Design and simulation of vehicle controllers through genetic algorithms

Genetic Programming (GP) is a population-based evolutionary technique, which, unlike a Genetic Algorithm (GA) does not work on a fixed-length data structure, but on a variable-length structure and aims to evolve functions, models or programs, rather than finding a set of parameters. There are different histories of driver development, so different proposals of the use of PG to evolve driver structures are presented. In the case of an autonomous vehicle, the development of a steering controller is complex in the sense that it is a non-linear system, and the control actions are very limited by the maximum angle allowed by the steering wheels. This paper presents the development of an autonomous vehicle controller with Ackermann steering evolved by means of Genetic Programming

Unsupervised learning algorithms applied to grouping problems

One of the tasks of great interest within process mining is the discovery of business process models, which consists of using an event log as input and producing a business process model by analyzing the data contained in the log and applying a process mining method, task and/or technique. The discovery allows the identification of the behaviors contained in the cases of the event log in order to detect possible deviations and/or validate that the business process is executed according to the business requirements. This paper presents an approach based on unsupervised learning techniques for the grouping of traces to generate simpler and more understandable models. The algorithms implemented for clustering are K-means, hierarchical agglomerative and density-based spatial clustering of applications with noise (DBSCAN)

Search for optimal routes on roads applying metaheuristic algorithms

The design of efficient routes for vehicles visiting a significant number of destinations is a critical factor for the competitiveness of many companies. The design of such routes is known as the vehicle routing problem. Indeed, efficient vehicle routing is one of the most studied problems in the areas of logistics and combinatorial optimization. The present study presents a memetic algorithm that evolves using a mechanism inspired by virus mutations. Additionally, the algorithm uses Taboo Search as an intensification mechanism

Design of a Network with wireless sensor applied to data transmission based on IEEE 802.15.4 standard

The problem of data transmission in wireless sensor networks (WSN), with real time guarantees, is an issue that has important references in the international scientific community, but that still does not have a solution that can completely satisfy this requirement [1]. Therefore, real time data transmission with WSN is considered an open issue with many possibilities of improvement. In this sense, this document presents a new procedure to ensure this type of transmission with WSN, particularly from the planning of the resources available for data transmission in the network, taking as a reference the IEEE 802.15.4 standard

Artificial techniques applied to the improvement of the previous signals in the power amplifiers

A rapid evolution in electronic systems has been experienced in recent years, and one of the fields where this development has been notorious is the telecommunication systems in which users demand more and better services and with higher data transfer speeds. This has generated the need to develop new devices, algorithms and systems that manage to satisfy the requirements demanded y new technologies. An example of the above is the front-end of telecommunication systems. Systems need to be more efficient, but some elements of the systems, as the power amplifier, present nonlinearity when operating in its most efficient region, causing that it has to make a commitment between efficiency and linearity. This paper presents a comparison of different artificial neural network architectures, as a behavioral modeling method, to perform digital predistortion of power amplifiers

Wireless sensor network for forest fire detection

Some methods for fire detection include monitoring from watch towers and the use of satellite images [1] [2]. Unfortunately, these are not efficient due to several reasons, such as high infrastructure costs (sophisticated equipment), the fact that they require a large number of trained personnel and that they make real-time monitoring difficult, since when the phenomenon is detected, its speed of propagation has produced uncontrollable levels of damage. This paper proposes a method for detecting forest fires, using a network of wireless sensors and information fusion methods

Recommendation of energy ffficiency indexes for the coffee sector in Honduras using multivariate statistics

The objectives of this study were to define and determine the energy efficiency indexes that should be considered to measure and analyze the energy performance in enterprises engaged in processing green coffee for export. The investigation arose through a case study on a coffee processing plant in Honduras. The purpose of this work was fulfilled under the recommendations set forth in ISO 50001:2011, which were used as references. In addition, determine the energy structure of the company target of study, establishing the strategy to obtain daily records of the energy situation of the company and under a four-step process: energy structure of the company, daily registry of consumption, indicators of energy performance, and potential evaluations for improvement. With the observed results for more than 100 days, a model of high-quality was found with a coefficient of determination of 0.88, which helped to find and define different energy performance indicators

Method for classifying images in databases through deep convolutional networks

Since 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image classification

Assembly language and processor design: an integrated project

The research describes a project in computer organization class with two groups, one in 2017 and the other in 2018, in a trimester course of 68.34 hours which integrated both topics assembly language and hardware design. The project involves the implementation of a reduced version of an embedded MIPS32 system, with a simulated and real hardware implementation using FPGA (Field Programmable Gate Array). Followed by the development of a game in C and assembly language that runs on the embedded system. The results show that all students from the first and second group that coursed computer organization class during a 10-week period expressed high levels of interest and engagement, despite the complexity of the project. With feedback from the first group and with some modifications to the project, all students from the second group successfully completed the projectLa investigación describe la aplicación de un proyecto en la clase de organización de computadoras con dos grupos, uno en 2017 y otro en 2018, en un curso trimestral de 68.34 horas que integró lenguaje ensamblador y diseño de hardware. El proyecto implica la implementación de una versión reducida de un sistema MIPS32 embebido, con una implementación simulada y una en hardware utilizando FPGA (Field Programmable Gate Array). Seguido del desarrollo de un juego en C y lenguaje ensamblador que se ejecuta en el sistema embebido. Los resultados muestran que todos los estudiantes del primer y segundo grupo que cursaron la clase de organización de computadoras durante un período de 10 semanas expresaron altos niveles de interés y participación, a pesar de la complejidad del proyecto. Con la retroalimentación del primer grupo y con algunas modificaciones al proyecto, todos los estudiantes del segundo grupo completaron el proyecto con éxit