目的:分析儿童体质测试成绩与粗大动作技能发展之间的关系,对不同儿童进行有针对性的指导和帮助。方法:北京市4所幼儿园共1 928名儿童,按照《国民体质测定标准手册》(幼儿部分)进行体质测试。随机整群选取了8个班作为整群对照(CG,n=244)...目的:分析儿童体质测试成绩与粗大动作技能发展之间的关系,对不同儿童进行有针对性的指导和帮助。方法:北京市4所幼儿园共1 928名儿童,按照《国民体质测定标准手册》(幼儿部分)进行体质测试。随机整群选取了8个班作为整群对照(CG,n=244),并根据体质测试成绩,选取成绩前10%的儿童(TG,n=203)和后10%儿童(BG,n=203),采用Test of Gross Motor Development-3(TGMD-3)对儿童粗大动作发展进行评估,对移动类和球类技能两大类基本动作的动作技能进行评估。结果:1)男童移动动作分值与女童无明显差别;2)男童球类动作技能好于女童,且随年龄增加其优势更加明显;3)体质测试成绩差的儿童,动作发展低于整体水平,特别是其球类动作技能的发展;4)随着年龄的增长,儿童动作发展的差距随年龄增长有逐渐扩大的趋势;5)粗大动作发展与体质测试结果中等相关。结论:针对3~6岁儿童,促进其体质发展,必须重视儿童动作技能的发展,对动作技能发展滞后的儿童应在教学活动中给予更多关注和指导。展开更多
Ionospheric delay error is considered to be one of the most prominent factors impacting the Global Navigation Satellite Systems(GNSS) positioning and navigation accuracies. Due to dispersive nature and anisotropic of ...Ionospheric delay error is considered to be one of the most prominent factors impacting the Global Navigation Satellite Systems(GNSS) positioning and navigation accuracies. Due to dispersive nature and anisotropic of the ionosphere above certain regions, the positioning accuracy is seriously affected when using a precision-limited model. In this paper, an attempt has been taken to estimate ionosphere-delays based on Planar Fit(PF) and Spherical Harmonic Function(SHF) models by applying the commonly used single layer Model(SLM) and an extended single layer model(ESLM) which has been explored sparsely over the region. The results show that ESLM of PF and SHF techniques performed better in estimating ionospheric delay compared to the existing SLM model. Although the performance of the ESLM approach is almost comparable to the SLM results during the quiet ionospheric conditions, the ESLM-PF and ESLMSHF models led to respective improvements of 4.66% and 7.14% over the classically used SLM model under the disturbed ionospheric conditions. In view of the uneven variability of equatorial/low latitude ionosphere above the Indian subcontinental region, the suitability of ESLM-PF and ESLM-SHF models has been emphasized and suggested for assessing its completeness and reliableness across other parts of the globe. The output of this work may be useful for high precession GNSS positioning through mitigating the ionospheric delays under quiet as well as varied ionospheric conditions across the low/equatorial latitude regions.展开更多
Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unsc...Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unscented KF(UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF(BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature(being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle's movement. Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.展开更多
文摘目的:分析儿童体质测试成绩与粗大动作技能发展之间的关系,对不同儿童进行有针对性的指导和帮助。方法:北京市4所幼儿园共1 928名儿童,按照《国民体质测定标准手册》(幼儿部分)进行体质测试。随机整群选取了8个班作为整群对照(CG,n=244),并根据体质测试成绩,选取成绩前10%的儿童(TG,n=203)和后10%儿童(BG,n=203),采用Test of Gross Motor Development-3(TGMD-3)对儿童粗大动作发展进行评估,对移动类和球类技能两大类基本动作的动作技能进行评估。结果:1)男童移动动作分值与女童无明显差别;2)男童球类动作技能好于女童,且随年龄增加其优势更加明显;3)体质测试成绩差的儿童,动作发展低于整体水平,特别是其球类动作技能的发展;4)随着年龄的增长,儿童动作发展的差距随年龄增长有逐渐扩大的趋势;5)粗大动作发展与体质测试结果中等相关。结论:针对3~6岁儿童,促进其体质发展,必须重视儿童动作技能的发展,对动作技能发展滞后的儿童应在教学活动中给予更多关注和指导。
文摘Ionospheric delay error is considered to be one of the most prominent factors impacting the Global Navigation Satellite Systems(GNSS) positioning and navigation accuracies. Due to dispersive nature and anisotropic of the ionosphere above certain regions, the positioning accuracy is seriously affected when using a precision-limited model. In this paper, an attempt has been taken to estimate ionosphere-delays based on Planar Fit(PF) and Spherical Harmonic Function(SHF) models by applying the commonly used single layer Model(SLM) and an extended single layer model(ESLM) which has been explored sparsely over the region. The results show that ESLM of PF and SHF techniques performed better in estimating ionospheric delay compared to the existing SLM model. Although the performance of the ESLM approach is almost comparable to the SLM results during the quiet ionospheric conditions, the ESLM-PF and ESLMSHF models led to respective improvements of 4.66% and 7.14% over the classically used SLM model under the disturbed ionospheric conditions. In view of the uneven variability of equatorial/low latitude ionosphere above the Indian subcontinental region, the suitability of ESLM-PF and ESLM-SHF models has been emphasized and suggested for assessing its completeness and reliableness across other parts of the globe. The output of this work may be useful for high precession GNSS positioning through mitigating the ionospheric delays under quiet as well as varied ionospheric conditions across the low/equatorial latitude regions.
文摘Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unscented KF(UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF(BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature(being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle's movement. Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.
