检索结果-南通市图书馆

Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2021年第7期18卷 134-146页

作者： Chao Pan Zhao Wang Zhenyu Zhou Xincheng Ren Hebei key laboratory of Power Internet of Things Technology North China Electric Power UniversityBeijing102206Baoding071003HebeiChina Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan UniversityYanan 716000China

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality curse.In this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs.Then,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort way.Compared with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

关键词： collaborative vehicular networks task of-floading URLLC awareness deep Q-learning

维普期刊数据库

Research on Electricity Consumption Model of Library Building Based on data Mining

在线全文

学校读者我要写书评

暂无评论

energy Engineering 2022年第6期119卷 2407-2429页

作者： Jiaming Dou Hongyan Ma Rong Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China Institute of Distributed energy Storage Safety big data Beijing University of Civil Engineering and ArchitectureBeijing102616China Beijing key laboratory of intelligent processing for Building big data Beijing102616China

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest subject.Although much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their subsystems.Using classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus library.Smart meter data from a library in Beijing,China,is adopted in this paper.Building electricity consumption patterns are investigated on an hourly/daily/monthly basis.According to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor temperatures.Hourly data analysis revealed a relatively stable consumption pattern.It shows three different types of daily load profiles.Daily data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of 5.9.Furthermore,links between temperature and subsystems were also discovered.Through a case study of library,this study provides a unique insight into campus electricity use.The results could help to develop operational strategies for campus facilities.

关键词： Electricity consumption data mining load profile campus building

Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China shaanxi key laboratory of Network data Analysis and intelligent processing Xi’an710121China Xi’an key laboratory of big data and intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an key laboratory of big data and intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential applications.Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR.However,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images.In addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local features.To tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent information.The main highlights of this study include two aspects.On the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images.Specifically,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features.On the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training.Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

Server load prediction algorithm based on CM-MC for cloud systems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2018年第5期29卷 1069-1078页

作者： XU Xiaolong ZHANG Qitong MOU Yiqi LU Xinyuan Jiangsu key laboratory of big data Security & intelligent processing Nanjing University of Posts and Telecommunications State key laboratory of Information Security Chinese Academy of Sciences Shanghai Stock Exchange Technology Co. LtdShanghai Stock Exchange Institute of big data Research at Yancheng Nanjing University of Posts and Telecommunications

Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.

关键词： cloud computing load prediction cloud model Markov chain energy saving

维普期刊数据库

Demand Learning and Cooperative Deployment of UAV Networks

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2022年第3期31卷 408-415页

作者： ZHANG Xiao WANG Xuehe XU Xinping ZHAO Yingchao South-Central University for Nationalities Caritas Institute of Higher Education Sun Yat-sen University & Guangdong key laboratory of big data Analysis and processing Nanyang Technological University

Unmanned aerial vehicle(UAV) as a powerful tool has found its applicability in assisting mobile users to deal with computation-intensive and delaysensitive applications(e.g., edge computing, high-speed Internet access, and local caching). However, deployment of UAV-aided mobile services(UMS) faces challenges due to the UAV limitation in wireless coverage and energy storage. Aware of such physical limitations, a future UMS system should be intelligent enough to self-plan trajectories and best offer computational capabilities to mobile users. There are important issues regarding the UAV-user interaction, UAV-UAV cooperation for sustainable service provision, and dynamic UMS pricing. These networking and resource management issues are largely overlooked in the literature and this article presents intelligent solutions for cooperative UMS deployment and operation. Mobile users’ locations are generally private information and changing over time. How to learn on-demand users’ truthful location reporting is important for determining optimal UAV deployment in serving all the users fairly. After addressing the truthful UAV-user interaction issue via game theory, we further study the UAV network sustainability for UMS provision by minimizing the energy consumption cost during deployment and seeking UAV-UAV cooperation. Finally, for profit-maximizing purpose, we analyze the cooperative UAVs’ deployment, capacity allocation, and dynamic service pricing.

关键词： Unmanned aerial vehicle networks Wireless coverage Dynamic pricing Capacity allocation

intelligent Preamble Allocation for Coexistence of mMTC/URLLC Devices:A Hierarchical Q-Learning Based Approach

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

China Communications 2023年第8期20卷 44-53页

作者： Jiadai Wang Chaochao Xing Jiajia Liu National Engineering laboratory for Integrated Aero-Space-Ground-Ocean big data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China

The emergence of various commercial and industrial Internet of Things(IoT)devices has brought great convenience to people’s life and production.Both low-power,massively connected mMTC devices(MDs)and highly reliable,low-latency URLLC devices(UDs)play an important role in different application scenarios.However,when dense MDs and UDs periodically initiate random access(RA)to connect the base station and send data,due to the limited preamble resources,preamble collisions are likely to occur,resulting in device access failure and data transmission delay.At the same time,due to the highreliability demands of UDs,which require smooth access and fast data transmission,it is necessary to reduce the failure rate of their RA process.To this end,we propose an intelligent preamble allocation scheme,which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side.In particular,considering the limited processing capacity and energy of IoT devices,we adopt the lightweight Qlearning algorithm on the device side and design simple states and actions for them.Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

关键词： preamble allocation random access mMTC URLLC reinforcement learning

维普期刊数据库

Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

在线全文

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第11期47卷 1987-2006页

作者： Yu Zhang Mingkui Zhang Jitao Li Guangshu Chen School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing key laboratory of intelligent processing for Building big data Beijing University of Civil Engineering and ArchitectureBeijing100044China State key laboratory for GeoMechanics and Deep Underground Engineering China University of Mining&TechnologyBeijing100083China

Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and tunnels.Light rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property loss.The frequency and degree of rockburst damage increases with the excavation depth.Moreover,rockburst is the leading engineering geological hazard in the excavation process,and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering.Therefore,the prediction of rockburst intensity grade is one problem that needs to be solved urgently.By comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient method.This overcomes the low accuracy problem of a single evaluation index prediction method.Following this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed.The batch gradient descent(BGD)module is used to replace the gradient descent algorithm,which effectively improves the efficiency of the network and reduces the model training time.Moreover,the multi-scale residual(MSR)module solves the problem of network degradation when there are too many hidden layers of the deep neural network(DNN),thus improving the model prediction accuracy.The experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable models.Finally,actual projects such as Qinling Tunnel and Daxiangling Tunnel,reached an accuracy of 100%.The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.

关键词： Rockburst prediction rockburst intensity grade deep neural network batch gradient descent multi-scale residual

Improved VGG Model for Road Traffic Sign Recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2018年第10期57卷 11-24页

作者： Shuren Zhou Wenlong Liang Junguo Li Jeong-Uk Kim Hunan Provincial key laboratory of intelligent processing of big data on Transportation Changsha University of Science&TechnologyChangsha410114China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of energy Grid Sangmyung UniversitySeoul110743Korea

Road traffic sign recognition is an important task in intelligent transportation system.Convolutional neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification.In this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural network.In natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG model.The IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training time.The paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification effect.It uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the experiment.The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

关键词： intelligent transportation traffic sign deep learning GTSRB data augmentation