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

Modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados

Tesis por compendio[ES] Las fuentes de energía renovable (ER) permiten una alta disgregación, por lo que hacen posible generar la energía que se utilizará en el mismo sitio de su aprovechamiento. Esto favorece un cambio en la estructura de las redes eléctricas, permitiendo pasar de un esquema de generación centralizado a un esquema distribuido. Sin embargo, las fuentes de ER son altamente dependientes de las condiciones medioambientales como la radiación solar, la nubosidad, el viento, entre otros, por lo que lograr un sistema de generación basado en energías renovables es un reto en la actualidad. Los sistemas de generación que integran fuentes renovables tienen que ser capaces de establecer estrategias de control y gestión de la energía que para hacer un uso eficiente de ella e intentar cubrir la demanda de energía de forma óptima al combinar más de un tipo de fuente y sistema de almacenamiento, siendo posible operar de manera aislada o conectada a la red eléctrica. En la actualidad es de interés el estudio, desarrollo e implementación de sistemas gestores de la energía (SGE) para microrredes eléctricas híbridas, que permitan aumentar su eficiencia, fiabilidad, y disminuir los costes de instalación, operación y mantenimiento. Diversos estudios de investigación han probado múltiples estrategias, desde SGE basados en reglas, algoritmos comparativos, controladores clásicos, y en años recientes, la integración de algoritmos de optimización bio-inspirados e inteligencia artificial. Estos algoritmos han mostrado ser una alternativa interesante a las técnicas clásicas para la solución de problemas de optimización y control en diversos problemas de ingeniería, su aplicación en el ámbito de las microrredes sigue en estudio y en ello se basa este trabajo de investigación. Los algoritmos bio-inspirados se fundamentan en imitar matemáticamente los mecanismos y estrategias que la naturaleza ha implementado a lo largo de millones de años para lograr un equilibrio en su funcionamiento, por ejemplo, imitando el cómo las aves vuelan en parvada buscando alimento, o como las hormigas y los lobos siguen patrones para la búsqueda de su alimento, o como las especies llevan a cabo mecanismos de cruce con el objetivo de mejorar su raza haciéndolas una especie optimizada y mejorando su supervivencia. Por tanto, se puede hacer una analogía con los sistemas artificiales para la mejora de controladores y diseño de sistemas en microrredes eléctricas. En este trabajo de investigación se muestra el modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados con el objetivo de mejorar su desempeño, partiendo desde el control primario, con la mejora de los convertidores de potencia, hasta el control terciario con las transacciones energéticas de la microrred. Se exploran diversos algoritmos, evaluando su desempeño. Los resultados para las diferentes etapas de esta investigación se encuentran plasmados en cuatro diferentes publicaciones científicas que se detallan en el Capítulo 2 del presente documento, donde se explica la metodología y los principales resultados y hallazgos para cada una de ellas.[CA] Les fonts d'energia renovables (ER) permeten una alta desagregació, pel que fan possible generar l'energia que s'utilitzarà en el mateix lloc del seu aprofitament. Això afavoreix un canvi en l'estructura de les xarxes elèctriques, permetent passar d'un esquema de generació centralitzat a un esquema distribuït. No obstant, les fonts d'ER són altament dependents de les condicions mediambientals com la radiació solar, la nuvolositat, el vent, entre altres; pel que aconseguir un sistema de generació basat en energies renovables és un repte. Els sistemes de generació que integren energies renovables han de ser capaços de: establir estratègies de control i gestió de l'energia que es genera per fer un ús eficient d'ella i intentar cobrir la demanda d'energia de la millor manera possible al combinar més d'un tipus de font d'energia, i sistemes d'emmagatzemament. Aquest esquema es coneix com a microxarxa elèctrica, la qual és capaç d'operar de manera aïllada de la xarxa elèctrica principal, o de manera interconnectada. Actualment s'està interessant en l'estudi, desenvolupament i implementació de sistemes gestors de l'energia (SGE) per a microxarxes elèctriques híbrides, que permeten augmentar la seua eficiència, fiabilitat i reduir els costos de la seua instal·lació i d'operació i manteniment. S'han provat múltiples estratègies, des de SGE basats en regles, algorismes comparatius, controladors clàssics i, en anys recents, la integració d'algorismes d'optimització bio-inspirats i intel·ligència artificial. Aquests algorismes han demostrat ser una alternativa interessant a les tècniques clàssiques per a la solució de problemes d'optimització i control en