Elmer P. Dadios

By Elmer P. Dadios, Jazper Jan C. Biliran, Ron-Ron G. Garcia, D. Johnson, and Adranne Rachel B. Valencia

Part of the book: Fuzzy Logic

By Elmer P. Dadios, Ryan Rhay Vicerra, Sandy Lauguico, Argel Bandala, Ronnie Concepcion II and Edwin Sybingco

It has been predicted by the United Nations that the world population will increase to 9.8 billion in 2050. This causes agricultural development areas to be transformed into urban areas. This urbanization and increase in population density cause food insecurity. Urban agriculture using precision farming becomes a feasible solution to meet the growing demand for food and space. An adaptive management system (AMS) is necessary for such farm to provide an artificial environment suitable to produce cultivars effectively. This research proposes the development of a computational intelligence-based urban farm automation and control system utilizing machine learning and fuzzy logic system models. A quality assessment is employed for adjusting the environmental parameters with respect to the cultivars’ requirements. The system is composed of sensors for data acquisition and actuators for model-dictated responses to stimuli. Data logging was done wirelessly through a router that would collect and monitor data through a cloud-based dashboard. The model intended for training from the acquired data undergo statistical comparative analysis and least computational cost analysis to optimize the performance. The system performance was evaluated by monitoring the conditions of the sensors and actuators. Experiment results showed that the proposed system is accurate, robust, and reliable.

Part of the book: Automation and Control

By Elmer Dadios, Jonah Jahara Baun, Mike Louie Enriquez, Adrian Genevie Janairo, Ronnie Concepcion II, Joseph Aristotle De Leon, Kate Francisco, Andres Philip Mayol, Argel Bandala and Ryan Rhay Vicerra

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.

Part of the book: Advances in Fuzzy Logic Systems