Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index ...Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.展开更多
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individua...To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.展开更多
A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rat...A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.展开更多
随着电子设备的广泛应用,印刷电路板(Printed Circuit Board,PCB)在电子制造行业中具有重要意义.然而,由于制造过程中的不完美和环境因素的干扰,PCB上可能存在微小的缺陷.因此,开发高效准确的缺陷检测算法对于确保产品质量至关重要.针对...随着电子设备的广泛应用,印刷电路板(Printed Circuit Board,PCB)在电子制造行业中具有重要意义.然而,由于制造过程中的不完美和环境因素的干扰,PCB上可能存在微小的缺陷.因此,开发高效准确的缺陷检测算法对于确保产品质量至关重要.针对PCB微小缺陷检测问题,本文提出了一种基于多维注意力机制的高精度PCB微小缺陷检测算法.为降低网络的模型参数量和计算量,引入部分卷积(Partial Convolution,PConv),将ELAN(Efficient Layer Aggregation Network)模块设计为更加高效的P-ELAN,同时,为增强网络对微小缺陷的特征提取能力,引入多维注意力机制(Multi-Dimensional Attention Mechanism,MDAM)的全维动态卷积(Omni-dimensional Dynamic Convolution,ODConv)并结合部分卷积,设计了POD-CSP(Partial ODconv-Cross Stage Partial)和POD-MP(Partial ODconv-Max Pooling)跨阶段部分网络模块,提出了OD-Neck结构.最后,本文基于(Youo Only Look Once version 7,YOLOv7)提出了对小目标更加高效的YOLO-POD模型,并在训练阶段采用一种新颖的Alpha-SIoU损失函数对网络进行优化.实验结果表明,YOLO-POD的检测精确率和召回率分别达到了98.31%和97.09%,并在多个指标上取得了领先优势,尤其是对于更严格的(mean Average Precision at IoU threshold of 0.75,mAP75)指标,比原始的YOLOv7模型提高28%.验证了YOLO-POD在PCB缺陷检测性能中具有较高的准确性和鲁棒性,满足高精度的检测要求,可为PCB制造行业提供有效的检测解决方案.展开更多
基金Project(61273187)supported by the National Natural Science Foundation of ChinaProject(61321003)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.
基金Project(2013CB733600) supported by the National Basic Research Program of ChinaProject(21176073) supported by the National Natural Science Foundation of China+2 种基金Project(20090074110005) supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-09-0346) supported by Program for New Century Excellent Talents in University of ChinaProject(09SG29) supported by "Shu Guang", China
文摘To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of China
文摘A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
文摘随着电子设备的广泛应用,印刷电路板(Printed Circuit Board,PCB)在电子制造行业中具有重要意义.然而,由于制造过程中的不完美和环境因素的干扰,PCB上可能存在微小的缺陷.因此,开发高效准确的缺陷检测算法对于确保产品质量至关重要.针对PCB微小缺陷检测问题,本文提出了一种基于多维注意力机制的高精度PCB微小缺陷检测算法.为降低网络的模型参数量和计算量,引入部分卷积(Partial Convolution,PConv),将ELAN(Efficient Layer Aggregation Network)模块设计为更加高效的P-ELAN,同时,为增强网络对微小缺陷的特征提取能力,引入多维注意力机制(Multi-Dimensional Attention Mechanism,MDAM)的全维动态卷积(Omni-dimensional Dynamic Convolution,ODConv)并结合部分卷积,设计了POD-CSP(Partial ODconv-Cross Stage Partial)和POD-MP(Partial ODconv-Max Pooling)跨阶段部分网络模块,提出了OD-Neck结构.最后,本文基于(Youo Only Look Once version 7,YOLOv7)提出了对小目标更加高效的YOLO-POD模型,并在训练阶段采用一种新颖的Alpha-SIoU损失函数对网络进行优化.实验结果表明,YOLO-POD的检测精确率和召回率分别达到了98.31%和97.09%,并在多个指标上取得了领先优势,尤其是对于更严格的(mean Average Precision at IoU threshold of 0.75,mAP75)指标,比原始的YOLOv7模型提高28%.验证了YOLO-POD在PCB缺陷检测性能中具有较高的准确性和鲁棒性,满足高精度的检测要求,可为PCB制造行业提供有效的检测解决方案.