diversos problemes d'enginyeria, la seua aplicació en l'àmbit de les microxarxes continua en estudi. Els algorismes bio-inspirats es basen en imitar matemàticament els mecanismes i estratègies que la Natura ha implementat al llarg de milions d'anys per aconseguir equilibri en el seu funcionament, per exemple, imitant com les aus volen en ramat buscant menjar, o com les formigues i els llops segueixen patrons per a la recerca del seu menjar, o com les espècies porten a terme mecanismes de creuament amb mira a millorar la seua raça fent-les una espècie més apta per a la supervivència;, el qual es pot fer una analogia a sistemes artificials per a la millora de controladors i disseny de sistemes en microxarxes elèctriques. En aquest treball de recerca es mostra el model i desenvolupament d'un sistema de gestió òptima per a una microxarxa emprant algorismes bio-inspirats amb l'objectiu de millorar el seu rendiment, partint des del control primari, amb la millora dels convertidors de potència, fins al control terciari amb les transaccions energètiques de la microxarxa. S'exploren diversos algorismes, avaluant el seu rendiment. Els resultats per a les diferents etapes d'aquesta recerca es troben plasmats en quatre diferents publicacions científiques que es detallen al Capítol 2 del present document, on s'explica la metodologia i els principals resultats i troballes per a cada una d'elles.[EN] Renewable energy sources (RES) allow for high disaggregation, making it possible to generate energy at the site of its use. This favors a change in the structure of electrical grids, allowing for a transition from a centralized generation scheme to a distributed scheme. However, RES are highly dependent on environmental conditions such as solar radiation, cloudiness, wind, among others, making the creation of a renewable energy generation system a challenge. Generation systems that integrate renewable energies must be able to establish control and energy management strategies to make efficient use of the energy generated and try to meet the energy demand in the best possible way by combining more than one type of energy source and storage systems. This scheme is known as a microgrid, which is capable of operating independently from the main electrical grid or interconnecting with it. Currently, the study, development, and implementation of energy management systems (EMS) for hybrid microgrids are of interest in order to increase their efficiency, reliability, and reduce installation, operation, and maintenance costs. Multiple strategies have been tested, including rule-based EMS, comparative algorithms, classical controllers, and in recent years, the integration of bio-inspired optimization algorithms and artificial intelligence. These algorithms have shown to be an interesting alternative to classical techniques for solving optimization and control problems in various engineering problems, although their application in the field of microgrids is still under study. Bio-inspired algorithms are based on mathematically imitating the mechanisms and strategies that Nature has implemented over millions of years to achieve balance in its operation, for example, by imitating how birds fly in flocks in search of food, or how ants and wolves follow patterns to search for food, or how species carry out crossing mechanisms in order to improve their breed and make them more suitable for survival; in other words, they are based on how Nature optimizes its resources to prosper. Therefore, an analogy can be made with artificial systems for improving controllers and designing systems in microgrids. In this research work, the model and development of an optimal management system for a microgrid using bio-inspired algorithms is presented with the aim of improving its performance, starting from primary control, with the improvement of power converters, to tertiary control with the energy transactions of the microgrid. Various algorithms are explored, evaluating their performance. The results for the different stages of this research are reflected in four different scientific publications that are detailed in Chapter 2 of this document, where the methodology and main results and findings for each of them are explained.The authors wish to acknowledge the National Council of Science and Technology of Mexico (CONACYT) for funding this work through the Ph.D. scholarship number 486670. The authors would also thank the Institute of Energy Engineering of the Polytechnic University of Valencia, Spain, and the Department of Water and Energy Studies of the University of Guadalajara, Mexico, for all their support and collaboration. This study has also been supported by Food and Agriculture Organization of the United Nations through the project “Design of a Hybrid Renewable Microgrid System”.Águila León, J. (2023). Modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196747Compendi

