This paper describes a novel,system-level design methodology based on a genetic algorithm(GA)using freeform geometries for microelectromechanical systems(MEMS)devices.The proposed method can concurrently design and co...This paper describes a novel,system-level design methodology based on a genetic algorithm(GA)using freeform geometries for microelectromechanical systems(MEMS)devices.The proposed method can concurrently design and co-optimize the electronic and mechanical parts of a MEMS device comprising freeform geometries to achieve a better system performance,i.e.,a high sensitivity,a good system stability,and large fabrication tolerances.Also,the introduction of freeform geometries allows higher degrees of freedom in the design process,improving the diversity and potentially the performance of the MEMS devices.A MEMS accelerometer comprising a freeform mechanical motion preamplifier in a closed-loop control system is presented to demonstrate the effectiveness of the design approach.The optimization process shows the main figure-of-merit(FOM)is improved by 195%.In the mechanical component alone(open-loop system),the product of sensitivity and bandwidth has improved by 151%,with sensitivity increasing by 276%.For closed-loop performance,there is an improvement of 120%for the ratio of open and closed-loop displacements.The product of sensitivity and bandwidth is improved by 27%in the closed-loop system.Excellent immunities to fabrication errors and parameter mismatch are achieved.Experiments show that the displacement of the MEMS accelerometer in the closed-loop system decreased by 86%with 4.85 V feedback voltage compared with that in the open-loop system under a 1 g 100 Hz acceleration input.The static and dynamic nonlinearities in the closed-loop system are improved by 64%and 61%,respectively,compared with those in the open-loop system,in the±1 g acceleration input range.Besides,the closed-loop system improves the cross-axis sensitivity by 18.43%,compared with that in the open-loop system.It is the first time a closed-loop system for a MEMS accelerometer comprising a mechanical motion preamplifier is successfully implemented experimentally.展开更多
随着大学生就业创业需求的日益增加,传统的多目标算法无法根据各学生群体特征给出个性化推荐。因此,为给大学生提供个性化的就业和创业推荐,研究基于遗传算法的改进OR-树算法(Genetic Algorithm-based MORA, GA-MORA),设计出面向学生群...随着大学生就业创业需求的日益增加,传统的多目标算法无法根据各学生群体特征给出个性化推荐。因此,为给大学生提供个性化的就业和创业推荐,研究基于遗传算法的改进OR-树算法(Genetic Algorithm-based MORA, GA-MORA),设计出面向学生群体的就业创业个性化推荐模型。该模型通过模拟生物进化过程来寻找最优解,最终生成个性化的推荐结果。结果可知,通过对GA-MORA算法在就业创业平台推荐中的性能评估,发现该算法在多样性等指标上表现出色。此外,研究还发现不同学生群体对职业偏好的程度受个人兴趣、专业属性、区域熟悉度和经济因素等多种因素影响。女性学生群体的区域熟悉度指标为0.8,比男性更为集中,可知女性群体在就业时更易选择在更熟悉的地方就业。综上可知,此次研究的算法模型优越,有利于为大学生就业创业提供一个可靠的方案。展开更多
基金supported by The Science and Technology Development Fund,Macao SAR(FDCT),004/2023/SKLThe Science and Technology Development Fund,Macao SAR(FDCT),0087/2023/ITP2.
文摘This paper describes a novel,system-level design methodology based on a genetic algorithm(GA)using freeform geometries for microelectromechanical systems(MEMS)devices.The proposed method can concurrently design and co-optimize the electronic and mechanical parts of a MEMS device comprising freeform geometries to achieve a better system performance,i.e.,a high sensitivity,a good system stability,and large fabrication tolerances.Also,the introduction of freeform geometries allows higher degrees of freedom in the design process,improving the diversity and potentially the performance of the MEMS devices.A MEMS accelerometer comprising a freeform mechanical motion preamplifier in a closed-loop control system is presented to demonstrate the effectiveness of the design approach.The optimization process shows the main figure-of-merit(FOM)is improved by 195%.In the mechanical component alone(open-loop system),the product of sensitivity and bandwidth has improved by 151%,with sensitivity increasing by 276%.For closed-loop performance,there is an improvement of 120%for the ratio of open and closed-loop displacements.The product of sensitivity and bandwidth is improved by 27%in the closed-loop system.Excellent immunities to fabrication errors and parameter mismatch are achieved.Experiments show that the displacement of the MEMS accelerometer in the closed-loop system decreased by 86%with 4.85 V feedback voltage compared with that in the open-loop system under a 1 g 100 Hz acceleration input.The static and dynamic nonlinearities in the closed-loop system are improved by 64%and 61%,respectively,compared with those in the open-loop system,in the±1 g acceleration input range.Besides,the closed-loop system improves the cross-axis sensitivity by 18.43%,compared with that in the open-loop system.It is the first time a closed-loop system for a MEMS accelerometer comprising a mechanical motion preamplifier is successfully implemented experimentally.
文摘随着大学生就业创业需求的日益增加,传统的多目标算法无法根据各学生群体特征给出个性化推荐。因此,为给大学生提供个性化的就业和创业推荐,研究基于遗传算法的改进OR-树算法(Genetic Algorithm-based MORA, GA-MORA),设计出面向学生群体的就业创业个性化推荐模型。该模型通过模拟生物进化过程来寻找最优解,最终生成个性化的推荐结果。结果可知,通过对GA-MORA算法在就业创业平台推荐中的性能评估,发现该算法在多样性等指标上表现出色。此外,研究还发现不同学生群体对职业偏好的程度受个人兴趣、专业属性、区域熟悉度和经济因素等多种因素影响。女性学生群体的区域熟悉度指标为0.8,比男性更为集中,可知女性群体在就业时更易选择在更熟悉的地方就业。综上可知,此次研究的算法模型优越,有利于为大学生就业创业提供一个可靠的方案。
