Systematic Analysis and Proposed AI-based Technique for Attenuating Inductive and Capacitive Parasitics in Low and Very Low Frequency Antennas

Francisco, Kate; Relano, R-Jay; Enriquez, Mike Louie; Concepcion, Ronnie; Baun, Jonah Jahara; Janairo, Adrian Genevie; Vicerra, Ryan Rhay P.; Dadios, Elmer P.; Dungca, Jonathan R.

doi:10.1109/iemtronics55184.2022.9795784

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…This has been a well-known strategy since it has a nondestructive approach to detecting underground objects and structures, reducing the pre-construction process's adverse effects [2]. Specifically, a capacitive resistivity underground imaging system is composed of a configured transmitter and receiver antennas that are capacitively coupled in the ground [4], [5] to measure the potential difference in resistivity readings [6]. The transmitter emits low-frequency electromagnetic waves into the ground and the multi-receiver systems will detect the reflected signals generated by a conductive anomaly [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Jahara Baun,

Genevie Janairo,

Concepcion II

et al. 2024

IJCDS

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Real-time monitoring of output electrical parameters of the transmitted signals in a capacitive resistivity underground imaging system is necessary because these are significant in the calculation of underground resistivity, however, machine learning has not yet been applied in this application to improve the accuracy of measurement. This study aims to develop and select the best prediction models that can be implemented for a digital measuring unit suitable for capacitive resistivity underground imaging. Three deep neural network models namely Elman recurrent neural network (ERNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were explored to build prediction models for the current and voltage of the transmitter circuit. The prediction models' performance was assessed using mean squared error (MSE), which is reduced to its absolute lowest value. The result shows that the best-trained models for current and voltage prediction are the ERNN models with configurations of 900-600-500 hidden neurons network with training MSE of 9.82 X 10 -9 and the configured 1300-1000-900 hidden neurons with training MSE of 0.465, respectively. With the help of the prediction models, it would be possible to measure current and voltage output accurately, allowing simultaneous data acquisition while avoiding the need for a separate measuring device.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Jahara Baun,

Genevie Janairo,

Concepcion II

et al. 2024

IJCDS

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“…This method has various applications such as detecting minerals, finding underground materials and faults, and detecting voids [1]. One of the techniques used in underground imaging is the capacitive resistivity (CR) method [1][2][3], where the ground-coupled transmitter and receiver antennas are designed and configured in a capacitive connection to measure resistivity by determining the potential difference [4][5][6]. The design of a capacitive resistivity imaging system can be either a single-pair antenna system that includes a transmitter and a receiver unit, or a multiple-antenna system with one transmitter and multiple receiver units [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Dadios,

Jahara Baun,

Louie Enriquez

et al. 2023

Advances in Fuzzy Logic Systems

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This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correct the latitude and longitude of Global Positioning System (GPS) used in locating towed vehicle system for underground imaging. The input used was the collected data from a developed Real-time Kinematic Global Positioning System sensor integrated with Inertial Measurement Unit. Different ANFIS models were developed and evaluated. For latitude correction, ANFIS model with hybrid optimization trained at 300 epochs was chosen, whereas for longitude correction, ANFIS model with hybrid optimization trained at 100 epochs was selected. Both models achieved the lowest Mean Squared Error (MSE), the highest Coefficient of Determination (R2), and lowest Mean Absolute Error (MAE). Moreover, selected best ANFIS models were compared to Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) models, but the results showed that the ANFIS models have superior performances. The selected ANFIS models were verified by testing on the collected actual dataset and the visualized map demonstrated that the corrected GPS latitude and longitude have significantly reduced error, indicating that the fuzzy system with neural network capabilities is a cost-effective and convenient method for error reduction in vehicle localization making it applicable to be integrated for capacitive resistivity underground imaging systems.

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Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming

Leon

Enriquez

Concepcion

et al. 2022

2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environ

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Systematic Analysis and Proposed AI-based Technique for Attenuating Inductive and Capacitive Parasitics in Low and Very Low Frequency Antennas

Cited by 8 publications

References 30 publications

Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming

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