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All Journal Mechatronics, Electrical Power, and Vehicular Technology International Journal of Artificial Intelligence and Robotics (IJAIR)
Parman, Setyamartana
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Application of empirical mode decomposition method for characterization of random vibration signals Parman, Setyamartana; Yazid, Edwar
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 7, No 1 (2016)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.549 KB) | DOI: 10.14203/j.mev.2016.v7.21-26

Abstract

Characterization of finite measured signals is a great of importance in dynamical modeling and system identification. This paper addresses an approach for characterization of measured random vibration signals where the approach rests on a method called empirical mode decomposition (EMD). The applicability of proposed approach is tested in one numerical and experimental data from a structural system, namely spar platform. The results are three main signal components, comprising: noise embedded in the measured signal as the first component, first intrinsic mode function (IMF) called as the wave frequency response (WFR) as the second component and second IMF called as the low frequency response (LFR) as the third component while the residue is the trend. Band-pass filter (BPF) method is taken as benchmark for the results obtained from EMD method.
Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization Machmudah, Affiani; Parman, Setyamartana
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 1 (2021): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.198 KB) | DOI: 10.25139/ijair.v3i1.3821

Abstract

A collision-free route is very important for achieving sustainability in a manufacturing process and vehicle robot trajectories that commonly operate in a hazardous environment surrounded by obstacles. This paper presents a collision avoidance algorithm using a Bezier curve as a route path. The route planning is modeled as an optimization problem with the objective optimization is to minimize the route length considering an avoiding collision constraint. The collision-avoidance algorithm based on curve point analysis is developed incorporating metaheuristic optimizations, namely a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO). In the collision avoidance algorithm, checking of curve point's position is important to evaluate the individual fitness value. The curve points are analyzed in such a way so that only the paths which are outside the obstacle area are selected. In this case, besides the minimum length as a fitness function, the constraint is the position of curve points from an obstacle. With the help of meta-heuristic optimization, the developed collision avoidance algorithm has been applied successfully to different types of obstacle geometries. The optimization problem is converted to the maximization problem so that the highest fitness value is used to measure the performance of the GA and GWO. In general, results show that the GWO outperforms the GA, where it exhibits the highest fitness value. However, the GA has shown better performance for the narrow passage problem than that of the GWO. Thus, for future research, implementing the hybrid technique combining the GA and the GWO to solve the advanced path planning is essential.
Co-Authors Affiani Machmudah Edwar Yazid