lift movement
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Author(s):  
Hiroshi Furutera ◽  
Shigehisa Kawakami ◽  
Naoki Kodama ◽  
Yosuke Manda ◽  
Keisuke Kitagawa ◽  
...  

2021 ◽  
Vol 3 (1 (111)) ◽  
pp. 70-78
Author(s):  
Аnton Nikiforov ◽  
Аlina Nykyforova ◽  
Roman Antoshchenkov ◽  
Vitalina Antoshchenkova ◽  
Sergey Diundik ◽  
...  

The modern practice of using vibratory machines when working with fine-size light-weight seeds is faced with such an undesirable phenomenon as the impact of aerodynamic forces and moments on the kinematics of vibrational movement of particles of the seed mixture fractions. According to the results of scientific studies devoted to the solution of this problem, only mathematical models of vibrational movement are used, where the aerodynamic factor is taken into account as taking the seeds by airflow. This is typical only for cleaning modes with the rebound of seeds from the vibrating surface. Aerodynamic forces and moments are present in them only as a force of aerodynamic resistance. The action of lateral aerodynamic forces and their moments are not taken into account. Their consideration allows to extend the range of action of the aerodynamic factor on the modes of vibration cleaning (vibroseparation) without rebound (but with sliding and rolling) which are of greater interest in terms of improving the efficiency of processing fine-size seeds. A mathematical model of seed vibration movement taking into account the action of a complete set of aerodynamic forces (dynamic resistance forces and lateral aerodynamic forces) and moments was proposed. This makes it possible to simulate non-lifting modes of vibrational movement of seeds. A system of algebraic equations that are linear with respect to the kinematic parameters of seed movement which was obtained by translating differential equations of movement into a finite-difference form was presented. The possibility of numerical solution of equations of movement by the Euler method was shown. The results of the evaluation of the model adequacy for the processes of vibration separation of tobacco seeds and false flax were presented. As shown by the results of calculations and experiments, the developed model provides an increase in the adequacy of the simulation results by 30 % in comparison with the model where the aerodynamic factor is not taken into account


2020 ◽  
Vol 47 (8) ◽  
pp. 967-976 ◽  
Author(s):  
Yukako Sunada ◽  
Jin Magara ◽  
Takanori Tsujimura ◽  
Kazuhiro Ono ◽  
Makoto Inoue

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 963 ◽  
Author(s):  
Baojun Chen ◽  
Francesco Lanotte ◽  
Lorenzo Grazi ◽  
Nicola Vitiello ◽  
Simona Crea

The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users’ movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class “no lift”) were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement.


Author(s):  
Baojun Chen ◽  
Lorenzo Grazi ◽  
Francesco Lanotte ◽  
Nicola Vitiello ◽  
Simona Crea

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