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RELIABILITY-DRIVEN REAL-TIME TASK REPLICATION FOR FAULT TOLERANCE IN HIERARCHICAL SCHEDULING MULTIPROCESSOR SYSTEMS

RELIABILITY-DRIVEN REAL-TIME TASK REPLICATION FOR FAULT TOLE...

2013IET International Conference on Information and Communication Technologies(IETICT2013)2013IET信息与通信技术国际会议

作者： Lin Liu Renfa Li Rui Li Yong Xie Qingguang Zeng Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangshaChina

ISBN: (纸本)9781629931357

The increasing complexity of systems poses a big challenge to design systems with fault-tolerant *** aim of this study was to establish fault-tolerant system for multiprocessor platform with the help of hierarchical scheduling framework.A mathematical fault and reliability model for hierarchical scheduling framework was proposed firstly,and then a heuristic algorithm by exploiting the difference of task resource overhead and the probability of task failures was presented and a task replication scheme was given *** experimental results demonstrated the proposed scheme could effectively guarantee reliability goal and reduce resource consumption simultaneously.

关键词： multiprocessor fault tolerance task replication reliability requirements the hierarchical scheduling

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Energy efficiency evaluation based on QoS parameter specification for cloud systems 22

Energy efficiency evaluation based on QoS parameter specific...

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22nd IEEE International Conference on High Performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Long, Saiqin Yu, Hao Li, Zhetao Tian, Shujuan Li, Yun Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan Intl. Sci. and Technol. Cooperation Base of Intelligent Network School of Computer Science Xiangtan China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China

ISBN: (纸本)9781728176499

Energy efficiency evaluation (EEE) is a research difficulty in the field of cloud computing. The current research mainly considers the relevant energy efficiency indicators of cloud systems and weights the interrelationship between energy consumption, system performance and QoS requirements. However, it lacks a combination of subjective and objective, qualitative and quantitative evaluation method to accurately evaluate the energy efficiency of cloud systems. We propose a novel EEE method based on the QoS parameter specification for cloud systems (EEE-QoS). Firstly, it reduces the metric values that affect QoS requirements to the same dimension range and then establishes a belief rule base (BRB). The best-worst method is utilized to determine the initial weights of the premise attributes in the BRB model. Then, the BRB model parameters are optimized by the mean-square error, the activation weight is calculated, and the activation rules of the evidence reasoning algorithm are integrated to evaluate the belief of the conclusion. The quantitative and qualitative evaluation of the energy efficiency of cloud systems is realized. The experiments show that the proposed method can accurately and objectively evaluate the energy efficiency of cloud systems. © 2020 IEEE.

关键词： Quality of service

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Improved Modular Convolution Neural network for Human Pose Estimation 12th

Improved Modular Convolution Neural Network for Human Pose E...

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Zhang, Zhengxuan Dong, Jing Zhou, Dongsheng Fang, Xiaoyong Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian1160243 China Research Institute of Human Factors and Safety Engineering Hunan Institute of Technology HengyangHunan Province421002 China

ISBN: (纸本)9783030237110

Human pose estimation in image is an important branch of computer vision and graphics research. In this paper, an improved modular convolution neural network is proposed to solve the problem of human pose estimation in static 2D images. A cascaded three-stage full convolutional network (FCN) can learn the non-linear mapping from image feature space to human pose space in an end-to-end way. In order to improve the accuracy of predicting joints, the method of multi-feature source fusion is adopted to improve the estimation process of the human body posture. The first two stages of the network focus on learning local image features and joints neighborhood pixel features, and these features are merged in the third stage of the network. Finally, the coordinates of human joints are obtained by regression of the merged features. In our experiments, using the strict PCP criteria on the full body pose dataset LSP, the average prediction accuracy of our method is 79.3%. In addition, using the PCKh standard on the upper body pose dataset FLIC, our method achieves an average prediction accuracy of 93% without additional training. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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Research on optical microfiber pressure sensor applied in marine field 4

Research on optical microfiber pressure sensor applied in ma...

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4th Optics Young Scientist Summit, OYSS 2020

作者： Zhou, Lingjun Yao, Sijia Huang, Huimin Yang, Junbo Zhang, Zhenrong Yu, Yang Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning530004 China Center of Material Science College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China Hunan University of information technology Changsha Hunan Province410151 China

ISBN: (数字)9781510643970

ISBN: (纸本)9781510643963

A novel and compact pressure sensor for marine application based on optical microfiber coupler interferometer(OMCI) is proposed theoretically. We theoretically analyzed the characteristics of OMCI's dual sensing unit, The highest pressure sensitivity of the waist sensing unit is 654pm/Mpa, and it increases with longer wavelength. The pressure sensitivity of the interference arm sensing unit is 7pm/Mpa, the sensitivity increases with the length of the embedded elastomer. Its excellent performance can provide strong theoretical guidance for the practical application of optical ocean monitoring equipment. © SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Pressure sensors

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A high-performance CNN method for offline handwritten Chinese character recognition and visualization

arXiv

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arXiv 2018年

作者： Melnyk, Pavlo You, Zhiqiang Li, Keqin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Department of Computer Science State University of New York at New Paltz New PaltzNY12561 United States

Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them did not address the problem of network interpretability. We propose a new architecture of a deep CNN with high recognition performance which is capable of learning deep features for visualization. A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage. We introduce a modification of global weighted average pooling (GWAP) - global weighted output average pooling (GWOAP). This paper demonstrates how they allow us to calculate class activation maps (CAMs) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class. Evaluating on the ICDAR-2013 offline HCCR competition dataset, we show that our model enables a relative 0.83% error reduction while having 49% fewer parameters and the same computational cost compared to the current state-of-the-art single-network method trained only on handwritten data. Our solution outperforms even recent residual learning approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Visualization

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Joint Partial Offloading and Resource Allocation for Distributed Multi-access Edge computing Enabled Vehicular network

TechRxiv

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TechRxiv 2022年

作者： Ma, Guifu Hu, Manjiang Wang, Xiaowei Wu, Di Li, Haoran Bian, Yougang Zhu, Konglin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Hunan University Wuxi214072 China The College of Design Hunan University Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China The College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio. Different from existing two-level centralized solution methods, we give a new perspective to deal with multi-variable non-convex optimization problem in a distributed manner. That is, by analyzing the feasibility conditions of the problem, we separate the coupling between variables and derive the bounds of partial offloading ratio and resource allocation ratio based on the constraints. We further separate the coupling variables among ICVs and transform the original problem into an equivalent form and decompose it into a two-level convex problem, where each sub-problem satisfies the task offloading requirements. We then develop two low-complexity distributed algorithms to obtain the optimal solution. Finally, extensive simulations demonstrate that our proposed algorithms can converge faster and significantly outperform the baseline schemes in terms of system cost. © 2022, CC BY.

关键词： Resource allocation

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Stacking-Based Attention Temporal Convolutional network for Action Segmentation 48

Stacking-Based Attention Temporal Convolutional Network for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Liu Jiang, Yu Hong, Junkun Wu, Zhenjie Yang, Zhan Long, Jun Central South University School of Computer Science and Engineering China Central South University Big Data Institute Central South University China Central South University Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha China

ISBN: (纸本)9781728163277

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast. © 2023 IEEE.

关键词： Convolution

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Completion time-a ware flow scheduling in heterogenous networks 1

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： He, Shiming Xie, Kun Zhang, Dafang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Hunan Province Engineering Research Center of Electric Transportation and Smart Distributed Network Changsha University of Science and Technology Changsha410114 China College of Computer Science and Electronics Engineering Hunan University Changsha410082 China Department of Electrical and Computer Engineering State University of New York at Stony Brook NY United States

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

In China, the expressway isn’t free. When a vehicle exits, the exit toll station needs to calculate the toll according to the vehicle trajectory obtained by sending a trajectory query task to the trajectory center remotely. For the accurate of toll and keep the exit smoothly, transmitting the trajectory result reliably and in time is a key issue. There are three heterogenous networks in expressway, Fibernet, Telecommunications network and 3G. We concurrently exploit three heterogenous networks by allocating the trajectory result into three sub-flows where each sub-flow is transmitted in one kind of networks. But existing multipath transfer solution(FMTCP) doesn’t consider the difference of completed time among all sub-flows and can’t make sure the completed time in time. Therefore, we formulate the sub-flows schedule as a integer programming problem, which is proved to be NP-hard problem, and propose a Completion time-aware Flow Scheduling scheme (CaFS) which calculates the size of sub-flows. For the reliably, it exploits generation-based network coding. For the transmission in time, CaFS allocates the size of sub-flow into suitable generation and scheduling the generations. The simulation results demonstrate that, comparing with the FMTCP, CaFS improves 31. 9% in completion time and 30% in task finished rate. © Springer International Publishing Switzerland 2015.

关键词： network coding

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A NOVEL CONVOLUTIONAL NEURAL network BASED ON ADAPTIVE MULTI-SCALE AGGREGATION AND BOUNDARY-AWARE FOR LATERAL VENTRICLE SEGMENTATION ON MR IMAGES 47

A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI...

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2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022

作者： Ye, Fei Wang, Zhiqiang Zhu, Sheng Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Department of Radiology Affiliated Hospital of Xiangnan University Chenzhou423000 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781665405409

In this paper, we propose a novel convolutional neural network based on adaptive multi-scale feature aggregation and boundary-aware for lateral ventricle segmentation (MB-Net), which mainly includes three parts, i.e., an adaptive multi-scale feature aggregation module (AMSFM), an embedded boundary refinement module (EBRM), and a local feature extraction module (LFM). Specifically, the AMSFM is used to extract multi-scale features through the different receptive fields to effectively solve the problem of distinct target regions on magnetic resonance (MR) images. The EBRM is intended to extract boundary information to effectively solve blurred boundary problems. The LFM can make the extraction of local information based on spatial and channel attention mechanisms to solve the problem of irregular shapes. Finally, extensive experiments are conducted from different perspectives to evaluate the performance of the proposed MB-Net. Furthermore, we also verify the robustness of the model on other public datasets, i.e., COVID-SemiSeg and CHASE DB1. The results show that our MB-Net can achieve competitive results when compared with state-of-the-art methods. © 2022 IEEE

关键词： Magnetic resonance

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