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Applied Artificial Neural Networks

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ISBN: 9783038422709 9783038422716 Year: Pages: XIV, 244 DOI: 10.3390/books978-3-03842-271-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2016-11-11 19:33:54
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Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves—in terms of training, topologies, types, etc.—a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc.This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

Artificial Neural Networks as Models of Neural Information Processing

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889454013 Year: Pages: 220 DOI: 10.3389/978-2-88945-401-3 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2018-11-16 17:17:57
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Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.

Application of Artificial Neural Networks in Geoinformatics

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ISBN: 9783038427421 9783038427414 Year: Pages: VI, 222 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Environmental Sciences
Added to DOAB on : 2018-04-27 16:05:32
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Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, “Application of Artificial Neural Networks in Geoinformatics,” was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

Short-Term Load Forecasting by Artificial Intelligent Technologies

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ISBN: 9783038975823 / 9783038975830 Year: Pages: 444 DOI: 10.3390/books978-3-03897-583-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-01-29 10:55:39
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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

Computational Intelligence in Photovoltaic Systems

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ISBN: 9783039210985 / 9783039210992 Year: Pages: 180 DOI: 10.3390/books978-3-03921-099-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 16:10:12
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Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.

Civil Engineering and Symmetry

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ISBN: 9783039210022 / 9783039210039 Year: Pages: 198 DOI: 10.3390/books978-3-03921-003-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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A topic of utmost importance in civil engineering is finding optimal solutions throughout the life cycle of buildings and infrastructural objects, including their design, manufacturing, use, and maintenance. Operational research, management science, and optimization methods provide a consistent and applicable groundwork for engineering decision-making. These topics have received the interest of researchers and, after a rigorous peer-review process, eight papers have been published in this Special Issue. The articles in this Printed Edition demonstrate how solutions in civil engineering, which bring economic, social, and environmental benefits, are obtained through a variety of methodologies and tools. Usually, decision-makers need to take into account not just a single criterion, but several different criteria and, therefore, multi-criteria decision-making (MCDM) approaches have been suggested for application in five of the published papers; the rest of the papers apply other research methods. Most approaches suggested decision models under uncertainty, proposing hybrid MCDM methods in combination with fuzzy or rough set theory, as well as D-numbers. The application areas of the proposed MCDM techniques mainly cover production/manufacturing engineering, logistics and transportation, and construction engineering and management. We hope that a summary of the Special Issue as provided here will encourage a detailed analysis of the papers included in the Printed Edition.

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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ISBN: 9783039214099 / 9783039214105 Year: Pages: 254 DOI: 10.3390/books978-3-03921-410-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

Keywords

parameter-dependent model --- surrogate modeling --- tensor-train decomposition --- gappy POD --- heterogeneous data --- elasto-viscoplasticity --- archive --- model reduction --- 3D reconstruction --- inverse problem plasticity --- data science --- model order reduction --- POD --- DEIM --- gappy POD --- GNAT --- ECSW --- empirical cubature --- hyper-reduction --- reduced integration domain --- computational homogenisation --- model order reduction (MOR) --- low-rank approximation --- proper generalised decomposition (PGD) --- PGD compression --- randomised SVD --- nonlinear material behaviour --- machine learning --- artificial neural networks --- computational homogenization --- nonlinear reduced order model --- elastoviscoplastic behavior --- nonlinear structural mechanics --- proper orthogonal decomposition --- empirical cubature method --- error indicator --- symplectic model order reduction --- proper symplectic decomposition (PSD) --- structure preservation of symplecticity --- Hamiltonian system --- reduced order modeling (ROM) --- proper orthogonal decomposition (POD) --- enhanced POD --- a priori enrichment --- modal analysis --- stabilization --- dynamic extrapolation --- computational homogenization --- large strain --- finite deformation --- geometric nonlinearity --- reduced basis --- reduced-order model --- sampling --- Hencky strain --- microstructure property linkage --- unsupervised machine learning --- supervised machine learning --- neural network --- snapshot proper orthogonal decomposition

New Trends in Recycled Aggregate Concrete

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ISBN: 9783039211401 / 9783039211418 Year: Pages: 280 DOI: 10.3390/books978-3-03921-141-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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This book is the result of a Special Issue published in Applied Sciences, entitled “New Trends in Recycled Aggregate Concrete"". It identifies emerging research areas within the field of recycled aggregate concrete and contributes to the increased use of this eco-efficient material.Its contents are organised in the following sections: Upscaling the use of recycled aggregate concrete in structural design; Large scale applications of recycled aggregate concrete; Long-term behaviour of recycled aggregate concrete; Performance of recycled aggregate concrete in very aggressive environments; Reliability of recycled aggregate concrete structures; Life cycle assessment of recycled aggregate concrete; New applications of recycled aggregate concrete.

