建模仿真即服务(Modelling Simulation as a service, MSaaS)是将云计算、面向服务的架构等技术引入建模与仿真领域的新思想,其建立的仿真系统的可信度是完成高性能仿真的基础问题与重要指标。分析了建模仿真即服务的分层体系架构,根据...建模仿真即服务(Modelling Simulation as a service, MSaaS)是将云计算、面向服务的架构等技术引入建模与仿真领域的新思想,其建立的仿真系统的可信度是完成高性能仿真的基础问题与重要指标。分析了建模仿真即服务的分层体系架构,根据其架构各层之间的功能不同,运行环境异构等特点建立了可信度评估指标体系,提出了一种定量计算和定性分析相结合的评估方法——熵权-灰色层次分析法,最后给出了基于MSaaS的战场环境仿真可信度评估实例,结果表明上述方法有效。展开更多
Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brai...Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.展开更多
建模仿真即服务(Modeling&Simulation as a Service,MSaaS)具有快速部署、可互操作、高安全性和应用面广等特点,而其可信度直接关系到仿真应用的成败。对此,文章提出一种MSaaS仿真可信度计算方法。根据MSaaS不同层面从数据的完备性...建模仿真即服务(Modeling&Simulation as a Service,MSaaS)具有快速部署、可互操作、高安全性和应用面广等特点,而其可信度直接关系到仿真应用的成败。对此,文章提出一种MSaaS仿真可信度计算方法。根据MSaaS不同层面从数据的完备性、仿真模型精度和正确性等影响因素建立可信度指标体系;采用层次分析法(AHP)得到各级评价指标权重,通过模糊综合评价引入主因素突出型算子得到评估结果;实例验证了方法有效。展开更多
建模与仿真具有极高的应用价值,并且其产品、数据与流程的易于访问性也变得极为重要。然而,为确保分布式仿真系统的互操作性和结果的可信性、一致性,需要花费极大的时间、人力和预算等开销。以有效、高效交付建模与仿真能力为目的,北约...建模与仿真具有极高的应用价值,并且其产品、数据与流程的易于访问性也变得极为重要。然而,为确保分布式仿真系统的互操作性和结果的可信性、一致性,需要花费极大的时间、人力和预算等开销。以有效、高效交付建模与仿真能力为目的,北约建模与仿真组(NMSG)提出建模与仿真即服务(MSaaS,Modelling and Simulation as a Service)概念并设计相应参考架构,支撑按需部署与执行组件化的分布式仿真环境。针对北约建模与仿真即服务的概念内涵、参考架构、工程过程、服务发现等相关内容进行分析研究。展开更多
文摘建模仿真即服务(Modelling Simulation as a service, MSaaS)是将云计算、面向服务的架构等技术引入建模与仿真领域的新思想,其建立的仿真系统的可信度是完成高性能仿真的基础问题与重要指标。分析了建模仿真即服务的分层体系架构,根据其架构各层之间的功能不同,运行环境异构等特点建立了可信度评估指标体系,提出了一种定量计算和定性分析相结合的评估方法——熵权-灰色层次分析法,最后给出了基于MSaaS的战场环境仿真可信度评估实例,结果表明上述方法有效。
基金supported in part by the National Natural Science Foundation of China under Grants 61861007in part by the Guizhou Province Science and Technology Planning Project ZK[2021]303in part by the Guizhou Province Science Technology Support Plan under Grants[2022]264,[2023]096,[2023]412 and[2023]409.
文摘Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.
文摘建模仿真即服务(Modeling&Simulation as a Service,MSaaS)具有快速部署、可互操作、高安全性和应用面广等特点,而其可信度直接关系到仿真应用的成败。对此,文章提出一种MSaaS仿真可信度计算方法。根据MSaaS不同层面从数据的完备性、仿真模型精度和正确性等影响因素建立可信度指标体系;采用层次分析法(AHP)得到各级评价指标权重,通过模糊综合评价引入主因素突出型算子得到评估结果;实例验证了方法有效。
文摘建模与仿真具有极高的应用价值,并且其产品、数据与流程的易于访问性也变得极为重要。然而,为确保分布式仿真系统的互操作性和结果的可信性、一致性,需要花费极大的时间、人力和预算等开销。以有效、高效交付建模与仿真能力为目的,北约建模与仿真组(NMSG)提出建模与仿真即服务(MSaaS,Modelling and Simulation as a Service)概念并设计相应参考架构,支撑按需部署与执行组件化的分布式仿真环境。针对北约建模与仿真即服务的概念内涵、参考架构、工程过程、服务发现等相关内容进行分析研究。