An investigation of the performance of a free-piston engine using Miller cycle and renewable fuels

A dual-piston type two-stroke spark-ignition free-piston engine prototype has been developed. A comprehensive review on recent published researches and patent documents from academia and industrial organizations on free-piston engine, especially the application of renewable fuels such as hydrogen and ethanol, was conducted. Relevant parameters affecting the operating performance and a number of challenges had been identified as the common denominator for this technology. Modelling and simulations using one-dimensional tools were conducted in parallel with the development activities. Three main simulation models for the crankshaft engines were developed, validated and optimised before converted into the free-piston engine model. This was done by using imposed-piston motion sub-model. The two-stroke free-piston engine model had undergone parametric study for valve timing optimisation. By using similar methods, a two-stroke hydrogen free-piston engine was developed from the prototype and it was validated by experimental data. Emission performance, along with the relationship between equivalent ratio, in-cylinder temperature and NOx emission on the hydrogen FPE was investigated. Series of ethanol-gasoline blend free-piston engine were developed, essential parameters such as compression ratio, air/fuel ratio and valve timing, etc., were amended for each model and both engine performance and emission performance were analysed. Miller cycle has been applied on three types of free-piston engine models: gasoline FPE, hydrogen FPE and ethanol FPE. Two types of Miller cycle (LIVC and EIVC) were both applied and analysed, and different late/early intake valve closing angles (from 5°CA to 35°CA) have been converted to specific time to apply on the free-piston engine. Engine performance and emission performance of Miller cycle free-piston engines have been obtained and individually presented. It turns out that large late/early intake valve closing angles (time) are not suitable in renewable free-piston engines due to the efficiency reduction

Load forecasting: a cross-field study on server and energy load forecasting Impact of temporal factors on generalization ability and performance of regression models

The server load prediction and energy load forecasting have available a wide range of approaches and applications, with their general goal being the prediction of future load for a specific period of time on a given system. Depending on the specific goal, different methodologies can be applied. In this dissertation, the integration of additional temporal information to datasets, as a mean to create a more generalized model is studied. The main steps involve a deep literature review in order to find the most suited methodologies and/or learning methods. A novel dataset enrichment process through the integration of extra temporal information and lastly, a cross-model testing stage, where trained models for server load prediction and energy load forecast are applied to the opposite field. This last stage, tests and analyses the generalization level of the created models through the temporal information integration procedure. The created models were both oriented to short-term load forecasting problems, with the use of data from single and combined months, regarding real data from Wikipedia servers of the year 2016 in the case of server load prediction and real data regarding the consumption levels in April 2016 of the city of Leiria/Portugal for the energy load forecasting case study. The learning methods used for creating the different models were linear regression, artificial neural networks and support vector machines oriented to regression problems, more precisely the Smoreg implementation. Results prove that it is possible to tune the dataset features, e.g., granularity and time window to improve prediction results and generalization. Results from this work, as well as an optimization approach through the use of genetic algorithms, normalization effects, split ratio vs crossvalidation influence and different granularities and time windows were peer-reviewed published

Transposon mediated genetic modification of gram-positive bacteria.

Genetic modification is vital for modern microbial research. In this thesis, genetic modification explored in bacteria with the use of plasmid vectors that contain modified transposons. Transformation of Gram-positive bacteria was explored through addition of the Himar1 transposon to the previously constructed chromatic bacteria toolbox. Conjugation was then used to insert the Himar-based plasmids into a range of Gram-positive and Gram-negative bacteria. Results of this showed that the Himar-based plasmids could not be used to produce insertion mutants of any Gram-positive strains. However, a handful of Gram-negative strains could be tagged using these plasmids including strains not tagged with the original chromatic bacteria toolbox. Following production of the Himar-based plasmids, transformation protocols were optimised in the Gram-positive strain Arthrobacter sp. Leaf145. Transformation allows for greater amounts of plasmid insertions than conjugation and would increase the number of insertion mutants produced the Himar-based plasmids. However, even after successful optimisation of transformation, insertion mutants were not observed with the Himar-based plasmids in Arthrobacter sp. Leaf145. During transformation optimisation, Tn1409- based plasmids were capable of producing insertion mutants in Arthrobacter sp. Leaf145. Therefore, the use of these plasmids was further explored for native protein over-expression in Arthrobacter sp. Leaf145. Along with genetic analysis of Arthrobacter sp. Leaf145, new Tn1409-based plasmids were constructed which allowed for expression of new genes in Arthrobacter sp. Leaf145. Due to time constraints, the plasmids that would allow for homologous protein over-expression in Arthrobacter sp. Leaf145 could not be tested. However, these are available for use in future research

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp