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A DQN-Based Cache Strategy for Mobile Edge Networks

Computers, Materials & Continua 2022年第5期71卷 3277-3291页

作者： Siyuan Sun Junhua Zhou Jiuxing Wen yifei wei Xiaojun Wang School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing100854China Ningbo Sunny Intelligent Technology Co. LTDYuyao315400China Dublin City University Dublin 9Ireland

The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse,thus the network transmission delay and system throughput can be improved significantly.Since the duplicate content transmissions between edge network and remote cloud can be reduced,the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great extent.This paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning algorithm.The deep neural network(DNN)and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network(DQN),in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning solution.The parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method,so the DQN algorithm could converge to the optimal solution quickly,and the network performance of the content caching policy can be optimized.The simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.

关键词： Mobile edge network edge caching energy efficiency

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Multi-Level Feature Aggregation-Based Joint Keypoint Detection and Description

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Computers, Materials & Continua 2022年第11期73卷 2529-2540页

作者： Jun Li Xiang Li yifei wei Mei Song Xiaojun Wang Beijing Key Laboratory of Work Safety Intelligent Monitoring School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing100876China Dublin City University Dublin9Ireland

Image keypoint detection and description is a popular method to find pixel-level connections between images,which is a basic and critical step in many computer vision tasks.The existing methods are far from optimal in terms of keypoint positioning accuracy and generation of robust and discriminative descriptors.This paper proposes a new end-to-end selfsupervised training deep learning network.The network uses a backbone feature encoder to extract multi-level feature maps,then performs joint image keypoint detection and description in a forward pass.On the one hand,in order to enhance the localization accuracy of keypoints and restore the local shape structure,the detector detects keypoints on feature maps of the same resolution as the original image.On the other hand,in order to enhance the ability to percept local shape details,the network utilizes multi-level features to generate robust feature descriptors with rich local shape information.A detailed comparison with traditional feature-based methods Scale Invariant Feature Transform(SIFT),Speeded Up Robust Features(SURF)and deep learning methods on HPatches proves the effectiveness and robustness of the method proposed in this paper.

关键词： Multi-scale information keypoint detection and description artificial intelligence

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Online Rail Fastener Detection Based on YOLO Network

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Computers, Materials & Continua 2022年第9期72卷 5955-5967页

作者： Jun Li Xinyi Qiu yifei wei Mei Song Xiaojun Wang Beijing Key Laboratory of Work Safety Intelligent Monitoring School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing100876China Dublin City University Dublin9Ireland

Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production.Track is the basic equipment of railway transportation,and its performance directly affects the service lifetime of railway lines and vehicles.The anomaly detection of rail fasteners is in a priority,while the traditional manual method is extremely inefficient and dangerous to workers.Therefore,this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners,but also to recognize the fasteners states.To be more specific,this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5(YOLOv5)network,and completes the real-time classification of fastener states.The improved YOLOv5 network proposed contains five sections,which are Input,Backbone,Neck,Head Detector and a read-only Few-shot Example Learning module.The main purpose of this project is to improve the detection precision and shorten the detection time.Ultimately,the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms.This model achieves on-line fastener detection by completing the“sampling-detection-recognition-warning”cycle of a single sample before the next image is sampled.Specifically,the mean average precision of model reaches 94.6%.And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU.

关键词： Fastener detection deep learning state recognition real-time classification

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Research and Practice of Telecommunication User Rating Method Based on Machine Learning

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Journal on Big Data 2022年第1期4卷 27-39页

作者： Qian Tang Hao Chen yifei wei Beijing University of Posts and Telecommunications Beijing100876China

The machine learning model has advantages in multi-category credit rating classification.It can replace discriminant analysis based on statistical methods,greatly helping credit rating reduce human interference and improve rating efficiency.Therefore,we use a variety of machine learning algorithms to study the credit rating of telecom users.This paper conducts data understanding and preprocessing on Operator Telecom user data,and matches the user’s characteristics and tags based on the time sliding window method.In order to deal with the deviation caused by the imbalance of multi-category data,the SMOTE oversampling method is used to balance the data.Using the Removing features with low variance method and packaging method for feature selection,then the basic models are established.The empirical results of the model show that the Random Forest and XGBOOST ensemble models are better than the single models such as Bayes,SVM,KNN,and Decision Tree.The performance of Decision Tree in single models is better.Therefore,Random Forest,XGBOOST and Decision Tree models were selected to debug the hyper parameters to achieve model optimization.Based on the optimized model,the accuracy,recall,precision,confusion matrix and other indicators are evaluated,and it is concluded that lowlevel recognition is more accurate than high-level recognition and fewer misjudgments.Comparing the evaluation indicators of each level of different models,it is found that the integrated model performs better,indicating that Random Forest and XGBOOST are more suitable for solving the problem of telecommunications user rating.For this reason,this article proposes an implementation plan based on Random Forest and XGBOOST algorithm and model for the problem of telecommunications user rating.

