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JACIII Vol.27 No.1 pp. 3-11
doi: 10.20965/jaciii.2023.p0003
(2023)

Research Paper:

SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence

R-Jay S. Relano*,†, Kate G. Francisco*, Ronnie S. Concepcion II*, Mike Louie C. Enriquez*, Jonah Jahara G. Baun**, Adrian Genevie G. Janairo**, Ryan Rhay P. Vicerra*, Argel A. Bandala**, and Elmer P. Dadios*

*Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

Received:
April 4, 2022
Accepted:
June 2, 2022
Published:
January 20, 2023
Keywords:
computational intelligence, feed forward neural network, genetic programming, regression tree, subterranean imaging
Abstract
SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence

SpeedX: The smart speed controller model

Land surveying has been one of the core operations in performing underground imaging. It is known that dynamic and continuous resistivity readings were employed through this technique using the array of capacitive electrodes being towed with a light vehicle. However, the main challenge in doing subsurface surveying is the change in speed of the system when there are inevitable obstacles and sloping road surfaces. To address it, this study will develop prediction models using different computational intelligence such as multigene symbolic regression genetic programming (MSRGP), regression-based decision tree (RTree), and feed forward neural network (FFNN) that will result in a smart speed controller system that can maintain the constant speed of the towed subterranean system. The best performing prediction model will be considered as the SpeedX. The expected output is a correction factor that will signal the speed controller in slow down or inclined plane road environment to maintain a constant speed of 1.6667 m/s for avoidance of data distortion on land surveying. Thus, the MSEs for MSRGP, FFNN, and RTree are 0.00163, 0.00178, and 0.00240, respectively. This results in MSRGP as the best performing model and was considered as the SpeedX model. Other evaluation metrics were employed such as the MAE and R2 which signify the advantage of SpeedX. Furthermore, the comparison between the CI-controlled and uncontrolled towed subterranean imaging trailer system, as well as its advantages clearly highlight the advantage of embedded SpeedX in the system.

Cite this article as:
R. Relano, K. Francisco, R. II, M. Enriquez, J. Baun, A. Janairo, R. Vicerra, A. Bandala, and E. Dadios, “SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.1, pp. 3-11, 2023.
Data files:
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Last updated on Feb. 01, 2023