394 research outputs found
Control and estimation techniques applied to smart microgrids : a review
DATA AVAILABILITY : No data was used for the research described in the article.The performance of microgrid operation requires hierarchical control and estimation schemes that coordinate and monitor the system dynamics within the expected manipulated and control variables. Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation technologies are reviewed for developing dynamic models of smart microgrids. The hierarchical system of a microgrid control consists of three architectural layers, primary, secondary and tertiary, which need to be supported by real-time monitoring and measurement environment of the system variables and parameters. Various control and estimation schemes have been devised to handle the dynamic performance of microgrids in the function of control layers requirement. Firstly, control schemes in the innovative grid environment are evaluated to understand the dynamics of the developed technologies. Six control technologies, linear, non-linear, robust, predictive, intelligent and adaptive, are mainly used to model the control design within the layer(s) regardless of the types of microgrids. Secondly, the estimation technologies are evaluated based on the state of variables, locations and modelling of microgrids that can efficiently support the performance of the controllers and operating microgrids. Finally, a future vision for designing hierarchical and architectural control techniques for the optimal operation of intelligent microgrids is also provided. Therefore, this study will serve as a fundamental conceptual framework to select a perfect optimal design modelling strategy and policy-making decisions to control, monitor and protect the innovative electrical network.http://www.elsevier.com/locate/rserhj2023Electrical, Electronic and Computer Engineerin
Automatic characterization and generation of music loops and instrument samples for electronic music production
Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process.
We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process.
We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation
Abordagens multimodais com utilização de deep learning e unimodais com aprendizado de máquina no reconhecimento de emoções em músicas
Orientadora: Profa. Dra. Denise Fukumi TsunodaCoorientadora: Profa. Dra. Marília Nunes SilvaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Gestão da Informação. Defesa : Curitiba, 30/08/2023Inclui referênciasResumo: Esta pesquisa foi realizada com base na compreensão da relevância da relação entre música e emoção na vida humana, abrangendo desde o lazer até estudos científicos. Embora a organização emocional da música seja intrínseca à natureza humana, o reconhecimento automático de emoções musicais enfrenta desafios, configurando-se como um tema complexo na recuperação de informações musicais. Nesse contexto, o propósito central desta tese foi investigar se a adoção de abordagens multimodais, envolvendo informações de diferentes fontes e arquiteturas de deep learning, pode superar o desempenho das abordagens unimodais baseadas em algoritmos de aprendizado de máquina. Essa indagação emergiu da carência de estratégias multimodais na área e da perspectiva de melhoria nos resultados de classificação reportados em pesquisas correlatas. Com cinco objetivos específicos, esta pesquisa abordou a identificação de um modelo cognitivo de emoções, definição de modalidades, construção de bases de dados multimodais, comparação de arquiteturas de deep learning e avaliação comparativa das abordagens multimodais com abordagens unimodais utilizando algoritmos tradicionais de aprendizado de máquina. A análise dos resultados demonstrou que as abordagens multimodais alcançaram desempenho superior em diversos cenários de classificação, comparadas às estratégias unimodais. Tais resultados contribuem positivamente para a compreensão da eficácia das abordagens multimodais e das arquiteturas de deep learning no reconhecimento de emoções em músicas. Adicionalmente, a pesquisa ressalta a necessidade de atenção aos modelos emocionais e metadados em plataformas online, visando evitar vieses e ruídos. Esta tese oferece contribuições relevantes na área de reconhecimento de emoções em músicas, particularmente no desenvolvimento de bases de dados multimodais, avaliação de arquiteturas de deep learning para problemas tabulares, protocolos de experimentos e abordagens voltadas à cognição musical. A comparação sistemática entre abordagens multimodais e unimodais evidencia as vantagens das primeiras, incentivando novas pesquisas nesse campoAbstract: This research was conducted based on the understanding of the significance of the relationship between music and emotion in human life, spanning from leisure to scientific studies. Although the emotional organization of music is intrinsic to human nature, the automatic recognition of musical emotions faces challenges, manifesting as a complex theme in the retrieval of musical information. Within this context, the central purpose of this thesis was to investigate whether the adoption of multimodal approaches, involving information from different sources and deep learning architectures, can outperform unimodal approaches based on machine learning algorithms. This inquiry arose from the lack of multimodal strategies in the field and the prospect of improvement in classification results reported in related research. With five specific objectives, this research addressed the identification of a cognitive model of emotions, definition of modalities, construction of multimodal databases, comparison of deep learning architectures, and comparative evaluation of multimodal approaches with unimodal approaches using traditional machine learning algorithms. The analysis of results demonstrated that multimodal approaches achieved superior performance in various classification scenarios, compared to unimodal strategies. These findings positively contribute to the understanding of the effectiveness of multimodal approaches and deep learning architectures in the recognition of emotions in music. Additionally, the research emphasizes the need for attention to emotional models and metadata in online platforms, aiming to avoid biases and noise. This thesis offers relevant contributions to the field of music emotion recognition, particularly in the development of multimodal databases, evaluation of deep learning architectures for tabular problems, experimental protocols, and approaches focused on musical cognition. The systematic comparison between multimodal and unimodal approaches highlights the advantages of the former, encouraging new research in this fiel
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
Elements of Ion Linear Accelerators, Calm in The Resonances, Other_Tales
The main part of this book, Elements of Linear Accelerators, outlines in Part
1 a framework for non-relativistic linear accelerator focusing and accelerating
channel design, simulation, optimization and analysis where space charge is an
important factor. Part 1 is the most important part of the book; grasping the
framework is essential to fully understand and appreciate the elements within
it, and the myriad application details of the following Parts. The treatment
concentrates on all linacs, large or small, intended for high-intensity, very
low beam loss, factory-type application. The Radio-Frequency-Quadrupole (RFQ)
is especially developed as a representative and the most complicated linac form
(from dc to bunched and accelerated beam), extending to practical design of
long, high energy linacs, including space charge resonances and beam halo
formation, and some challenges for future work. Also a practical method is
presented for designing Alternating-Phase- Focused (APF) linacs with long
sequences and high energy gain. Full open-source software is available. The
following part, Calm in the Resonances and Other Tales, contains eyewitness
accounts of nearly 60 years of participation in accelerator technology.
(September 2023) The LINACS codes are released at no cost and, as always,with
fully open-source coding. (p.2 & Ch 19.10)Comment: 652 pages. Some hundreds of figures - all images, there is no data in
the figures. (September 2023) The LINACS codes are released at no cost and,
as always,with fully open-source coding. (p.2 & Ch 19.10
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings
Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which
provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels
Exploring QCD matter in extreme conditions with Machine Learning
In recent years, machine learning has emerged as a powerful computational
tool and novel problem-solving perspective for physics, offering new avenues
for studying strongly interacting QCD matter properties under extreme
conditions. This review article aims to provide an overview of the current
state of this intersection of fields, focusing on the application of machine
learning to theoretical studies in high energy nuclear physics. It covers
diverse aspects, including heavy ion collisions, lattice field theory, and
neutron stars, and discuss how machine learning can be used to explore and
facilitate the physics goals of understanding QCD matter. The review also
provides a commonality overview from a methodology perspective, from
data-driven perspective to physics-driven perspective. We conclude by
discussing the challenges and future prospects of machine learning applications
in high energy nuclear physics, also underscoring the importance of
incorporating physics priors into the purely data-driven learning toolbox. This
review highlights the critical role of machine learning as a valuable
computational paradigm for advancing physics exploration in high energy nuclear
physics.Comment: 146 pages,53 figure
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