Eduardo Caicedo

Simplifying the Bacteria Foraging Optimization Algorithm

The Bacterial Foraging Optimization Algorithm is a swarm intelligence technique which models the individual and group foraging policies of the E. Coli bacteria as a distributed optimization process. The algorithm is structurally complex due to its nested loop architecture and includes several parameters whose selection deeply influences the result. This paper presents some modifications to the original algorithm that simplifies the algorithm structure, and the inclusion of best member information into the search strategy, which improves the performance. The results on several benchmarks show reasonable performance in most tests and a considerable improvement in some complex functions. Also, with the use of the T-Test we were able to confirm that the performance enhancement is statistically significant.

Procesamiento de imágenes infrarrojas para la detección de defectos en materiales

El Ensayo Termografico No Destructivo (ETND) es una tecnica de evaluacion de materiales, en la que la superficie de una muestra de material es estimulada tecnicamente para producir una diferencia de temperatura entre las areas no defectuosas y las eventualmente defectuosas. Los cambios de temperatura son registrados mediante una camara infrarroja; posteriormente, dada la distorsion generada por el ruido, se ejecutan etapas de procesamiento para detectar y/o caracterizar los defectos en el material. En este articulo se analizan y comparan experimentalmente varios de estos metodos de procesamiento y se profundiza en la tecnica CAD (Contraste Absoluto Diferencial) modificada por cuadrupolos termicos.

Bacteria Swarm Foraging Optimization for Dynamical Resource Allocation in a Multizone Temperature Experimentation Platform

In this work, an algorithm based on the Bacteria Swarm Foraging Optimization was used for the dynamical resource allocation in a multiple input/output experimentation platform. This platform, which mimics a temperature grid plant, is composed of multiple sensors and actuators organized in zones. The use of the bacteria based algorithm in this application allows the search the best actuators in each sample time. This allowed us to obtain a uniform temperature over the platform. Good behavior of the implemented algorithm in the experimentation platform was observed.

Market-Based Task Allocation in a Multi-robot Surveillance System

This paper presents a market-based cooperation strategy for task allocation in a multi-robot system oriented to surveillance tasks. A global grid-based map is used by the robots in order to share information about recently visited cells, where each cell represents a task to be done. Here the concept of stigmergy is applied, where each cell value represents the concentration of pheromone left by the robots. In order to reduce computational load, groups of tasks are being formed using K-means for the task decomposition phase, and a market-based algorithm is executed in order to assign each task to a robot. The strategy has been evaluated in a simulated environment using up to three robots.

Defect quantification with reference-free thermal contrast and artificial neural networks

The Infrared Nondestructive Testing (IRNT) methods based on thermal contrast are strongly affected by non-uniform heating at the surface. Hence, the results obtained from these methods considerably depend on the chosen reference point. One of these methods is Artificial Neural Networks (ANN) that uses thermal contrast curves as input data for training and test in order to detect and estimate defect depth. The Differential Absolute Contrast (DAC) has been successfully used as an alternative thermal contrast to eliminate the need of a reference point by defining the thermal contrast with respect to an ideal sound area. The DAC technique has been proven effective to inspect materials at early times since it is based on the 1D solution of the Fourier equation. A modified DAC version using thermal quadrupoles explicitly includes the sample thickness in the solution, extending in this way the range of validity when the heat front approaches the sample rear face. We propose to use ANN to detect and quantify defects in composite materials using data extracted from the modified DAC with thermal quadrupoles in order to decrease the non-uniform heating and plate shape impact on the inspection.

Implementation of a distributed control experimentation platform

In this work, we present the implementation of a planar temperature grid plant. This plant emulates the working of a control system that is designed to maintain a constant temperature over a surface. The behavior of the implemented plant presents characteristics difficult to describe in mathematical terms like disturbances, interferences, deviations, and temperature gradients. We describe the functional characteristics of the system and its application in the study of distributed control systems in an educational environment. To test the working of the plant we present the obtained results with two resource allocation strategies such as control algorithms

Adaptive BCI based on software agents

The selection of features is generally the most difficult field to model in BCIs. Therefore, time and effort are invested in individual feature selection prior to data set training. Another great difficulty regarding the model of the BCI topology is the brain signal variability between users. How should this topology be in order to implement a system that can be used by large number of users with an optimal set of features? The proposal presented in this paper allows for obtaining feature reduction and classifier selection based on software agents. The software agents contain Genetic Algorithms (GA) and a cost function. GA used entropy and mutual information to choose the number of features. For the classifier selection a cost function was defined. Success rate and Cohens Kappa coefficient are used as parameters to evaluate the classifiers performance. The obtained results allow finding a topology represented as a neural model for an adaptive BCI, where the number of the channels, features and the classifier are interrelated. The minimal subset of features and the optimal classifier were obtained with the adaptive BCI. Only three EEG channels were needed to obtain a success rate of 93% for the BCI competition III data set IVa.

Ant Colony Optimization for Dynamical Resource Allocation in a Multizone Temperature Experimentation Platform

In this work, an algorithm based on the ant system was used for the dynamical resource allocation in a multiple input/output experimentation platform. This platform, which mimics a temperature grid plant, is composed of multiple sensors and actuators organized in zones. The use of ants in this application allows to search the best actuator in each sample time. This allowed us to obtain a uniform temperature over the platform. Good behavior of the implemented algorithm in the experimentation platform was observed

Superposition model for Steady State Visually Evoked Potentials

Steady State Visually Evoked Potentials (SSVEP) are signals produced in the occipital part of the brain when someone gaze a light flickering at a fixed frequency. These signals have been used for Brain Machine Interfacing (BMI), where one or more stimuli are presented and the system has to detect what is the stimulus the user is attending to. It has been proposed that the SSVEP signal is produced by superposition of Visually Evoked Potentials (VEP) but there is not a model that shows that. We propose a model for a SSVEP signal that is a superposition of the response due to the rising and falling edges of the stimulus and that can be calculated for different frequencies.

A honeybee social foraging algorithm for feedback control of smart lights

In contrast to the wide array of research that uses swarm intelligence to solve optimization problems, a few approaches have recently been taken a feedback control perspective as we do here. To employ a feedback control approach, this paper shows that an algorithmic model of how honeybees forage can be used for control of smart lights. We show that only slight modifications to this model are needed to control multiple lights and to provide uniform illumination across the floor of an experimental testbed. The most challenging case is when there are no walls between lighting zones since then there are a significant inter-zone couplings, and the approach here performs especially well under these conditions. Performance of this method is compared with a variety of testbed conditions where we assume inter-zone coupling as overlapping sources. Experimental results supported by parametric statistical tests suggest that the method here is better when significant overlapping is addressed.