关键词： Credit rating model evaluation random forest XGBOOST

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Research on Service Function Chain Orchestrating Algorithm Based on SDN and NFV

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Journal of Quantum Computing 2022年第1期4卷 39-52页

作者： Yuning Jia Yu Gong yifei wei Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and TelecommunicationsBeijing100876China

Software defined network(SDN)and network function virtualization(NFV)have become a new paradigm of a new generation of network architecture.SDN and NFV can effectively improve the flexibility of deploying and managing service function chains(SFCs).By combining SDN and NFV and applying them to the resource orchestration problem of SFC deployment,the three-tier architecture consisting of SDN controller,network function virtualization and physical underlying computing resource layer in the process of heterogeneous network resource mapping is considered.And an optimization algorithm for active control resources based on SDN and NFV is proposed.Firstly,the user’s utility is modeled by the multistandard aggregated multi-criteria utility algorithm,and the optimization goal is transformed into the problem of maximizing the user’s utility.Then the controller,based on the algorithm’s prediction of the future state and realtime monitoring of the network utilization,makes decisions and issues control commands for the arriving SFC requests,based on which it occupies the underlying resources held by the virtualized network function(VNF).The simulation results show that,compared with the static timing resource allocation algorithm,the active control resource deployment algorithm proposed in the article has better performance in terms of resource utilization,acceptance rate,and user creation utility.

关键词： SDN NFV service function chain resource allocation

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Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing

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Computers, Materials & Continua 2019年第4期59卷 89-104页

作者： yifei wei Zhaoying Wang Da Guo FRichard Yu Beijing Key Laboratory of Work Safety Intelligent Monitoring School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing100876China Department of Systems and Computer Engineering Carleton UniversityOttawaON K1S 5B6Canada

To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.

关键词： Mobile edge computing computation offloading resource allocation deep reinforcement learning

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Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

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Computers, Materials & Continua 2020年第7期64卷 501-514页

作者： yifei wei Yu Gong Qiao Li Mei Song Xiaojun Wang Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and TelecommunicationsBeijing100867China School of Electronic Engineering Dublin City UniversityDublin9Ireland

In this paper,maximizing energy efficiency(EE)through radio resource allocation for renewable energy powered heterogeneous cellular networks(HetNet)with energy sharing,is investigated.Our goal is to maximize the network EE,conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources.We define the objective function as a sum weighted EE of all links in the HetNet.We formulate the resource allocation problem in terms of subcarrier assignment,power allocation and energy sharing,as a mixed combinatorial and non-convex optimization problem.We propose an energy efficient resource allocation scheme,including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker(KKT)conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning(RL).Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations.The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.

关键词： Heterogeneous networks energy harvesting energy efficiency resource allocation energy sharing

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Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

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Computers, Materials & Continua 2020年第6期63卷 1515-1532页

作者： yifei wei Yinxiang Qu Min Zhao Lianping Zhang F.Richard Yu Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and TelecommunicationsBeijing100876China Alibaba Cloud Computing Hangzhou311121China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.

关键词： D2D communication resource allocation power control multi-agent Q-learning fuzzy C-means

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OTT Messages Modeling and Classification Based on Recurrent Neural Networks

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Computers, Materials & Continua 2020年第5期63卷 769-785页

作者： Guangyong Yang Jianqiu Zeng Mengke Yang yifei wei Xiangqing Wang Zulfiqar Hussain Pathan School of Economics&Management Beijing University of Posts and TelecommunicationsBeijing100876China School of Automation Beijing University of Posts and TelecommunicationsBeijing100876China School of Electronic&Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Mehran University of Engineering&Technology JamshoroPakistan

A vast amount of information has been produced in recent years,which brings a huge challenge to information management.The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages.In this paper,we propose a nine-rectangle-grid information model according to the information value and privacy,and then present information use policies based on the rough set theory.Recurrent neural networks were employed to classify OTT messages.The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages,ending with a reliable trained classification model.Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages.

关键词： OTT messages information privacy nine-rectangle-grid hierarchical classification recurrent neural networks

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电驱式玉米高速作业播种机控制系统设计与试验

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中国农机化学报 2024年第3期45卷 24-30,57页

作者：蒋振晗王钰旭魏逸飞于艳孙永佳孙宜田青岛农业大学机电工程学院青岛市266109 山东省农业机械科学研究院济南市250100 农业农村部黄淮海现代农业装备重点实验室济南市250100

我国市面上流行的玉米播种机多数采用指夹式排种器和气吸式排种器,依靠地轮传递带动排种器排种,在一定程度上提高播种粒距均匀性,但排种仍然会受到地轮打滑的影响。针对以上问题,设计电驱式玉米高速作业智能播种机控制系统,以STM32F103... 详细信息

我国市面上流行的玉米播种机多数采用指夹式排种器和气吸式排种器,依靠地轮传递带动排种器排种,在一定程度上提高播种粒距均匀性,但排种仍然会受到地轮打滑的影响。针对以上问题,设计电驱式玉米高速作业智能播种机控制系统,以STM32F103芯片作为主控器核心,该系统由地轮安装速度传感器测量机具速度,根据智能车载终端设置的作业参数,通过算法计算目标排种电机转速,实现播种株距与机具前进速度实时匹配,采用红外光电式传感器进行实时播种监测。室内试验和田间试验结果表明:该系统转速控制精度高,播种计数和漏播监测精度较高;设置株距为25 cm时,作业速度分别为8 km/h、10 km/h、12 km/h进行3组重复试验,在3种作业速度下,平均合格指数分别为95.18%、94.36%、91.24%;变异系数分别为15.36%、16.83%、18.24%。

关键词：玉米播种机控制系统电驱式高速作业智能化

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