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Fuzzy Techniques for Decision Making

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ISBN: 9783038428879 9783038428886 Year: Pages: VI, 404 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Mathematics
Added to DOAB on : 2018-05-18 13:55:26
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Abstract

This book contains the successful invited submissions to a Special Issue of Symmetry in the subject area of “Fuzzy Techniques for Decision Making”. We invited contributions addressing novel techniques and tools for decision making (e.g., group or multi-criteria decision making) with notions that overcome the problem of finding the membership degree of each element as in Zadeh's original model. We could garner interesting articles on a variety of setups as well as applications. As a result, this book includes some novel techniques and tools for decision making, such as instrumental tools for analysis (correlation coefficients, similarity measures, aggregation operators) in various settings and novel methodological contributions (discrete optimization with fuzzy constraints, COMET, or fuzzy bi-matrix games) and applications (to project delivery systems, maintenance performance in industry, group emergencies, pedestrians flows, valuation of assets, water pollution control, or aquaculture enterprise sustainability). The published submissions cover models like fuzzy soft sets, hesitant fuzzy sets, (fuzzy) soft rough sets, neutrosophic sets, as well as other hybrid models.

From Natural to Artificial Intelligence - Algorithms and Applications

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ISBN: 9781789847024 9781789847031 Year: Pages: 216 DOI: 10.5772/intechopen.71252 Language: English
Publisher: IntechOpen
Subject: Computer Science
Added to DOAB on : 2019-10-03 07:51:52

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Artificial intelligence (AI) is a field experiencing constant growth and change, with a long history. The challenge to reproduce human behavior in machines requires the interaction of many fields, from engineering to mathematics, from neurology to biology, from computer science to robotics, from web search to social networks, from machine learning to game theory, etc. Numerous applications and possibilities of AI are already a reality but other ones are still needed to reduce the human limitations and to expand the human capability to limits beyond our imagination. This book brings together researchers working on areas related to AI such as speech and face recognition, representation of learning and acoustic scenarios, fuzzy inference and data exploration, cellular automata applications with a special interest in the tools and algorithms that can be applied in these different branches of the AI discipline. The book provides a new reference to an audience interested in the development of this field.

Analysis for Power Quality Monitoring

Authors: ---
ISBN: 9783039281107 / 9783039281114 Year: Pages: 210 DOI: 10.3390/books978-3-03928-111-4 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:57
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We are immersed in the so-called digital energy network, continuously introducing new technological advances for a better way of life. Numerous emerging words are in the spotlight, namely: Internet of Things (IoT), Big Data, Smart Cities, Smart Grid, Industry 4.0, etc. To achieve this formidable goal, systems should work more efficiently, and this fact inevitably leads to power quality (PQ) assurance. Apart from its economic losses, a bad PQ implies serious risks for machines, and consequently for people. Many researchers are endeavoring to develop new analysis techniques, instruments, measurement methods, and new indices and norms that match and fulfil the requirements regarding the current operation of the electrical network. This book offers a compilation of the some recent advances in this field. The chapters range from computing issues to technological implementations, going through event detection strategies and new indices and measurement methods that contribute significantly to the advancement of PQ analysis. Experiments have been developed within the frames of research units and projects, and deal with real data from industry and public buildings. Human beings have an unavoidable commitment with sustainability, which implies adapting PQ monitoring techniques to our dynamic world, defining a digital and smart concept of quality for electricity.

Keywords

power system measurements --- dynamic phasor estimation --- Kalman filters --- phasor measurement --- power quality --- signal waveform compression --- higher-order statistics (HOS) --- power quality (PQ) --- computational solutions for advanced metering infrastructure (AMI) --- smart grid (SG) applications --- harmonics --- constant amplitude trend --- fourth-order statistics --- detection --- spectral kurtosis --- low-voltage DC networks --- power quality disturbances --- power quality monitoring --- DC power quality indices --- voltage ripple --- reconfigurable computing --- FPGA --- power quality --- spectral kurtosis --- digital signal processing --- embedded system --- power quality disturbance --- convolution neural network --- improved principal component analysis --- wind-grid distribution --- power quality (PQ) --- embedded microcontroller --- low cost monitor --- sensor node --- wireless sensor network --- IoT --- RMS voltage estimation --- low computational cost --- limited resources hardware --- power event detection --- energizing warning --- power quality --- voltage sags --- islanding operation --- induction machines --- modelling --- distribution networks --- power quality --- phasor measurement units --- voltage fluctuations --- flicker --- modulation --- power distribution systems --- smart grids --- dense-mesh topology --- municipal distribution network --- smart grid --- power quality monitor --- long-term --- operation analysis --- power quality (PQ) --- PQ indices and thresholds --- reliability --- sensors and instruments for PQ --- big data --- machine learning --- soft computing --- statistical signal processing --- data scalability --- data compression

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-03-08 11:42:05
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This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water

Keywords

data scarce basins --- runoff series --- data forward prediction --- ensemble empirical mode decomposition (EEMD) --- stopping criteria --- method of tracking energy differences (MTED) --- deep learning --- convolutional neural networks --- superpixel --- urban water bodies --- high-resolution remote-sensing images --- monthly streamflow forecasting --- artificial neural network --- ensemble technique --- phase space reconstruction --- empirical wavelet transform --- hybrid neural network --- flood forecasting --- self-organizing map --- bat algorithm --- particle swarm optimization --- flood routing --- Muskingum model --- machine learning methods --- St. Venant equations --- rating curve method --- nonlinear Muskingum model --- hydrograph predictions --- flood routing --- Muskingum model --- hydrologic models --- improved bat algorithm --- Wilson flood --- Karahan flood --- flood susceptibility modeling --- ANFIS --- cultural algorithm --- bees algorithm --- invasive weed optimization --- Haraz watershed --- ANN-based models --- flood inundation map --- self-organizing map (SOM) --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- ensemble technique --- artificial neural networks --- uncertainty --- streamflow predictions --- sensitivity --- flood forecasting --- extreme learning machine (ELM) --- backtracking search optimization algorithm (BSA) --- the upper Yangtze River --- deep learning --- LSTM network --- water level forecast --- the Three Gorges Dam --- Dongting Lake --- Muskingum model --- wolf pack algorithm --- parameters --- optimization --- flood routing --- flash-flood --- precipitation-runoff --- forecasting --- lag analysis --- random forest --- machine learning --- flood prediction --- flood forecasting --- hydrologic model --- rainfall–runoff, hybrid & --- ensemble machine learning --- artificial neural network --- support vector machine --- natural hazards & --- disasters --- adaptive neuro-fuzzy inference system (ANFIS) --- decision tree --- survey --- classification and regression trees (CART), data science --- big data --- artificial intelligence --- soft computing --- extreme event management --- time series prediction --- LSTM --- rainfall-runoff --- flood events --- flood forecasting --- data assimilation --- particle filter algorithm --- micro-model --- Lower Yellow River --- ANN --- hydrometeorology --- flood forecasting --- real-time --- postprocessing --- machine learning --- early flood warning systems --- hydroinformatics --- database --- flood forecast --- Google Maps

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