to build a KOA prediction model. Experiments demonstrate that our method significantly improves the performance in multiple aspects. In addition, our method demonstrates good interpretability. Codes are available at https://***/ddw2AIGROUP2CQUPT/DFGR © 2024 IEEE.

关键词： Forecasting

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On RNN-Based k-WTA Models With Time-Dependent Inputs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2034-2036页

作者： Mei Liu Mingsheng Shang the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China

Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeas... 详细信息

关键词： letter convergence finite

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Model Predictive Current Control for Asymmetric Six-phase Permanent Magnet Motors with Less Computation and Optimized Voltage Vector Magnitude

Model Predictive Current Control for Asymmetric Six-phase Pe...

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2022 International Conference on Smart Energy and Electrical Engineering, SEEE 2022

作者： Sun, Jinyu Chen, Fubing Huai'An Key Laboratory of Big Data Intelligent Computing and Analysis China The School of Computer Science and Technology Huaiyin Normal University Huaian223001 China

For the sake of the fixed magnitude of candidate control sets, the conventional model predictive current control (MPCC) for asymmetric six-phase permanent magnet motors suffers from huge harmonic currents. This article designs a novel MPCC that aims to improve operational performance. First, virtual-voltage vector (VV) sets are synthesized as the new candidate vectors to restrain harmonic currents. Then, a simplified method for evaluating voltage vectors is used to alleviate the computation in the enumeration process. Moreover, the duty cycle modulation with the deadbeat solution is introduced to optimize the magnitude of the VV. The modulation range can be expanded by utilizing null vectors, which will significantly reduce the torque fluctuations and flux ripples. The experiments are conducted to verify the designed control strategy. © Published under licence by IOP Publishing Ltd.

关键词： Vectors

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Auxiliary Attributes-Guided Face Age Estimation 11th

Auxiliary Attributes-Guided Face Age Estimation

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： You, Fan Zhong, Fujin Yu, Hong Hu, Jun Yang, Yan Liu, Mengqi School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing 400065 China

ISBN: (纸本)9783031927409

Facial age estimation is a critical task within computer vision, with wide-ranging applications in demographic analysis, video retrieval, and access control. Prior research has highlighted that facial aging is significantly influenced by attributes such as gender and ethnicity, which present substantial challenges to achieving accurate and robust age prediction. To tackle this challenge, we propose MSAN, an innovative end-to-end age estimation framework that integrates auxiliary information, including gender and ethnicity, during the prediction phase and dynamically models their interactions with age. Extensive evaluations on multiple public datasets demonstrate that our model outperforms several state-of-the-art approaches. This study provides an effective solution for enhancing the accuracy of facial age estimation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Age estimation Auxiliary-guided Multitask learning

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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