This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgama...This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.展开更多
With the development of satellite navigation technology,the user demands for the integrity of Global Navigation Satellite System(GNSS)have increased more and more.A ground-based monitoring system can hardly report an ...With the development of satellite navigation technology,the user demands for the integrity of Global Navigation Satellite System(GNSS)have increased more and more.A ground-based monitoring system can hardly report an alarm message to GNSS users during the valid alarming period due to the satellite-Earth propagation delay.It is beneficial to monitor abnormal events and report the corresponding alarms from orbit.Adopting this approach,which is an important feature for future GNSS integrity monitoring,the time needed to provide an alarm is shorter and the system integrity capability is strengthened.The BeiDou Navigation Satellite System(BDS)new generation satellites have the capabilities of satellite autonomous integrity monitoring(SAIM).This paper presents the technical scheme of SAIM,and proposes the integrity monitoring method of both navigation signals and the clocks onboard.The proposed method was verified through the onboard test on the BDS satellites.In addition,we analyzed the integrity telemetry data from the new generation of BDS satellite,including signal delay,power,carrier phase measurement,correlation peak,consistency of pseudo-code and carrier phase,clock phase and frequency step.The analysis results indicated that the quality of the data on orbit met the requirements,and SAIM could monitor effectively any abnormal change of satellite clocks and navigation signal,generate rapidly an alarm message,and transmit it to the user.The alarm time was less than 6 s through the message,and 2 s through non-standard code(NSC).Finally,we present future opportunities for improving the SAIM technology of BDS.展开更多
文摘This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.
文摘With the development of satellite navigation technology,the user demands for the integrity of Global Navigation Satellite System(GNSS)have increased more and more.A ground-based monitoring system can hardly report an alarm message to GNSS users during the valid alarming period due to the satellite-Earth propagation delay.It is beneficial to monitor abnormal events and report the corresponding alarms from orbit.Adopting this approach,which is an important feature for future GNSS integrity monitoring,the time needed to provide an alarm is shorter and the system integrity capability is strengthened.The BeiDou Navigation Satellite System(BDS)new generation satellites have the capabilities of satellite autonomous integrity monitoring(SAIM).This paper presents the technical scheme of SAIM,and proposes the integrity monitoring method of both navigation signals and the clocks onboard.The proposed method was verified through the onboard test on the BDS satellites.In addition,we analyzed the integrity telemetry data from the new generation of BDS satellite,including signal delay,power,carrier phase measurement,correlation peak,consistency of pseudo-code and carrier phase,clock phase and frequency step.The analysis results indicated that the quality of the data on orbit met the requirements,and SAIM could monitor effectively any abnormal change of satellite clocks and navigation signal,generate rapidly an alarm message,and transmit it to the user.The alarm time was less than 6 s through the message,and 2 s through non-standard code(NSC).Finally,we present future opportunities for improving the SAIM technology of BDS.
