This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features ...This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.展开更多
目的探讨精神分裂症患者认知损害与阳性和阴性症状量表(positive and negative syndrome scale,PANSS) 5因子模型之间的关系。方法选取首发或者复发未治疗住院的精神分裂症患者130名和85名健康对照,采用精神分裂症认知功能成套测验中文...目的探讨精神分裂症患者认知损害与阳性和阴性症状量表(positive and negative syndrome scale,PANSS) 5因子模型之间的关系。方法选取首发或者复发未治疗住院的精神分裂症患者130名和85名健康对照,采用精神分裂症认知功能成套测验中文版(MATRICS consensus cognitive battery,MCCB)评估2组的认知功能,PANSS量表5因子模型评估患者的精神症状。结果患者组在MCCB测量的连线、符号编码、言语流畅、霍普金斯词语学习、空间广度、空间记忆、迷宫及情绪管理分测验评分均低于对照组( P <0.001);患者组在MCCB评估的认知维度与PANSS 5因子模型的关联性分析中,认知损害因子与信息处理加工速度、言语学习、推理及问题解决、社会认知能力均呈负相关( P <0.050),而其他因子与MCCB评估的认知维度均无相关。结论精神分裂症患者认知功能不同程度受损,其中信息处理加工速度受损程度更为严重;精神分裂症患者认知功能与阴性症状是相互独立的症状群,在患者治疗过程中,需要制定不同的方案。展开更多
基金This work is supported by the National Natural Science Foundation of China(12002218)the Youth Foundation of Education Department of Liaoning Province(JYT19034).These supports are gratefully acknowledged.
文摘This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.
文摘目的探讨精神分裂症患者认知损害与阳性和阴性症状量表(positive and negative syndrome scale,PANSS) 5因子模型之间的关系。方法选取首发或者复发未治疗住院的精神分裂症患者130名和85名健康对照,采用精神分裂症认知功能成套测验中文版(MATRICS consensus cognitive battery,MCCB)评估2组的认知功能,PANSS量表5因子模型评估患者的精神症状。结果患者组在MCCB测量的连线、符号编码、言语流畅、霍普金斯词语学习、空间广度、空间记忆、迷宫及情绪管理分测验评分均低于对照组( P <0.001);患者组在MCCB评估的认知维度与PANSS 5因子模型的关联性分析中,认知损害因子与信息处理加工速度、言语学习、推理及问题解决、社会认知能力均呈负相关( P <0.050),而其他因子与MCCB评估的认知维度均无相关。结论精神分裂症患者认知功能不同程度受损,其中信息处理加工速度受损程度更为严重;精神分裂症患者认知功能与阴性症状是相互独立的症状群,在患者治疗过程中,需要制定不同的方案。
