Despliegue óptimo de redes de distribución eléctricas soterradas usando métodos metaheurísticos y simulación

This document presents a planning model that allows optimizing the deployment of underground electrification networks for distribution considering the number of users simultaneously connected to a transformer. We present a model based on a heuristic process that seeks to reduce costs by using the resources required for a minimum cost routing on a geo-referenced scenario. The model is scalable because it allows the population density of the studied georeferenced area to be varied, that is, it adjusts to the use of resources required for different population quantities. Additionally, a simulation process is presented, articulated to the planning model using the Cymdist software, contemplating elements of a real underground electrification network, in order to verify voltage problems, failures, overloads, etc. The obtained results allow to quickly diagnose the possible deployment and routing options of underground networks for distribution, warning to decrease the times for deployment of new networks, in addition the work successfully explores the optimality principle and makes the heuristic process computationally useful. Finally, the proposal provides a road map with a view to the optimal planning of underground electrification networks for distribution.Este documento presenta un modelo de planeación que permite optimizar el despliegue de redes de electrificación soterradas para distribución considerando la cantidad de usuarios conectados simultáneamente a un transformador. Se presenta un modelo basado en un proceso heurístico que busca reducir costes por uso de recursos requeridos para un enrutamiento de mínimo costo sobre un escenario georreferenciado. El modelo es escalable pues permite que se varíe la densidad poblacional del área georreferenciada estudiada, es decir, se ajusta al uso de recursos requeridos para diferentes cantidades poblacionales. Adicionalmente se presenta un proceso de simulación articulado al modelo de planeación mediante el software Cymdist, contemplando elementos de una red de electrificación soterrada real, con la finalidad de verificar problemas de tensión, fallos, sobrecargas, etc. Los resultados obtenidos permiten diagnosticar rápidamente las posibles opciones de despliegue y enrutamiento de redes soterradas para distribución, advirtiendo disminuir los tiempos por despliegue de nuevas redes, además el trabajo explora con éxito el principio de optimalidad y hace que el proceso heurístico sea computacionalmente útil. Finalmente, la propuesta brinda un mapa de ruta con visión hacia la óptima planeación de redes de electrificación soterradas para distribución

Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

The conjugation of small nodes with sensing, communication and processing capabilities allows for the creation of wireless sensor networks (WSNs). These networks can be deployed to measure a very wide range of environmental phenomena and send data from remote locations back to users. They offer new and exciting possibilities for applications and research. This paper presents the background of WSNs by firstly exploring the different fields applications, with examples for each of these fields, then the challenges faced by these networks in areas such as energy-efficiency, node localization, node deployment, limited storage and routing. It aims at explaining each issue and giving solutions that have been proposed in the research literature. Finally, the paper proposes a practical scenario of deploying a WSN by autonomous robot path construction. The requirements for such a scenario and the open issues that can be tackled by it are exposed, namely the issues of associated with measuring RSSI, the degree of autonomy of the robot and connectivity restoration.The authors would like to acknowledge the company Inspiring Sci, Lda for the interest and valuable contribution to the successful development of this work.info:eu-repo/semantics/publishedVersio

Automatic machine learning:methods, systems, challenges

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications