scholarly journals TTNet: Real-time temporal and spatial video analysis of table tennis

2020 ◽  
Author(s):  
Roman Voeikov ◽  
Nikolay Falaleev ◽  
Ruslan Baikulov
Keyword(s):  
Real Time ◽  
Video Analysis ◽  
Table Tennis ◽  
Temporal And Spatial ◽  
Spatial Video

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

scholarly journals Near real time ETEM streaming video analysis

2017 ◽  
Author(s):  
Yuewei Lin ◽  
Dmitri Zakharov ◽  
Remi Megret ◽  
Shinjae Yoo ◽  
Eric Stach
Keyword(s):  
Real Time ◽  
Video Analysis ◽  
Streaming Video

scholarly journals Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Peng Ge ◽  
Zhixue Liao ◽  
Chang Liu ◽  
Peiyu Ren ◽  
Zhaoxia Guo
Keyword(s):  
Real Time ◽  
Distribution System ◽  
Low Carbon ◽  
The Real ◽  
Scheduling Model ◽  
Scenic Spot ◽  
Full Capacity ◽  
Develop Economy ◽  
Temporal And Spatial ◽  
Demand And Capacity

Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and theωpath of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

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