Research on the Role of Hybrid Mesh Warm-up in Flow Prediction Based on Deep Learning

2021 ◽  
Author(s):  
Xiaoyu Wang ◽  
Chuanfu Xu ◽  
Xiang Gao ◽  
Wenqiang Li ◽  
Dong Zhu
Author(s):  
Судоргин Е.П. ◽  
Карсакова И.Н.

Аннотация: О роли разминки перед тренировкой и соревнованиями написано много научных статьей и диссертаций. Ещё больше о значимости физической подготовки спортсменов, в том числе и шахматистов. В то же время авторы считают, что вопросу физической подготовки шахматистов и в частности разминке в научной и научно-методической литературе уделяется недостаточно внимания. В своей статье авторы приводят собственные экспериментальные данные о влиянии физических упражнений (разминки) на умственную работоспособность студентов-шахматистов БГУ и как следствие на спортивные результаты команды. Ключевые слова: Шахматы, разминка, умственная работоспособность, средства и методы разминки, методы оценки результатов. Аннотация: Машыгуунун жана мелдештердин алдында даярдоонун ролу жөнүндө көптөгөн илимий макала жана диссертация жазылган. Ошондой эле илимий жана илимий-методикалык адабияттарда шахматка даярдоого көп көңүл бурулбай жатат. Макаланын авторлору шахмат ойногон студенттерге физикалык көнүгүүлөрдүн тийгизген таасири жөнүндө өздөрүнүн эксперименталдык маалыматтарын көргөзүштү. Түйүндүү сөздөр: Шахмат, акыл-дарамет, курулуштар жана ыкмалар, баа берүү жыйынтыгы боюнча кабыл алынат. Abstract: on the role of warm-up before training and competitions written many scientific articles and theses. More about the significance of the physical preparation of athletes, including players. At the same time, the authors believe that the issue of fit- ness players and in particular workout in scientific and scientific-methodical literature neglected. In his article the authors cite their own experimental data on the influence of physical exercises (warm-up) on the mental fitness of students-BSU players and as a result the sport performance team. Keywords: chess, warm-up, mental fitness, workout tools and methods, methods of evaluation results.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.


Author(s):  
Mahmood Alzubaidi ◽  
Haider Dhia Zubaydi ◽  
Ali Bin-Salem ◽  
Alaa A Abd-Alrazaq ◽  
Arfan Ahmed ◽  
...  

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