Keywords

reactive power concrete --- shrinkage --- creep --- steel fibre --- model --- compressive strength --- models --- geological nature of aggregates --- quality of aggregates --- concrete --- recycled aggregates --- seismic load --- strain rate --- fiber-reinforced concrete --- dynamic mechanical property --- recycled aggregate quality --- bond strength --- shear behavior --- aggregate interlock mechanism --- size effect --- ready-mixed concrete --- recycled concrete aggregates --- returned concrete --- concrete sludge fines --- soil stabilization --- recycled aggregate --- recycled aggregate concrete --- artificial neural networks --- aggregate characteristic --- input variable --- recycled concrete --- aggregate --- mixture proportioning --- flexural behavior --- recycling --- heavyweight waste glass --- cyclic load --- reinforced concrete member --- recycled aggregate concrete (RAC) --- steel reinforced recycled aggregate concrete (SRRAC) --- elevated temperature --- residual properties --- recycled coarse aggregate concrete --- nylon fiber --- mechanical properties --- permeability --- microstructure --- foam concrete --- cellular concrete --- ceramic foam --- modulus --- crushing --- energy absorbing --- CT --- foam structure --- foam stability --- recycled aggregate --- concrete --- life cycle assessment --- environmental impact --- recycled concrete aggregate --- crumb rubber --- crushed glass --- compressive strength --- tensile splitting strength --- water absorption --- concrete --- aggregates --- fly-ash --- silica fume --- blast-furnace slag --- mechanical properties --- water absorption --- reinforced concrete --- recycled aggregate concrete --- columns --- seismic performance --- numerical analysis --- variable sensitivity --- recycled aggregate --- concrete --- construction waste --- mechanical characteristics --- durable characteristics --- n/a

Sustainable Energy Systems: From Primary to End-Use

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ISBN: 9783039210961 / 9783039210978 Year: Pages: 314 DOI: 10.3390/books978-3-03921-097-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Physics (General)
Added to DOAB on : 2019-12-09 11:49:15
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This book focuses on sustainable energy systems. While several innovative and alternative concepts are presented, the topics of energy policy, life cycle assessment, thermal energy, and renewable energy also play a major role. Models on various temporal and geographical scales are developed to understand the conditions of technical as well as organizational change. New methods of modeling, which can fulfil technical and physical boundary conditions and nevertheless consider economic environmental and social aspects, are also developed.

Keywords

Active Disturbance Rejection Control --- Probabilistic Robustness --- Monte Carlo --- secondary air regulation --- areal grey relational analysis --- fuzzy rough set --- game theory --- AHP --- uncertainty analysis --- coal-fired power unit --- renewable energy --- biomass --- torrefaction --- grindability --- rotary reactor --- generation system scheduling --- integrated model --- basic plan for long-term electricity supply and demand --- forecasting model for electricity demand --- biomass --- Pinus pinaster --- fuel --- heating value --- fuelwood value index --- energy density --- ash recovery --- peach --- Energy Life-Cycle Assessment --- post-harvest --- fuzzy logic control --- artificial neural networks control --- tidal stream generator --- swell effect disturbance --- doubly fed induction generator --- maximum power point tracking --- capacity investment --- market power --- wind resources --- dynamic planning --- stochastic approach --- levelized cost of energy --- photovoltaic with energy storage system --- HOMER simulation --- LCOE comparison --- sensitivity analysis --- transient impact --- renewable energy source penetration --- power system stability --- robust optimization --- renewable energy --- flexibility --- deficit --- uncertainty --- flexible resource --- energy storage systems --- active power harmonics filter --- electrostatic devices --- hysteresis switching --- op-amp --- power electronics --- power supply reliability --- electricity --- manufacturing industry --- choice experiment --- willingness to pay --- nexus concept --- energy modelling --- resource efficiency --- renewable energy --- low-carbon economy --- forecasting --- multilayer perception --- photovoltaic --- sustainable energy --- pseudo-Huber loss --- energy from biomass --- textile industrial sector --- alternative energy --- SWOT analysis --- energy costs --- Internet of Things --- thermodynamic cycle concepts --- sustainability --- modified cycle concepts --- efficiency --- energy systems --- renewable energies --- wind power plants --- hollow rollers --- large bearings

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