Assessing LAPAN-A3 Satellite with Line Imager Space Application (LISA) Sensor for Oil Spill Detection

Afgatiani, Pingkan Mayestika; Ibrahim, Andi; Hartuti, Maryani; Anggari, Ega Asti; Herawan, Agus; Hakim, Patria Rachman; Parwati, Ety

doi:10.18517/ijaseit.12.6.16076

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…The computer installs the Matlab/NN tool software, installs the ANN/MLP algorithm, performs network training and will receive input data from the platform, then executes the learned algorithm, and makes predictive decisions for the machine. computer so that the PLC controller makes handling situations earlier [36].…”

Section: B Building a Neural Network Using Matlab/nntoolmentioning

confidence: 99%

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Khanh

Thao

2023

JOIV : Int. J. Inform. Visualization

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Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.

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Section: B Building a Neural Network Using Matlab/nntoolmentioning

confidence: 99%