To manage and orchestrate Network Slices (NSs) for 5G Core (5GC), the MANO (MANagement and Orchestration) framework is proposed by European Telecommunications Standard Institute (ETSI). In most research testbeds, MANO...To manage and orchestrate Network Slices (NSs) for 5G Core (5GC), the MANO (MANagement and Orchestration) framework is proposed by European Telecommunications Standard Institute (ETSI). In most research testbeds, MANO systems such as Tacker, OSM and ONAP are used to initiate network slices. However, this doesn’t comply with the 3GPP 5G standards as MANO should only be responsible for dynamic management of NSs, and the static management such as provisioning or unprovisioning a network slice should be left to OSS/BSS (Operation/Business Support System). Thus, in our testbed, an integrated architecture was designed in which the management of network slices will be coordinated by both MANO and OSS/BSS. MANO would handle on-boarding, instantiating, scaling and terminating of network slices while OSS/BSS is responsible for static management of slices including provisioning and unprovisioning of network slices. To evaluate our system, it was compared with the management systems equipped with only OSS/BSS or MANO in order to analyze the shortfalls of those systems when used to deploy network slices. Through this analysis, this research confirms the necessity of applying both OSS/BSS and MANO for the coordinated management of 5G core slices as adopted by 3GPP.展开更多
The Mano River is a transboundary river that runs through C<span style="color:#4F4F4F;font-family:-apple-system, "font-size:16px;white-space:normal;background-color:#FFFFFF;">ô</span&g...The Mano River is a transboundary river that runs through C<span style="color:#4F4F4F;font-family:-apple-system, "font-size:16px;white-space:normal;background-color:#FFFFFF;">ô</span>te d’Ivoire, Liberia, Guinea, and Sierra Leone. According to a 2018 United Nation report on Sustainable Development Goal (SDG) 6.5, which aims to improve the adoption of cohesive water resource management by 2030;the impact of this goal on the Mano River management is minimal. The research summarizes previous studies using an integrative literature review method, drawing general conclusions and identifying unsolved problems with respect to water resources management. The research finding demonstrated that existing water stress and poor management as exacerbated by socioeconomic practices in the region are the major threats to achieve SDG 6.5. As a result of these activities, long-term management of the river basins has become extremely difficult. The research informs a discussion to achieve cooperative water resource management, which is best achieved by shared collaboration and shared interests as described by SDG 6.5.展开更多
基于视觉的手部位姿估计技术应用于诸多领域,具备着广泛的国际应用市场前景和巨大发展潜力。然而,手部自身存在检测目标过小、手指高自由度以及手部自遮挡等问题。通过对目前存在的难点分析,将手部位姿估计任务分为手部检测和手部关键...基于视觉的手部位姿估计技术应用于诸多领域,具备着广泛的国际应用市场前景和巨大发展潜力。然而,手部自身存在检测目标过小、手指高自由度以及手部自遮挡等问题。通过对目前存在的难点分析,将手部位姿估计任务分为手部检测和手部关键点检测,提出基于改进的Faster R-CNN的手部位姿估计方法。首先提出基于改进的Faster R-CNN手部检测网络,将传统Faster R-CNN网络中的对ROI(regional of interest)的最大值池化,更改为ROI Align,并增加损失函数用于区分左右手。在此基础上增加了头网络分支用以训练输出MANO(hand model with articulated and non-rigid deformations)手部模型的姿态参数和形状参数,得到手部关键点三维坐标,最终得到手部的三维位姿估计结果。实验表明,手部检测结果中存在的自遮挡和尺度问题得到了解决,并且检测结果的准确性有所提高,本文手部检测算法准确率为85%,比传统Faster R-CNN算法提升13%。手部关键点提取算法在MSRA、ICVL、NYU三个数据集分别取得关键点坐标的均方误差值(key-point mean square error,KMSE)为7.50、6.32、8.50的结果。展开更多
文摘To manage and orchestrate Network Slices (NSs) for 5G Core (5GC), the MANO (MANagement and Orchestration) framework is proposed by European Telecommunications Standard Institute (ETSI). In most research testbeds, MANO systems such as Tacker, OSM and ONAP are used to initiate network slices. However, this doesn’t comply with the 3GPP 5G standards as MANO should only be responsible for dynamic management of NSs, and the static management such as provisioning or unprovisioning a network slice should be left to OSS/BSS (Operation/Business Support System). Thus, in our testbed, an integrated architecture was designed in which the management of network slices will be coordinated by both MANO and OSS/BSS. MANO would handle on-boarding, instantiating, scaling and terminating of network slices while OSS/BSS is responsible for static management of slices including provisioning and unprovisioning of network slices. To evaluate our system, it was compared with the management systems equipped with only OSS/BSS or MANO in order to analyze the shortfalls of those systems when used to deploy network slices. Through this analysis, this research confirms the necessity of applying both OSS/BSS and MANO for the coordinated management of 5G core slices as adopted by 3GPP.
文摘The Mano River is a transboundary river that runs through C<span style="color:#4F4F4F;font-family:-apple-system, "font-size:16px;white-space:normal;background-color:#FFFFFF;">ô</span>te d’Ivoire, Liberia, Guinea, and Sierra Leone. According to a 2018 United Nation report on Sustainable Development Goal (SDG) 6.5, which aims to improve the adoption of cohesive water resource management by 2030;the impact of this goal on the Mano River management is minimal. The research summarizes previous studies using an integrative literature review method, drawing general conclusions and identifying unsolved problems with respect to water resources management. The research finding demonstrated that existing water stress and poor management as exacerbated by socioeconomic practices in the region are the major threats to achieve SDG 6.5. As a result of these activities, long-term management of the river basins has become extremely difficult. The research informs a discussion to achieve cooperative water resource management, which is best achieved by shared collaboration and shared interests as described by SDG 6.5.
文摘基于视觉的手部位姿估计技术应用于诸多领域,具备着广泛的国际应用市场前景和巨大发展潜力。然而,手部自身存在检测目标过小、手指高自由度以及手部自遮挡等问题。通过对目前存在的难点分析,将手部位姿估计任务分为手部检测和手部关键点检测,提出基于改进的Faster R-CNN的手部位姿估计方法。首先提出基于改进的Faster R-CNN手部检测网络,将传统Faster R-CNN网络中的对ROI(regional of interest)的最大值池化,更改为ROI Align,并增加损失函数用于区分左右手。在此基础上增加了头网络分支用以训练输出MANO(hand model with articulated and non-rigid deformations)手部模型的姿态参数和形状参数,得到手部关键点三维坐标,最终得到手部的三维位姿估计结果。实验表明,手部检测结果中存在的自遮挡和尺度问题得到了解决,并且检测结果的准确性有所提高,本文手部检测算法准确率为85%,比传统Faster R-CNN算法提升13%。手部关键点提取算法在MSRA、ICVL、NYU三个数据集分别取得关键点坐标的均方误差值(key-point mean square error,KMSE)为7.50、6.32、8.50的结果。
