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Modelling the bird flight : Scientific Report 2007-2010

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ISBN: 9783866447615 Year: Pages: 44 p. DOI: 10.5445/KSP/1000020294 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-30 20:02:00
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Abstract

The aerodynamics of flying birds and insects plays a crucial role in the domain of aeronautical engineering. The energy-efficient construction of winglets for airplanes, the formation flight of tactical aircraft or the drone engineering or military applications are inspired by birds. This holds also for flow and structure simulation of flapping wing motion, taking the unsteady aerodynamics and corresponding wing deformations into account at high flow velocities and flapping frequencies.

Process Modelling and Simulation

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ISBN: 9783039214556 9783039214563 Year: Pages: 298 DOI: 10.3390/books978-3-03921-456-3 Language: English
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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Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.

Keywords

process model validation --- partial least square regression --- phytochemicals --- natural extracts --- wheat germ --- fluidized bed drying --- mathematical model --- moisture content --- condensation --- simulation --- Pharmaceutical Processes --- Mammalian Cell Culture --- sensitivity analysis --- parameter estimation --- Design of Experiments --- algebraic modeling language --- dynamic optimization --- model predictive control --- moving horizon estimation --- fluid bed granulation --- heat and mass balance --- population balance model --- binder dissolution --- kernel development --- robust optimization --- uncertainty --- point estimation method --- equality constraints --- parameter correlation --- barley --- simulation --- hydration --- swelling --- cooking --- porridge --- extents --- graph theory --- model identification --- observability --- optimal clustering --- parameter estimation --- state decoupling --- data-mining --- machine learning --- neural networks --- chemistry --- materials --- engineering --- energy --- grey-box model --- machine learning --- SOS programming --- process modeling --- scrap dissolution --- scrap melting --- thermodynamics --- kinetics --- dynamic converter modelling --- Combined Heat and Power --- gray-box model --- utility management --- CHP legislation --- optimization --- polyacrylonitrile-based carbon fiber --- coagulation bath --- dry-jet wet spinning process --- computational fluid dynamics --- wave resonance --- maximum wave amplitude --- reactor coolant pump --- vane --- costing stopping --- mathematical model --- idling test --- n/a

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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