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Remote Sensing based Building Extraction

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ISBN: 9783039283828 9783039283835 Year: Pages: 442 DOI: 10.3390/books978-3-03928-383-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Construction
Added to DOAB on : 2020-04-07 23:07:09
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Abstract

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D

Keywords

roof segmentation --- outline extraction --- convolutional neural network --- boundary regulated network --- very high resolution imagery --- building boundary extraction --- convolutional neural network --- active contour model --- high resolution optical images --- LiDAR --- richer convolution features --- building edges detection --- high spatial resolution remote sensing imagery --- building --- modelling --- reconstruction --- change detection --- LiDAR --- point cloud --- 3-D --- building extraction --- deep learning --- attention mechanism --- very high resolution --- imagery --- building detection --- aerial images --- feature-level-fusion --- straight-line segment matching --- occlusion --- building regularization technique --- point clouds --- boundary extraction --- regularization --- building reconstruction --- digital building height --- 3D urban expansion --- land-use --- DTM extraction --- open data --- developing city --- accuracy analysis --- building detection --- building index --- feature extraction --- mathematical morphology --- morphological attribute filter --- morphological profile --- building extraction --- deep learning --- semantic segmentation --- data fusion --- high-resolution satellite images --- GIS data --- high-resolution aerial images --- deep learning --- generative adversarial network --- semantic segmentation --- Inria aerial image labeling dataset --- Massachusetts buildings dataset --- building extraction --- simple linear iterative clustering (SLIC) --- multiscale Siamese convolutional networks (MSCNs) --- binary decision network --- unmanned aerial vehicle (UAV) --- image fusion --- high spatial resolution remotely sensed imagery --- object recognition --- deep learning --- method comparison --- LiDAR point cloud --- building extraction --- elevation map --- Gabor filter --- feature fusion --- semantic segmentation --- urban building extraction --- deep convolutional neural network --- VHR remote sensing imagery --- U-Net --- remote sensing --- deep learning --- building extraction --- web-net --- ultra-hierarchical sampling --- 3D reconstruction --- indoor modelling --- mobile laser scanning --- point clouds --- 5G signal simulation --- building extraction --- high-resolution aerial imagery --- fully convolutional network --- semantic segmentation --- n/a

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

Advances in Near Infrared Spectroscopy and Related Computational Methods

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ISBN: 9783039280520 9783039280537 Year: Pages: 496 DOI: 10.3390/books978-3-03928-053-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Chemistry (General) --- Analytical Chemistry
Added to DOAB on : 2020-01-30 16:39:46
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In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or used for characterization of food products. Its contribution in basic science and physical chemistry should be noted as well, e.g. in exploration of the nature of molecular vibrations or intermolecular interactions. One of the current development trends involves the miniaturization and simplification of instrumentation, creating prospects for the spread of NIR spectrometers at a consumer level in the form of smartphone attachments—a breakthrough not yet accomplished by any other analytical technique. A growing diversity in the related methods and applications has led to a dispersion of these contributions among disparate scientific communities. The aim of this Special Issue was to bring together the communities that may perceive NIR spectroscopy from different perspectives. It resulted in 30 contributions presenting the latest advances in the methodologies essential in near-infrared spectroscopy in a variety of applications.

Keywords

hyperspectral imaging --- variety discrimination --- Chrysanthemum --- deep convolutional neural network --- DNA --- FTIR spectroscopy --- rapid identification --- PLS-DA --- animal origin --- near-infrared hyperspectral imaging --- raisins --- support vector machine --- pixel-wise --- object-wise --- maize kernel --- hyperspectral imaging technology --- accelerated aging --- principal component analysis --- support vector machine model --- standard germination tests --- blackberries --- Rubus fructicosus --- phenolics --- carotenoids --- bioanalytical applications --- near infrared --- chemometrics --- VIS/NIR hyperspectral imaging --- corn seed --- classification --- freeze-damaged --- image processing --- imaging visualization --- wavelength selection --- NIR spectroscopy --- binary dragonfly algorithm --- ensemble learning --- quantitative analysis modeling --- NIR --- SCiO --- pocket-sized spectrometer --- cheese --- fat --- moisture --- multivariate data analysis --- Fourier-transform near-infrared spectroscopy --- glucose --- fructose --- dry matter --- partial least square regression --- Ewing sarcoma --- Fourier transform infrared spectroscopy --- FTIR --- chemotherapy --- bone cancer --- calibration transfer --- NIR spectroscopy --- PLS --- quantitative analysis model --- melamine --- FT-IR --- NIR spectroscopy --- quantum chemical calculation --- anharmonic calculation --- overtones --- combination bands --- near infrared spectroscopy --- Trichosanthis Fructus --- geographical origin --- chemometric techniques --- crude drugs --- prepared slices --- support vector machine-discriminant analysis --- near-infrared fluorescence --- fluorescent probes --- Zn(II) --- di-(2-picolyl)amine --- living cells --- cellular imaging --- near-infrared (NIR) spectroscopy --- calibration transfer --- affine invariance --- multivariate calibration --- partial least squares (PLS) --- NIR --- direct model transferability --- MicroNIR™ --- SVM --- hier-SVM --- SIMCA --- PLS-DA --- TreeBagger --- PLS --- calibration transfer --- agriculture --- photonics --- imaging --- spectral imaging --- spectroscopy --- handheld near-infrared spectroscopy --- pasta/sauce blends --- partial least squares calibration --- nutritional parameters --- bootstrapping soft shrinkage --- partial least squares --- extra virgin olive oil --- adulteration --- FT-NIR spectroscopy --- near-infrared spectroscopy --- ethanol --- anharmonic quantum mechanical calculations --- isotopic substitution --- overtones --- combinations bands --- seeds vitality --- rice seeds --- near-infrared spectroscopy --- hyperspectral image --- discriminant analysis --- near-infrared spectroscopy --- counter propagation artificial neural network --- detection --- auxiliary diagnosis --- BRAF V600E mutation --- colorectal cancer --- tissue --- paraffin-embedded --- deparaffinized --- stained --- ultra-high performance liquid chromatography --- Folin–Ciocalteu --- total hydroxycinnamic derivatives --- phytoextraction --- near-infrared spectroscopy --- origin traceability --- data fusion --- Paris polyphylla var. yunnanensis --- Fourier transform mid-infrared spectroscopy --- near-infrared spectroscopy --- aquaphotomics --- water --- light --- near infrared spectroscopy --- water-mirror approach --- perturbation --- biomeasurements --- biodiagnosis --- biomonitoring --- Vitis vinifera L. --- proximal sensing --- precision viticulture --- near infrared --- chemometrics --- non-destructive sensor --- NIRS --- osteopathy --- late preterm --- brain --- splanchnic --- Raman spectroscopy --- hyperspectral imaging --- analytical spectroscopy --- counterfeit and substandard pharmaceuticals --- DFT calculations --- chemometrics --- PLSR --- API --- lumefantrine --- artemether --- antimalarial tablets --- FT-NIR spectroscopy --- PLS-R --- water --- glucose --- test set validation --- RMSEP --- hyperspectral image processing --- perfusion measurements --- clinical classifications --- n/a

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english (3)


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2020 (3)