高清图像(高分辨率图像)前景遮罩提取问题是图像合成、自动前景提取等图像处理领域的热点难题,其本质是前景背景像素对的大规模组合优化问题,目前相关研究成果较少.本文针对问题维度过高难以直接求解这一问题,设计了基于RGB聚类的多类...高清图像(高分辨率图像)前景遮罩提取问题是图像合成、自动前景提取等图像处理领域的热点难题,其本质是前景背景像素对的大规模组合优化问题,目前相关研究成果较少.本文针对问题维度过高难以直接求解这一问题,设计了基于RGB聚类的多类协同优化策略,以实现决策空间的有效降维;给出协同目标反馈的分组优化策略,通过将协同目标中的最优前景背景像素对作为启发式信息反馈给每个分组,实现大规模组合优化问题的分组协同求解.在分组优化策略的基础上,论文提出了基于分组协同的群体竞争优化算法(competitive swarm optimization algorithm based on group collaboration,GC-CSO),为高维优化问题分析提供了借鉴.为了验证所提方法的有效性,本文选用alpha matting基准数据集作为测试数据,通过与群体竞争优化算法、典型带分组策略的大规模优化算法进行对比分析,验证了:(1)基于RGB聚类的协同优化策略可以显著地降低问题维度;(2) GC-CSO算法提高了高清图像前景遮罩的提取精度.展开更多
This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have ...This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.展开更多
文摘高清图像(高分辨率图像)前景遮罩提取问题是图像合成、自动前景提取等图像处理领域的热点难题,其本质是前景背景像素对的大规模组合优化问题,目前相关研究成果较少.本文针对问题维度过高难以直接求解这一问题,设计了基于RGB聚类的多类协同优化策略,以实现决策空间的有效降维;给出协同目标反馈的分组优化策略,通过将协同目标中的最优前景背景像素对作为启发式信息反馈给每个分组,实现大规模组合优化问题的分组协同求解.在分组优化策略的基础上,论文提出了基于分组协同的群体竞争优化算法(competitive swarm optimization algorithm based on group collaboration,GC-CSO),为高维优化问题分析提供了借鉴.为了验证所提方法的有效性,本文选用alpha matting基准数据集作为测试数据,通过与群体竞争优化算法、典型带分组策略的大规模优化算法进行对比分析,验证了:(1)基于RGB聚类的协同优化策略可以显著地降低问题维度;(2) GC-CSO算法提高了高清图像前景遮罩的提取精度.
文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.
