Autonomous vehicle parking using artificial intelligent approach

Author(s):  
Chen-Kui Lee ◽  
Chun-Liang Lin ◽  
Bing-Min Shiu
2020 ◽  
Vol 1500 ◽  
pp. 012022
Author(s):  
Min-Wen Wang ◽  
Fatahul Arifin ◽  
Jhen-Wei Kuo ◽  
Tzong-Horng Dzwo

Author(s):  
M.P.L. Perera

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.


2020 ◽  
Vol 11 (3) ◽  
pp. 167
Author(s):  
Eko Wahyu Prasetyo ◽  
Nambo Hidetaka ◽  
Dwi Arman Prasetya ◽  
Wahyu Dirgantara ◽  
Hari Fitria Windi

The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.


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