scholarly journals Kinematic models evaluation of shoulder complex during the badminton overhead forehand smash task in various speed

2021 ◽  
pp. 100697
Author(s):  
Hamidreza Barnamehei ◽  
Farhad Tabatabai Ghomsheh ◽  
Afsaneh Safar Cherati ◽  
Majid Pouladian
2021 ◽  
Vol 502 (3) ◽  
pp. 3294-3311
Author(s):  
Yuanming Wang ◽  
Artem Tuntsov ◽  
Tara Murphy ◽  
Emil Lenc ◽  
Mark Walker ◽  
...  

ABSTRACT We present the results from an Australian Square Kilometre Array Pathfinder search for radio variables on timescales of hours. We conducted an untargeted search over a 30 deg2 field, with multiple 10-h observations separated by days to months, at a central frequency of 945 MHz. We discovered six rapid scintillators from 15-min model-subtracted images with sensitivity of $\sim\! 200\, \mu$Jy/beam; two of them are extreme intra-hour variables with modulation indices up to $\sim 40{{\ \rm per\ cent}}$ and timescales as short as tens of minutes. Five of the variables are in a linear arrangement on the sky with angular width ∼1 arcmin and length ∼2 degrees, revealing the existence of a huge plasma filament in front of them. We derived kinematic models of this plasma from the annual modulation of the scintillation rate of our sources, and we estimated its likely physical properties: a distance of ∼4 pc and length of ∼0.1 pc. The characteristics we observe for the scattering screen are incompatible with published suggestions for the origin of intra-hour variability leading us to propose a new picture in which the underlying phenomenon is a cold tidal stream. This is the first time that multiple scintillators have been detected behind the same plasma screen, giving direct insight into the geometry of the scattering medium responsible for enhanced scintillation.


2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Sung-Jun Pang ◽  
Kyung-Sun Ahn ◽  
Seog Goo Kang ◽  
Jung-Kwon Oh

AbstractIn this study, the lateral resistances of mass timber shear walls were investigated for seismic design. The lateral resistances were predicted by kinematic models with mechanical properties of connectors, and compared with experimental data. Four out of 7 shear wall specimens consisted of a single Ply-lam panel and withdrawal-type connectors. Three out of 7 shear wall specimens consisted of two panels made by dividing a single panel in half. The divided panels were connected by 2 or 4 connectors like a single panel before being divided. The applied vertical load was 0, 24, or 120 kN, and the number of connectors for connecting the Ply-lam wall-to-floor was 2 or 4. As a result, the tested data were 6.3 to 52.7% higher than the predicted value by kinematic models, and it means that the lateral resistance can be designed by the behavior of the connector, and the prediction will be safe. The effects of wall-to-wall connectors, wall-to-floor connectors and vertical loads on the shear wall were analyzed with the experimental data.


2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110105
Author(s):  
Jnana Sai Abhishek Varma Gokaraju ◽  
Weon Keun Song ◽  
Min-Ho Ka ◽  
Somyot Kaitwanidvilai

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3744
Author(s):  
Rizuwana Parween ◽  
M. A. Viraj J. Muthugala ◽  
Manuel V. Heredia ◽  
Karthikeyan Elangovan ◽  
Mohan Rajesh Elara

The inspection and maintenance of drains with varying heights necessitates a drain mapping robot with trained labour to maintain community hygiene and prevent the spread of diseases. For adapting to level changes and navigating in the narrow confined environments of drains, we developed a self-configurable hybrid robot, named Tarantula-II. The platform is a quadruped robot with hybrid locomotion and the ability to reconfigure to achieve variable height and width. It has four legs, and each leg is made of linear actuators and modular rolling wheel mechanisms with bi-directional movement. The platform has a fuzzy logic system for collision avoidance of the side wall in the drain environment. During level shifting, the platform achieves stability by using the pitch angle as the feedback from the inertial measuring unit (IMU) mounted on the platform. This feedback helps to adjust the accurate height of the platform. In this paper, we describe the detailed mechanical design and system architecture, kinematic models, control architecture, and stability of the platform. We deployed the platform both in a lab setting and in a real-time drain environment to demonstrate the wall collision avoidance, stability, and level shifting capabilities of the platform.


2017 ◽  
Vol 107 ◽  
pp. 20-33 ◽  
Author(s):  
Lin Chen ◽  
Jiwei Hu ◽  
Dinghui Yang ◽  
Haibin Song ◽  
Zhenhua Wang

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