This study applies augmented reality(AR)to design an experiment to explore the academic performance,motivation and cognitive load level of students in distance education when leaning about communications engineering c...This study applies augmented reality(AR)to design an experiment to explore the academic performance,motivation and cognitive load level of students in distance education when leaning about communications engineering courses.The samples were 62 distance education students,consisting of 42 part-time adult students from a school of network education and 20 full-time students form a university,all of whom studied remotely in the experimental course.The results show that augmented reality can help distance education students improve their academic performance.As for learning motivation,the enhancement effect is not significant.The study also found that the use of AR did not increase students'cognitive load as long as the guidance was appropriate.展开更多
With the development of the Low Earth Orbit(LEO)communication constellations,it has become a hot area of research to provide additional navigation augmentation services.Limited by volume,weight,power consumption,and r...With the development of the Low Earth Orbit(LEO)communication constellations,it has become a hot area of research to provide additional navigation augmentation services.Limited by volume,weight,power consumption,and running time,the in-flight performance of navigation augmentation payload remains to be investigated.In this paper,we analyze the data quality of on-board GNSS observation and evaluate the precision of short-arc dynamic Precise Orbit Determination(POD)performance based on the WangTong-01(WT01)mission.Furthermore,the downlink navigation measurement data of WT01 satellites are analyzed and compared with the GNSS observations.The results show that the average multipath errors of the WT01 on-board GPS L1,L2 and BeiDou Satellite Navigation System(BDS)B1,B2 code observation are 0.54,0.74,0.65,and 0.58 m,respectively.The short-arc dynamic POD three-dimensional(3D)overlapping accuracy is 7.1 cm.The average multipath errors of downlink navigation signal Z1 and Z2 are 0.81 and 0.80 m,respectively,which at the same order of magnitude as GNSS signals.The maximum Carrier-to-Noise Ratio(C/N0)value of WT01 downlink measurement data can reach 60 dB Hz,which is much stronger than GNSS and indicates the navigation signals of LEO satellites can meet the basic requirement of navigation augmentation.展开更多
The main aim of an educational institute is to offer high-quality education to students. The system to achieve better quality in the educational system is to find the knowledge from educational data and to discover th...The main aim of an educational institute is to offer high-quality education to students. The system to achieve better quality in the educational system is to find the knowledge from educational data and to discover the attributes that manipulate the performance of students. Student performance prediction is a major issue in education and training, specifically in the educational data mining system. This research presents the student performance prediction approach with the MapReduce framework based on the proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network. The proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network is derived by integrating fractional calculus with competitive multi-verse optimization. The MapReduce framework is designed with the mapper and the reducer phase to perform the student performance prediction mechanism with the deep learning classifier. The input data is partitioned at the mapper phase to perform the data transformation process, and thereby the features are selected using the distance measure. The selected unique features are employed for the data segmentation process, and thereafter the prediction strategy is accomplished at the reducer phase by the deep neuro-fuzzy network classifier. The proposed method obtained the performance in terms of mean square error, root mean square error and mean absolute error with the values of 0.338 3, 0.581 7, and 0.391 5, respectively.展开更多
This article examines the main variables that influence the intention to use Augmented Reality(AR)applications in the tourism sector in Jordan.The study model has been constructed based on the unified theory of accept...This article examines the main variables that influence the intention to use Augmented Reality(AR)applications in the tourism sector in Jordan.The study model has been constructed based on the unified theory of acceptance and the use of technology2(UTAUT2),by incorporating a new construct(aesthetics)to explore the usage intention of Mobile Augmented Reality in Tourism(MART).A questionnaire was used and distributed to a sample of 450 participants.Data were analyzed using the Smart PLS version 3.0.for testing 12 hypotheses.29 measurement items were carefully reviewed based on previous studies that were selected to assess the research hypotheses.The findings revealed that the proposed model elucidates 35.7%of the variance in the users’intention to use MART.The results also showed that both performance expectancy and aesthetics were found to be the most significant factors at level(0.001).Four variables,respectively,were at level(0.01)which consisted of social influence,facilitating conditions,hedonic motivation,and price value.The weakest effect was effort expectancy at level(0.05).As the use of AR has become important for tourists,this study establishes a research base that can be built upon for future researchers.MART developers can benefit from the results of this research to design and deliver this service successfully and to ensure that its adoption by users is achieved.展开更多
The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO...The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.展开更多
从导航增强与弹性定位、导航和授时(Positioning,Navigation and Timing,PNT)两个方向,系统阐述基于低轨星座的PNT性能提升能力,包括全球天基监测、全球准实时高精度、可信认证、弹性应急备份等。综合国内外低轨星座发展现状,分析了低...从导航增强与弹性定位、导航和授时(Positioning,Navigation and Timing,PNT)两个方向,系统阐述基于低轨星座的PNT性能提升能力,包括全球天基监测、全球准实时高精度、可信认证、弹性应急备份等。综合国内外低轨星座发展现状,分析了低轨星座实现PNT性能提升的两种实现途径:一是在导航频段播发信号实现性能提升,可与现有各全球卫星导航系统(Global Navigation Satellite System,GNSS)实现良好的兼容与互操作性能;二是利用通信频段播发导航通信(导通)融合信号,实现与现有导航频段信号的弹性应急备份。二者均可实现定位精度及其收敛时间、完好性、安全性等性能提升,但因信号频段和业务类型差异,两者的技术体制、实现能力和代价有所不同。为此,文章从星载接收与处理技术、导航增强和导通融合信号体制设计、同时同频收发干扰抑制技术、弹性终端融合接收处理技术等方面,分析了实现导航增强和弹性PNT服务的关键技术,最后给出了基于低轨星座的PNT性能提升的发展建议。文章的研究成果,有助于进一步探索低轨星座的潜力,促进全球卫星导航系统的发展,并为低轨PNT性能提升技术的研究和应用提供参考。展开更多
文摘This study applies augmented reality(AR)to design an experiment to explore the academic performance,motivation and cognitive load level of students in distance education when leaning about communications engineering courses.The samples were 62 distance education students,consisting of 42 part-time adult students from a school of network education and 20 full-time students form a university,all of whom studied remotely in the experimental course.The results show that augmented reality can help distance education students improve their academic performance.As for learning motivation,the enhancement effect is not significant.The study also found that the use of AR did not increase students'cognitive load as long as the guidance was appropriate.
