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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhiyong Li Tingkun Yuan Bo Yang Shilong Jiang Yanmei Xie College of Computer Science and Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China PKU-HKUST Shenzhen-Hongkong Institution Shenzhen China Hunan Vocational College of Modern Logistics Changsha China

His article proposes a new hybrid algorithm EBCRO, which combines the local search operator of chemical reaction optimization algorithm (CRO) with modified employed bee operator (EB) main part of artificial bee colony algorithm (ABC), and the introduction of probability-based selection of molecular method. This algorithm creates new solutions not only by local search operation of CRO but also mechanisms of EB. CRO is a newly, effectively swarm intelligence optimization algorithm. However, owing to random characteristics of CRO and not make use of other solutions information, its search ability and convergence speed are inefficient. EB as a part of ABC has simplicity and efficiency global search ability, but the solution is updated only by other solutions information which leading to lack of local search ability. Therefore, this paper presents EBCRO, which can balance the exploitation and exploration of algorithm of numerical function optimization. In this study, EBCRO is tested on 23 benchmark numerical functions, and the simulation results of EBCRO, CRO, orthogonal chemical reaction optimization (OCRO), hybrid particle swarm and chemical reaction optimization (HP-CRO2), and ABC showed that EBCRO outperforms other algorithms in convergence performance.

关键词： Optimization Chemicals Convergence Particle swarm optimization Linear programming Kinetic energy

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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...

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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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Non-cooperative game approach for task offloading in edge clouds

arXiv

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arXiv 2018年

作者： Yang, Bo. Li, Zhiyong Liu, Wenbin College of Information and Management Hunan University of Finance and Economics Changsha410205 China College of Computer Science and Electronic Engineering of Hunan University National Supercomputing Center in Changsha Key Laboratory for Embedded and Network Computing of Hunan Province Changsha410082 China

—Task offloading provides a promising way to enhance the capability of the mobile terminal (also called terminal user) that is distributed on network edge and communicates edge clouds with wireless. Generally, there are multiple edge cloud nodes with distinct processing capability in a geographic area, which can offer computing service for various terminal users. Furthermore, the terminal users are competitive and selfish, i.e., each user takes into account only maximizing her own profit, while conducting task offloading strategies. In this paper, we focus on the resource management optimization for edge clouds, and formulate the problem of resource competition among terminal users as a non-cooperative game, in which the terminal user who acts as the player always pursues the minimization of the expected response time for her tasks by optimizing allocation strategies. We present the utility function of the user with queuing theory, and then prove the existence of Nash equilibrium for the formulated game. Using the concept of Nash bargaining solution to calculate the optimal task offloading scheme for the user, we propose a distributed task offloading algorithm with low computation complexity. The results of simulated experiments demonstrate that our method can quickly reach the Nash equilibrium point, and deliver satisfying performance at the expected response time of the user’s tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Game theory

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A Real-Time CAN-CAN Gateway with Tight Latency Analysis and Targeted Priority Assignment

A Real-Time CAN-CAN Gateway with Tight Latency Analysis and ...

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Real-Time Systems Symposium (RTSS)

作者： Guoqi Xie Haijie Gong Yunbo Han Samarjit Chakraborty Wanli Chang College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China Future Network Lab CSIG Tencent China University of North Carolina at Chapel Hill USA University of York UK

ISBN: (数字)9781728183244

ISBN: (纸本)9781728183251

There is a demand in the automotive industry to connect two CAN-based subsystems. The commercial CAN-CAN gateway supports basic message forwarding with no real-time behavior. To address this issue, a new gateway architecture is described, on which we present a novel worst-case latency analysis. Specifically, we bound the arrival of the messages at the gateway, which is then used by the Pointer Reachability Exploration (PRE) to derive the interfering message jobs. Our analysis computes a safe gateway latency tighter than the conventional one applied in CAN. Furthermore, we propose a Targeted Priority Assignment (TPA) algorithm that targets at the priorities assigned at the CAN bus and runs a reordering at the gateway to enhance the schedulability. TPA performs better than DMPO (Deadline Monotonic Priority Ordering), while OPA (Audsley's Optimal Priority Assignment) cannot be applied in this context. Evaluation over real-life and scalable CAN message sets is conducted. The reported analysis and priority assignment algorithm are developed for dynamic use to improve the acceptance ratio and can also be deployed statically to provide timing guarantees. This work can be easily extended to support multiple CAN subsystems.

关键词： Heuristic algorithms Prototypes Computer architecture Logic gates Real-time systems Timing Vehicle dynamics

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System-Level Energy-Aware Design Methodology Towards End-To-End Response Time Optimization

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2019年

作者： Xie, Guoqi Huang, Jing Li, Yan Liu Renfa Li, Keqin Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan 410082 China. College of Computer Science and Electronic Engineering Hunan University China 410082 China and also is with the Department of Computer Science State University of New York New Paltz New York 12561 USA.

Energy provision is known and limited for battery-constrained/powered systems (e.g., mobile phones, electric cars, robots, and wireless sensor networks). System-level energy-aware design methodologies have been proposed to control energy usage for energy provision guarantee, and to optimize the end-to-end response time for high-performance in heterogeneous energy-aware systems, where energy pre-assignment strategies are considered as their main contributions. However, when pre-assigning energy to tasks, these strategies either are overly pessimistic or adopt an average manner without a customizable approach to specific tasks. In this paper, we propose an effective algorithm called ratio-based energy pre-assignment (REP) to be customizable to split the available energy according to the energy ratios of individual tasks. Experiments confirm that REP outperforms the state-of-the-art three algorithms of the same type. After REP is used, we further propose a new innovation algorithm called energy surplus reduction (ESR) to reduce the execution time of small partial tasks, thereby optimizing the end-to-end response time within its energy provision. Experiments confirm ESR can reduce the energy usages of small partial tasks to achieve sufficient response time optimization. IEEE

关键词： Wireless sensor networks

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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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Monocular Camera-Based Visual Truck Type Recognition

SSRN

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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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