Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reli...Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.展开更多
This study applies cloud computing combining the theory of learning hierarchy to the digital learning assessment and remedial instruction, and explores the learning effectiveness on the learning model of self-learning...This study applies cloud computing combining the theory of learning hierarchy to the digital learning assessment and remedial instruction, and explores the learning effectiveness on the learning model of self-learning in Chinese idioms. In this study, 60 students in grades three to six of an Elementary School in Nantou County of Taiwan participated in the research experiments. The results of this study reveal that the learning on Chinese idioms through the cloud-based E-learning assessment and remedial tutoring system is superior to the traditional reading to learn. The learning achievements of the students learning Chinese idioms by the proposed system are significantly improved. Therefore, the cloud-based E-learning teaching materials, methods of assessment, and remedial instruction are worth to develop and research.展开更多
Based on the related theories and research results of learning behavioral engagement,this study constructed an evaluation framework of learning behavioral engagement in live teaching,which included 24 indicators in th...Based on the related theories and research results of learning behavioral engagement,this study constructed an evaluation framework of learning behavioral engagement in live teaching,which included 24 indicators in three dimensions:compliance with norms,learning participation and social participation.A small-class live English learning for younger students on the ClassIn was taken as a case study program.Five younger students attended this English learning course of 16 lessons totaling 950 minutes.The preset indicators were preliminarily examined based on the teaching records and the recorded course data.Then,experts in the field of educational technology were invited to develop the learning behavioral engagement dimensions and indicator weightings by using the Analytic Hierarchy Process,and to determine the evaluation indicator system for the evaluation of learning behavioral engagement.Finally,based on this framework,the characteristics of learning behavioral engagement of the case course were analyzed,and the influences of students’individual factors,teaching and environmental factors on learning behavioral engagement in live teaching were investigated.展开更多
The decision of traffic congestion degree is an important research topic today.In severe traffic jams,the speed of the car is slow,and the speed estimate is very inaccurate.This paper first uses the data collected by ...The decision of traffic congestion degree is an important research topic today.In severe traffic jams,the speed of the car is slow,and the speed estimate is very inaccurate.This paper first uses the data collected by Google Maps to reclassify road levels by using analytic hierarchy process.The vehicle speed,road length,normal travel time,traffic volume,and road level are selected as the input features of the limit learning machine,and the delay coefficient is selected.As the limit learning machine as the output value,10-fold cross-validation is used.Compared with the traditional neural network,it is found that the training speed of the limit learning machine is 10 times that of the traditional neural network,and the mean square error is 0.8 times that of the traditional neural network.The stability of the model Significantly higher than traditional neural networks.Finally,the delay coefficient predicted by the extreme learning machine and the normal travel time are combined with the knowledge of queuing theory to finally predict the delay time.展开更多
基金supported by Science and Technology Project of China Southern Power Grid Company Limited under Grant Number 036000KK52200058(GDKJXM20202001).
文摘Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.
文摘This study applies cloud computing combining the theory of learning hierarchy to the digital learning assessment and remedial instruction, and explores the learning effectiveness on the learning model of self-learning in Chinese idioms. In this study, 60 students in grades three to six of an Elementary School in Nantou County of Taiwan participated in the research experiments. The results of this study reveal that the learning on Chinese idioms through the cloud-based E-learning assessment and remedial tutoring system is superior to the traditional reading to learn. The learning achievements of the students learning Chinese idioms by the proposed system are significantly improved. Therefore, the cloud-based E-learning teaching materials, methods of assessment, and remedial instruction are worth to develop and research.
基金This article results from Year 2019 project“Online Learning Engagement Analysis Technology and Evaluation Model Based on Three-Layer Space Multidimensional Time Features”(Project No.:61977011)sponsored by National Natural Science Foundation of China(NSFC)+1 种基金from Year 2019 standard pre-research project“Online Course Elements and Evaluation Indicators Based on National Distance Education Public Service System”(Project No.:CELTS-201902)funded by China e-Learning Technology Standardization Committee(CELTSC).
文摘Based on the related theories and research results of learning behavioral engagement,this study constructed an evaluation framework of learning behavioral engagement in live teaching,which included 24 indicators in three dimensions:compliance with norms,learning participation and social participation.A small-class live English learning for younger students on the ClassIn was taken as a case study program.Five younger students attended this English learning course of 16 lessons totaling 950 minutes.The preset indicators were preliminarily examined based on the teaching records and the recorded course data.Then,experts in the field of educational technology were invited to develop the learning behavioral engagement dimensions and indicator weightings by using the Analytic Hierarchy Process,and to determine the evaluation indicator system for the evaluation of learning behavioral engagement.Finally,based on this framework,the characteristics of learning behavioral engagement of the case course were analyzed,and the influences of students’individual factors,teaching and environmental factors on learning behavioral engagement in live teaching were investigated.
文摘The decision of traffic congestion degree is an important research topic today.In severe traffic jams,the speed of the car is slow,and the speed estimate is very inaccurate.This paper first uses the data collected by Google Maps to reclassify road levels by using analytic hierarchy process.The vehicle speed,road length,normal travel time,traffic volume,and road level are selected as the input features of the limit learning machine,and the delay coefficient is selected.As the limit learning machine as the output value,10-fold cross-validation is used.Compared with the traditional neural network,it is found that the training speed of the limit learning machine is 10 times that of the traditional neural network,and the mean square error is 0.8 times that of the traditional neural network.The stability of the model Significantly higher than traditional neural networks.Finally,the delay coefficient predicted by the extreme learning machine and the normal travel time are combined with the knowledge of queuing theory to finally predict the delay time.