基金the National Key Research and Development Program of China[grant numbers 2017YFB0503402,2019YFC1511504].
文摘With the development of the Low Earth Orbit(LEO)communication constellations,it has become a hot area of research to provide additional navigation augmentation services.Limited by volume,weight,power consumption,and running time,the in-flight performance of navigation augmentation payload remains to be investigated.In this paper,we analyze the data quality of on-board GNSS observation and evaluate the precision of short-arc dynamic Precise Orbit Determination(POD)performance based on the WangTong-01(WT01)mission.Furthermore,the downlink navigation measurement data of WT01 satellites are analyzed and compared with the GNSS observations.The results show that the average multipath errors of the WT01 on-board GPS L1,L2 and BeiDou Satellite Navigation System(BDS)B1,B2 code observation are 0.54,0.74,0.65,and 0.58 m,respectively.The short-arc dynamic POD three-dimensional(3D)overlapping accuracy is 7.1 cm.The average multipath errors of downlink navigation signal Z1 and Z2 are 0.81 and 0.80 m,respectively,which at the same order of magnitude as GNSS signals.The maximum Carrier-to-Noise Ratio(C/N0)value of WT01 downlink measurement data can reach 60 dB Hz,which is much stronger than GNSS and indicates the navigation signals of LEO satellites can meet the basic requirement of navigation augmentation.
文摘The main aim of an educational institute is to offer high-quality education to students. The system to achieve better quality in the educational system is to find the knowledge from educational data and to discover the attributes that manipulate the performance of students. Student performance prediction is a major issue in education and training, specifically in the educational data mining system. This research presents the student performance prediction approach with the MapReduce framework based on the proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network. The proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network is derived by integrating fractional calculus with competitive multi-verse optimization. The MapReduce framework is designed with the mapper and the reducer phase to perform the student performance prediction mechanism with the deep learning classifier. The input data is partitioned at the mapper phase to perform the data transformation process, and thereby the features are selected using the distance measure. The selected unique features are employed for the data segmentation process, and thereafter the prediction strategy is accomplished at the reducer phase by the deep neuro-fuzzy network classifier. The proposed method obtained the performance in terms of mean square error, root mean square error and mean absolute error with the values of 0.338 3, 0.581 7, and 0.391 5, respectively.
文摘This article examines the main variables that influence the intention to use Augmented Reality(AR)applications in the tourism sector in Jordan.The study model has been constructed based on the unified theory of acceptance and the use of technology2(UTAUT2),by incorporating a new construct(aesthetics)to explore the usage intention of Mobile Augmented Reality in Tourism(MART).A questionnaire was used and distributed to a sample of 450 participants.Data were analyzed using the Smart PLS version 3.0.for testing 12 hypotheses.29 measurement items were carefully reviewed based on previous studies that were selected to assess the research hypotheses.The findings revealed that the proposed model elucidates 35.7%of the variance in the users’intention to use MART.The results also showed that both performance expectancy and aesthetics were found to be the most significant factors at level(0.001).Four variables,respectively,were at level(0.01)which consisted of social influence,facilitating conditions,hedonic motivation,and price value.The weakest effect was effort expectancy at level(0.05).As the use of AR has become important for tourists,this study establishes a research base that can be built upon for future researchers.MART developers can benefit from the results of this research to design and deliver this service successfully and to ensure that its adoption by users is achieved.
基金financial support extended for this academic work by the Beijing Natural Science Foundation(Grant 2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212).
文摘The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.
