Dario Amaya

Prediction of Solar Radiation through the Anfis Algorithm

The constant evolution of the energy industry, has introduced the need for ongoing research studies about climate change due to its direct action on the production of alternative energies. Thus, they have focused on developing predictive algorithms in order to resolve, in an early way, the climate action on each point of energy production. In the development of this work, the ANFIS algorithm and information from the NASA Langley research center virtual database were implemented. They being oriented to the analysis and prediction of solar radiation over the geographic area of the Nueva Granada Military University campus, Cajicá, Colombia, with the purpose of making appropriate use of the power generating system located in the zone. The development of such systems, would allow the early identification of solar radiation that can be present in different geographical areas of Colombia, in order to provide the necessary power to cover the electricity demand required in each region, achieving as results an approximation error less than 1%.

Control structure for a car-like robot using artificial neural networks and genetic algorithms

The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work.

FragariaVesca Dehydration by flat solar collector with natural flow in Bogota

Actually optimization concepts in agricultural process are try to involve with major presence over this economical sector worldwide. This, due to use in an efficient way the resources since the cultivation to post-harvest. The last one, provide large amounts of food which are lost due to accelerated maturity and overproduction. In order to minimize the lost and supply the growing demand of natural and health products, process with a low nutritional impact are implemented to preserve the food on the time, solar dehydration is a trend which involve natural resources as primary source in the process related. In this work is presented the results obtained in FragariaVesca dehydration, known as strawberry by a flat solar collector working under natural circulation in Bogota.

MODEL AND SIMULATION OF A HOTAIR DEHYDRATION SYSTEM OF FRUITS

Abstract The most used practice for food preservation is the dehydration, in particular, the heat-air dehydration which is theoldest method used nowadays. In this work, a mathematical model was developed to describe this behavior, always working in the linear region of the process of dehydration. The model proposed was implemented in MATLAB software with the Simulink tool and evaluated with a simulation CFD in SolidWorks. The behavior of the analytical model compared with the CFD simulation, showed a high variation for the values of final moisture in the food. This is due to the initial restrictions made for the model, especially, assume that the temperature inside the chamber is homogeneous. The model allows to vary all the features of the system and identify the behavior during the process.

Design and Implementation of an Algorithm for Selecting Mangifera Indica Crop Fruits Using Machine Vision and Artificial Intelligence

The use of technology in terms of improving the production processes of the agro-industry, is increasing significantly. This paper presents the results of the design of an algorithm based on image processing and artificial intelligence for segmenting fruits of Mangifera Indica, with the purpose to establish the shape, the degree of maturity based on color and to estimate the fruit size. This work can be used as a support tool for the agriculturists, for the management of harvest in the crops. Algorithm results allow to determine the condition of the fruit in the harvesting process, helping agriculturist to improve crop productivity.