交替极永磁(consequent pole permanent magnet,CPPM)电机每对极下的气隙磁密不对称,在特定极槽配合下其反电动势(electromotive force,EMF)中存在2、4次等偶次谐波分量,引起额外的转矩脉动,降低转矩输出品质。为解决上述问题,提出一种...交替极永磁(consequent pole permanent magnet,CPPM)电机每对极下的气隙磁密不对称,在特定极槽配合下其反电动势(electromotive force,EMF)中存在2、4次等偶次谐波分量,引起额外的转矩脉动,降低转矩输出品质。为解决上述问题,提出一种注入多谐波电流产生的转矩补偿原有转矩脉动的控制策略。推导适用于任意次谐波磁链产生的转矩脉动通用解析模型;并基于此模型,给出利用谐波电流抑制转矩脉动的理论依据;提出在同步旋转坐标系下注入多次谐波电流的方法,抑制由2、4、5、7、11、13次谐波反电势引起的3、6、12次转矩脉动;并利用准-比例谐振控制器实现谐波电流的精确跟踪。最后,以一台三相9槽10极交替极永磁电机为例,通过不同工况下的转矩脉动抑制实验,验证所提控制策略的有效性。展开更多
Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from li...Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.展开更多
文摘交替极永磁(consequent pole permanent magnet,CPPM)电机每对极下的气隙磁密不对称,在特定极槽配合下其反电动势(electromotive force,EMF)中存在2、4次等偶次谐波分量,引起额外的转矩脉动,降低转矩输出品质。为解决上述问题,提出一种注入多谐波电流产生的转矩补偿原有转矩脉动的控制策略。推导适用于任意次谐波磁链产生的转矩脉动通用解析模型;并基于此模型,给出利用谐波电流抑制转矩脉动的理论依据;提出在同步旋转坐标系下注入多次谐波电流的方法,抑制由2、4、5、7、11、13次谐波反电势引起的3、6、12次转矩脉动;并利用准-比例谐振控制器实现谐波电流的精确跟踪。最后,以一台三相9槽10极交替极永磁电机为例,通过不同工况下的转矩脉动抑制实验,验证所提控制策略的有效性。
文摘Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.
