The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource pr...The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.展开更多
针对人工检测电子级玻璃纤维布表面疵点速度慢,效率低,漏检率高等问题,提出应用机器视觉的技术代替人工进行布面疵点检测。首先搭建系统硬件结构采集电子布图像,然后采用高斯差分(Difference of Gaussians,DoG)算法增强图像,最后采用动...针对人工检测电子级玻璃纤维布表面疵点速度慢,效率低,漏检率高等问题,提出应用机器视觉的技术代替人工进行布面疵点检测。首先搭建系统硬件结构采集电子布图像,然后采用高斯差分(Difference of Gaussians,DoG)算法增强图像,最后采用动态阈值分割方法可视化疵点。实验结果表明本系统可对电子布常见12种疵点进行检测,疵点检出率可达95%以上,且实时性良好,最高检测速度达到90 m/min,满足工业生产的实际需求。展开更多
文摘The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.
文摘针对人工检测电子级玻璃纤维布表面疵点速度慢,效率低,漏检率高等问题,提出应用机器视觉的技术代替人工进行布面疵点检测。首先搭建系统硬件结构采集电子布图像,然后采用高斯差分(Difference of Gaussians,DoG)算法增强图像,最后采用动态阈值分割方法可视化疵点。实验结果表明本系统可对电子布常见12种疵点进行检测,疵点检出率可达95%以上,且实时性良好,最高检测速度达到90 m/min,满足工业生产的实际需求。
