scholarly journals An online learning approach to eliminate Bus Bunching in real-time

2016 ◽  
Vol 47 ◽  
pp. 460-482 ◽  
Author(s):  
Luís Moreira-Matias ◽  
Oded Cats ◽  
João Gama ◽  
João Mendes-Moreira ◽  
Jorge Freire de Sousa
2021 ◽  
Vol 1828 (1) ◽  
pp. 012001
Author(s):  
Yeoh Keng Yik ◽  
Nurul Ezaila Alias ◽  
Yusmeeraz Yusof ◽  
Suhaila Isaak

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402097983
Author(s):  
Abdullah Yasin Gündüz ◽  
Buket Akkoyunlu

The success of the flipped learning approach is directly related to the preparation process through the online learning environment. It is clear that the desired level of academic achievement cannot be reached if the students come to class without completing their assignments. In this study, we investigated the effect of the use of gamification in the online environment of flipped learning to determine whether it will increase interaction data, participation, and achievement. We used a mixed-methods sequential explanatory design, which implies collecting and analyzing quantitative and then qualitative data. In the online learning environment of the experimental group, we used the gamification. However, participants in the control group could not access the game components. According to the findings, the experimental group had higher scores in terms of interaction data, participation, and achievement compared with the control group. Students with low participation can be encouraged to do online activities with gamification techniques.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.


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