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Integrierter Umweltschutz in der Metallerzeugung: Simulationsgestützte operative Produktionsplanung zur Optimierung metallurgischer Abfallverwertungsprozesse. Schlussbericht des Forschungsvorhabens

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ISBN: 9783866440289 Year: Pages: XX, 242 p. DOI: 10.5445/KSP/1000004729 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Business and Management
Added to DOAB on : 2019-07-30 20:02:00
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Als Resultat der Metallerzeugung und -verarbeitung fallen zwangsläufig eine Reihe von Abfällen an. Zur Aufbereitung und Verwertung eines Teils dieser Kuppelprodukte, hauptsächlich Stäuben und Schlämmen aus Gasreinigungen, konnte sich in den vergangenen Jahren die Verwertung in modifizierten Hochöfen etablieren. Zielsetzung des diesem Buch zugrunde liegenden Forschungsvorhabens ist es, die Wirtschaftlichkeit solcher abfallverwertenden Prozesse zu stärken, natürliche Ressourcen zu schonen und den Abfalleintrag in die Umwelt zu mindern.

L’espace public à l’épreuve : Régressions et émergences

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ISBN: 9782858925322 DOI: 10.4000/books.msha.9991 Language: French
Publisher: Maison des Sciences de l’Homme d’Aquitaine
Subject: Social Sciences
Added to DOAB on : 2019-12-06 13:15:39
License: OpenEdition Licence for Books

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Dans son acception classique, dite républicaine, l'espace public traverse une crise dont beaucoup pensent qu'il ne s'en remettra pas. Menacé aussi dans ses manifestations matérielles par la violence, l'Insécurité et les divers communautarismes ou replis sur soi, l'espace public ne semble plus pouvoir répondre aux attentes dont il avait fait l'objet par l'intermédiaire de l'aménagement. Cet ouvrage prend le contre-pied de ces Interprétations, en montrant que, loin d'avoir perdu de sa pertinence, la préoccupation pour l'espace public demeure essentielle pour affronter les défis posés par la société contemporaine, dans la mesure où la réflexion est constamment ramenée à la spatialité qui en fonde l'Intérêt et la portée. Une approche renouvelée de l'espace public est Ici proposée, qui se déploie en associant étroitement trois composantes : la matérialité, la politique, l'action, car nos analyses ne peuvent plus faire l'économie de les considérer séparément. Sa mise à l'épreuve de grands enjeux contemporains montre que l'on peut parler tout autant de régressions que d'émergences.

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.

Atmospheric Composition Observations

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ISBN: 9783038422846 9783038422853 Year: Pages: XIV, 322 DOI: 10.3390/books978-3-03842-285-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2016-11-04 11:24:05
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The composition of the atmosphere is a critical factor in understanding the nature and magnitude of processes associated with the planet’s energy balance, clouds and precipitation, biogeochemical cycling of nutrients, and public health and welfare. A detailed understanding of trace gases, aerosol particles, and hydrometeors is challenging due to the combination of their physicochemical complexity, variable lifetimes, and spatial inhomogeneity. Recent advances in instrumentation have resulted in improved measurements and an increased understanding of atmospheric composition. Laboratory and field in-situ measurement studies have benefited from such improvements, including improved spatial and temporal resolution, the ability to sample in challenging conditions (e.g., on airborne platforms, in clouds, at widely ranging pressure and temperature conditions), and the ability to measure a wider range of chemical species, and, in the case of aerosol particles, to detect smaller sizes. Remote sensing capabilities have increased in recent years, thus offering new views of atmospheric composition across broad spatiotemporal ranges. Manuscripts related to all aspects of atmospheric observations are included in this Special Issue, including advances in observational techniques and scientific insights into atmospheric composition.

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.

Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

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ISBN: 9783038972921 9783038972938 Year: Pages: 186 DOI: 10.3390/books978-3-03897-293-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-22 10:01:53
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The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve 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.

Intelligent Optimization Modelling in Energy Forecasting

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ISBN: 9783039283644 9783039283651 Year: Pages: 262 DOI: 10.3390/books978-3-03928-365-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2020-04-07 23:07:09
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Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Keywords

short-term load forecasting --- weighted k-nearest neighbor (W-K-NN) algorithm --- comparative analysis --- empirical mode decomposition (EMD) --- particle swarm optimization (PSO) algorithm --- intrinsic mode function (IMF) --- support vector regression (SVR) --- short term load forecasting --- crude oil price forecasting --- time series forecasting --- hybrid model --- complementary ensemble empirical mode decomposition (CEEMD) --- sparse Bayesian learning (SBL) --- multi-step wind speed prediction --- Ensemble Empirical Mode Decomposition --- Long Short Term Memory --- General Regression Neural Network --- Brain Storm Optimization --- substation project cost forecasting model --- feature selection --- data inconsistency rate --- modified fruit fly optimization algorithm --- deep convolutional neural network --- multi-objective grey wolf optimizer --- long short-term memory --- fuzzy time series --- LEM2 --- combination forecasting --- wind speed --- electrical power load --- crude oil prices --- time series forecasting --- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) --- kernel learning --- kernel ridge regression --- differential evolution (DE) --- artificial intelligence techniques --- energy forecasting --- condition-based maintenance --- asset management --- renewable energy consumption --- Gaussian processes regression --- state transition algorithm --- five-year project --- forecasting --- Markov-switching --- Markov-switching GARCH --- energy futures --- commodities --- portfolio management --- active investment --- diversification --- institutional investors --- energy price hedging --- metamodel --- ensemble --- individual --- regression --- interpolation

Elements of Causal Inference

Authors: --- ---
Book Series: Adaptive Computation and Machine Learning series ISBN: 9780262344296 9780262037310 Year: Pages: 288 Language: English
Publisher: The MIT Press
Subject: Computer Science
Added to DOAB on : 2019-01-17 11:41:31
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A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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ISBN: 9783039212156 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

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