Ronnie Concepcion

Bio: Ronnie Concepcion is an academic researcher from De La Salle University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 10, co-authored 63 publications receiving 203 citations. Previous affiliations of Ronnie Concepcion include Mapúa Institute of Technology & University of Perpetual Help System DALTA.

Adaptive Neuro-Fuzzy Inference System on Aquaphotomics Development for Aquaponic Water Nutrient Assessments and Analyses

Abstract: Water quality monitoring, assessment, and analysis in an aquaponics system are vital procedures in maintaining a productive and an efficient ecosystem for cultivars being cultured. However, these require labor-intensive, long-standing, and high-priced laboratory methods, as water quality and its nutrients are dependent on micro-biological and physio-chemical variables. To reduce the use of costly sensors and the time consumed for expensive calculations, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based aquaphotomics approach is conducted on this study. Water samples were collected from a pond water of an aquaponics system (AP) where species of fish are cultivated. The samples went through aquaphotomics with the aid of spectrophotometer and was applied on to near infrared, visible light, and ultraviolet (NIR-Vis-UV) spectrum with wavelength range of 100 to 1000 nm. Spectrometry was utilized to determine three significant nutrient compounds which are the nitrate, phosphate, and potassium. Temperature, power of hydrogen (pH), and electrical conductivity sensors (EC) were used simultaneously to serve as data attributes in predicting the three compounds assigned as targets. Feature selection algorithms such as Minimum Redundancy Maximum Relevance (MRMR) and Univariate Feature Ranking for Regression Using F-Tests (UFT) were used to determine the two most significant predictor relative to a specific target. Results showed that MRMR with ANFIS is best used for predicting Phosphate with R2 value of 0.8284. The UFT with ANFIS produced the best performance for regressing Nitrate and Potassium with R2 values of 0.9321 and 0.9961 respectively.

Estimation of Photosynthetic Growth Signature at the Canopy Scale Using New Genetic Algorithm-Modified Visible Band Triangular Greenness Index

Abstract: Greenness index has been proven sensitive to vegetation properties for multispectral and hyperspectral imaging. However, most controlled microclimatic cultivation chambers are equipped with low-cost RGB camera for crop growth monitoring. The lack of camera credentials specially the wavelength sensitivity of visible band provides added challenge in materializing greenness index. The proposed method in this study compensates the unavailability of generic camera peak wavelength sensitivities by employing genetic algorithm (GA) to derive a visible band triangular greenness index (TGI) based on green waveband signal normalized TGI model called gvTGI. The selection, mutation and crossover rates used in configuring the GA model are 0.2, 0.01 and 0.8 respectively. Lettuce images are captured from an aquaponic cultivation chamber for 6-week crop life cycle. The annotated and extracted gvTGI channels are inputted to deep learning models of MobileNetV2, ResNetl01 and InceptionResNetV2 for estimation of photosynthetic growth signatures at canopy scale. In predicting cultivation period in weeks after germination, MobileNetV2 bested other image classification models with accuracy of 80.56%. In estimating canopy area, MobileNetV2 bested other image regression models with $\mathrm{R}^{2}$ of 0.9805. The proposed gvTGI proved to be highly accurate on estimation of photosynthetic growth signatures by using generic RGB camera, thus, providing a low-cost alternative for crop phenotyping.

Optimization of Subsurface Imaging Antenna Capacitance through Geometry Modeling using Archimedes, Lichtenberg and Henry Gas Solubility Metaheuristics

Abstract: Capacitive resistivity subsurface imaging of roads operating at very low frequency is susceptible to antenna characteristic capacitance dynamics that may cause unwanted signal reflection, coupling, and unfavorable effect on reception sensitivity. Antennas are conventionally modeled using a complex and repetitive default mathematical method that is prone to human error and discrete results. To address this emerging challenge, this study has developed a new technique for plate-wire antenna capacitance optimization through equatorial dipole-dipole antenna geometry modeling using genetic programming (GP) integrated with metaheuristic methods, namely Archimedes optimization algorithm (AOA), Lichtenberg algorithm (LA), and Henry gas solubility optimization (HGSO). GP was used to construct the antenna capacitance fitness function based on 241 combinations of wire antenna radius and elevation, and dipole plate elevation, length, width, and thickness measurements. Minimization of antenna capacitance (approaching 1 nF) to achieve quasi-static condition was performed using GP-AOA, GP-LA, and GP-HGSO. The 3 metaheuristic-based antennas were 3D-modeled using Altair Feko and compared from the default antenna’s electrical features. It was found that even with the smallest dipole geometry, hybrid GP-LA antenna model exhibited the most practical outputs at 5 kHz with correct directional propagation based on its radiation pattern, a realistic receiver voltage of -8.86 dBV which is close to the default model, and a high-power efficiency of 99.925%. While hybrid GP-AOA and GP-HGSO resulted in indirect coupled transceiver systems with unsuitable antenna characteristic capacitance inducing anomalous receiver voltages. The experimental results prove the validity of the developed technique for more accurate determination of optimal antenna geometry.

The Technology Adoption and Governance of Artificial Intelligence in the Philippines

Abstract: Artificial intelligence is primed to disrupt our society and the industry. The AI trend of technological singularity is continuously accelerating and is being employed to the different facets of humanity from education, medicine, business, engineering, arts and the like. Government and private companies have been hooked up with this fast pacing technology. AI may displace some non-digital jobs that performs heavy load and repetitive tasks, but it certainly augments labor shortage by realigning the workforce competitiveness to what the technology requires. The diffusion of AI technology is necessary for mental shift of the government and industry leaders to adopt the technology. Research and development is very promising to uplift mankind to faster productivity and positively affect the industries in international perspective. The Philippines is still coping up with the adoption of AI system, but it can steer up globally by strengthening the technology governance of strictly implementing the policies with measures the PDP 2017-2022 and its HNRDA.

Plant Disease Recognition Model Based on Improved YOLOv5

Abstract: To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

An Overview on Optimal Flocking

Abstract: The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature focusing on optimization approaches to achieve flocking behavior that provide strong safety guarantees. We separate the literature into cluster and line flocking, and categorize cluster flocking with respect to the system objective, which may be realized by a reactive, or planning, control algorithm. We also present several approaches aimed at minimizing flocking communication and computational requirements in real systems via neighbor filtering and event-driven planning. We conclude the overview with our perspective on the outlook and future research direction of optimal flocking algorithms.

Comprehensive study on applications of artificial neural network in food process modeling

Abstract: Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ...

Analog layout synthesis - Recent advances in topological approaches

Abstract: This paper gives an overview of some recent advances in topological approaches to analog layout synthesis and in layout-aware analog sizing. The core issue in these approaches is the modeling of layout constraints for an efficient exploration process. This includes fast checking of constraint compliance, reducing the search space, and quickly relating topological encodings to placements. Sequence-pairs, B*-trees, circuit hierarchy and layout templates are described as advantageous means to tackle these tasks.