Indonesia Conference Directory

Corresponding Author
Achwan Restu Prayoga

Institutions
a) Faculty of Engineering
Universitas Sebelas Maret
Surakarta, Indonesia
*achwanrestu[at]yahoo.com
b) National Center for Sustaniable Transportaion Technology (NCSTT), Bandung, Indonesia

Abstract
Vehicles are the most important thing to use by human and to make it safe to use, all vehicle need a safe and reliable braking system, the use of frictional brake can raise the probability of braking failure because of high pressure and temperature operation, to make braking safer, there is a new, alternative braking system called Eddy-Current Brake (ECB) that uses magnet in their braking process. This paper aims to know the influence between the shapes of conductor-s face on braking torque using finite element method, using aluminum with mid-iron in one construction to improve the braking torque produced by conductor. Validation was done before starting FEM calculation to achieve accurate FEM settings, the modeling uses ANSYS Electronics Desktop. The shapes used on conductor-s face are sawtooth, half-circle, and square. The highest braking torque performance on these variables are 15.39213, 16.40432, and 14.25 Nm respectively at their critical speed with a magnetic flux of 0.8 – 2 Tesla at all variables.

Keywords
eddy-current brake, brakes, electromagnet

Topic
EV System and Integration

Link: https://ifory.id/abstract/4vrETHZKJbAP

Corresponding Author
Bernard Renardi

Institutions
Institut Teknologi Bandung (ITB)

Abstract
The development of road infrastructures, especially in developing countries such as Indonesia, continues significantly in the last decade. Along with the acceleration of development that has resulted in 406.14 kilometers of new roads in the last 5 years, an updated road map is needed as rapid as the growth of these roads proportionally. To solve this problem, commonly used technology takes three years to update the map and still unable to map the entire road, especially those that can only be passed by smaller vehicles. In this paper, an alternative mapping technique using multi-quadrotor system is introduced for updating the road map effectively. Multi-quadrotor system allows that the terrain mapping can be larger compared to single quadrotor system. The designed system is an integration of two Robot Operating System (ROS) packages as the framework for the software development, which is ardrone_autonomy and tum_ardrone. The multi-quadrotor will take images of contour using a 720p front camera with frequency of 2 – 4 Hz while flying. The images taken with a required specification will be processed into a new map of the area using Agisoft Photoscan. This new map will be processed by a YOLO-based object detection algorithm for specific object identification purpose. Real-time experimental results using two AR.Drone 2.0 showed that successful image recognition was obtained with high resolution images of map.

Keywords
multi-quadrotor, robot operating system, image recognition, AR. Drone 2.0, YOLO

Topic
Control System

Link: https://ifory.id/abstract/GNJqUkFcdRyB

Corresponding Author
Imam Boni Mores

Institutions
a)Instrumentation and Control Research Group,
Department of Engineering Physics, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132, Indonesia

b)National Center for Sustainable Transportation Technology, CRCS Building, 2nd floor, Jl. Ganesha 10, Bandung 40132, Indonesia

*yul[at]tf.itb.ac.id

Abstract
Particle Swarm Optimization (PSO) and Brain Storm Optimization (BSO) are alternative methods to find out the optimized solution of a non-linear equation. This paper will discuss the application of both methods to find out the weight of neurons from Adaptive Neuro Fuzzy Inference Systems (ANFIS) technique, which is used in predicting the bus arrival time at the bus stop. Comparison of the performance from both methods will also be made. After the modeling, training and testing of the proposed algorithm, the RMSE value produced from ANFIS which was trained by the PSO testing was 0.8145, and if it was trained by BSO was 0.8352. These results also conclude that the ANFIS with PSO algorithm yields better predicting bus arrival time better rather than ANFIS BSO in this case

Keywords
Adaptive Neuro-fuzzy Inference System, bus arrival time, Brain Storm Optimization, Particle Swarm Optimization

Topic
Control System

Link: https://ifory.id/abstract/dvpUFA3KcTwB