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Spectral analysis of structural deformation - A case study / Alojz Kopacik in Journal of applied geodesy, vol 6 n° 3-4 (November 2012)
[article]
Titre : Spectral analysis of structural deformation - A case study Type de document : Article/Communication Auteurs : Alojz Kopacik, Auteur ; Imrich Liptak, Auteur Année de publication : 2012 Article en page(s) : pp 159 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse spectrale
[Termes IGN] auscultation d'ouvrage
[Termes IGN] Bratislava
[Termes IGN] déformation d'édifice
[Termes IGN] lever tachéométrique
[Termes IGN] pont
[Termes IGN] surveillance d'ouvrage
[Termes IGN] transformation rapide de FourierRésumé : (Auteur) Building structures are extremely sensitive to the influence of outdoor conditions. The most frequent types of outdoor conditions are the influence of wind, sunshine, changes in the temperature of a building's surroundings and, lastly, the effect of a buildings own loading or the improper loading by another source. According to the resonance of the structure with its surroundings, vibrations and oscillations at relatively high frequency intervals (0.1–100.0 Hz) also occur. These phenomena significantly affect the static and dynamic characteristics of structures, as well as their safety and functionality. The paper provides an example of the monitoring of these phenomena using geodetic methods at two different types of structures. The first example is an industrial structure with a cylindrical shape, the monitoring of which was made by a total station with a measuring frequency of approximately 2 Hz. The second example is the Apollo Bridge on the Danube in Bratislava (Slovakia), the steel structure of which was measured by acceleration sensors with a frequency of up to 10 Hz. The central aim of the paper is an analysis of the dynamic behavior of both structures using spectral analysis methods. The use of the Fast Fourier Transform (FFT) and the Lomb–Scargle periodogram is described; the structure's own frequencies and the amplitudes of the structure's oscillations are calculated. Numéro de notice : A2012-596 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : doi.org/10.1515/jag-2012-0023 En ligne : http://www.degruyter.com/view/j/jag.2012.6.issue-3-4/jag-2012-0023/jag-2012-0023 [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32042
in Journal of applied geodesy > vol 6 n° 3-4 (November 2012) . - pp 159 - 166[article]A wavelet spectral analysis technique for automatic detection of geomagnetic sudden commencements / E. Ghamry in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
[article]
Titre : A wavelet spectral analysis technique for automatic detection of geomagnetic sudden commencements Type de document : Article/Communication Auteurs : E. Ghamry, Auteur ; A. Hafez, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 4503 - 4512 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géophysique interne
[Termes IGN] analyse spectrale
[Termes IGN] détection automatique
[Termes IGN] tempête magnétique
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Maximal overlap discrete wavelet transform is used to perform spectral analysis of geomagnetic storm sudden commencements (SCs) (SSCs). This spectral analysis guided us in the development of an automatic SSC detection algorithm. The SC can be an indicator of the onset of a geomagnetic storm; in this case, it is called an SSC. The geomagnetic records used in this study were 3-s resolution data collected from the Circum-Pan Pacific Magnetometer Network. Using such high-resolution data enabled us to achieve a small detection error and short processing time. In addition to these technical merits, we introduce a new algorithm that automatically detects, for the first time, the SC from high-resolution data (sampled at the rate of 1 sample/3 s), unlike previous studies that focused on determining the SSC times automatically using 1-min data. Ninety-three geomagnetic storms were considered for testing the proposed algorithm; it was found that the average and maximum standard deviation of the errors in the detection times determined by the algorithm were 7 and 18 samples, respectively, of the corresponding manually determined arrival times. Numéro de notice : A2012-589 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2192279 Date de publication en ligne : 08/05/2012 En ligne : https://doi.org/110.1109/TGRS.2012.2192279 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32035
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4503 - 4512[article]Estimating urban leaf area index (LAI) of individual trees with hyperspectral data / R. Jensen in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 5 (May 2012)
[article]
Titre : Estimating urban leaf area index (LAI) of individual trees with hyperspectral data Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Hardin, Auteur ; A. Hardin, Auteur Année de publication : 2012 Article en page(s) : pp 495 - 504 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] arbre (flore)
[Termes IGN] feuillu
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] réflectance végétale
[Termes IGN] Utah (Etas-Unis)
[Termes IGN] zone urbaineRésumé : (Auteur) This study estimated leaf area index (LAI) of individual urban trees as a function of spectral features derived from airborne hyperspectral data. Candidate features included spectral indexes, principal components, and calibrated reflectance values. Hyperspectral images were acquired over Provo, Utah area, and LAI of 204 deciduous trees was measured in the field. These tree canopies were identified on the images, and spectral features were extracted using both whole canopy and mean-lit spectra techniques. Multiple regression and artificial neural networks were used to model leaf area and determine which spectral features were most strongly related to it. Results established that simple hyperspectral vegetation indexes explained more variation in urban tree LAI than either principal component scores or simple band reflectance values. The neural network model trained with a subset of those indexes explained more variation in LAI (R2 = 64.8 percent) than any of the multiple regression models tested. Numéro de notice : A2012-234 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.5.495 En ligne : https://doi.org/10.14358/PERS.78.5.495 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31680
in Photogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 5 (May 2012) . - pp 495 - 504[article]Modelos armonicos no lineales para series temporales geodéticas = Non-linear harmonic models for geodetic time series / P.A. Martinez-Ortiz (2011)
Titre : Modelos armonicos no lineales para series temporales geodéticas = Non-linear harmonic models for geodetic time series Type de document : Thèse/HDR Auteurs : P.A. Martinez-Ortiz, Auteur ; J.M. Fernadiz Leal, Directeur de thèse Editeur : Alicante : Escuella politécnica superior Année de publication : 2011 Importance : 402 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Espagnol (spa) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] analyse harmonique
[Termes IGN] analyse spectrale
[Termes IGN] bruit blanc
[Termes IGN] bruit rose
[Termes IGN] géocentre
[Termes IGN] marée terrestre
[Termes IGN] masse de la Terre
[Termes IGN] Matlab
[Termes IGN] modèle non linéaire
[Termes IGN] positionnement par GPS
[Termes IGN] série temporelle
[Termes IGN] système de référence géodésiqueIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The dissertation addresses the development of new methods and software for the spectral analysis of scalar or vectorial time series, with emphasis in the applications of geodetic interest. The starting point can be placed in the method introduced by Harada and Fukushima for the non-linear analysis of time series, which allows the recursive detection of frequencies and their associated amplitudes and phases as well as the secular mixed Fourier terms when found in the signal. That method is extended in different ways, allowing the treatment of series affected by auto correlated noise with a power law, either evenly or unevenly spaced. This is made both at the level of frequency detection and non-linear fitting. Reduction of the computational overhead is also obtained. The theoretical work is accompanied by the developing of comprehensive and specialized software for such non-linear harmonic analysis of time series using the MATLAB programming language. Much of the tools we can find today for analyzing these periodic time series are valid only for certain types whereas the programs in the thesis can be applied to oddly spaced series in the presence of combinations of white and flicker noise. The new methods and routines are used for analyzing some interesting series as those ones describing the celestial pole offsets, the geocentre variations due to the redistribution of water mass on the Earth's surface, the excess of the length of day, continental water flux and the positions of GPS stations, among others. We estimate harmonic models that explain each one of these phenomena in the considered time domain and allow us to draw conclusions of their behavior. Note de contenu : Agradecimientos
Resumen
Abstract
I MODELOS ARMÓNICOS NO LINEALES PARA SE RÍES TEMPORALES GEODÉTICAS
1. Introducción
1.1. Objetivos y metodología
1.2. Contenidos
2. El problema de la detección de señales
2.1. Definición de serie temporal
2.1.1. Componentes de una serie temporal
2.1.2. Componente de ruido
2.2. Técnicas para el estudio de series temporales
2.2.1. Análisis clásico
2.2.2. Análisis espectral
2.2.3. Análisis wavelet
2.3. El problema de la detección de señales
2.3.1. Periodograma de Lomb
2.3.2. Dominio de frecuencia
3. Análisis armónico no lineal
3.1. Introducción
3.2. Descripción del método
3.2.1. Función objetivo y funciones base
3.2.2. Solución mínimo cuadrática
3.2.3. Optimización no lineal: Algoritmo BFGS
3.2.4. Extracción de frecuencias
3.3. Incertidumbre
3.4. Generalización
3.5. Tratamiento de los términos periódicos de corta frecuencia
4. Variaciones del polo celeste
4.1. Modelo de precesión-nutación IAU1980
4.1.1. Introducción
4.1.2. Descripción de los datos
4.1.3. Características del análisis y resultados
4.1.4. Conclusiones
4.2. Modelo de precesión-nutación IAU2000
4.2.1. Introducción
4.2.2. Descripción de los datos
4.2.3. Características del análisis y resultados
4.2.4. Conclusiones
4.3. Modelos dinámicos para la predicción a corto plazo de (óV óe)
5. Variaciones geocéntricas causadas por el flujo de agua continental 101
5.1. Introducción
5.2. Descripción de los datos
5.3. Características del análisis y resultados
5.4. Conclusiones
6. Modelos espacio-temporales para el flujo de agua continental
6.1. Introducción
6.2. Descripción de datos
6.3. Características del análisis
6.4. Resultados
6.5. Conclusiones
7. Estudio armónico del exceso en la duración del día
7.1. Introducción
7.2. Descripción de los datos
7.3. Características del análisis y resultados
7.4. Conclusiones
8. Ruido
8.1. Tipología básica
8.2. Matrices de covarianza
8.2.1. Matriz de covarianzas para un ruido blanco
8.2.2. Matriz de covarianzas para un ruido parpadeante
8.2.3. Matriz de covarianzas para un paseo aleatorio
8.3. Relación ruido-periodograma
8.4. Ruido en las observaciones GPS
9. Algoritmo FHAST
9.1. Introducción
9.2. Función objetivo
9.3. Modelo estocástico
9.3.1. Estimación de un índice espectral
9.3.2. Componente residual como combinación de varios ruidos. Es-timación de la frecuencia de transición,
9.4. Modelo funcional
9.5. Estimación de la componente de varianza
9.5.1. Condición de no negatividad
9.6. Extensión del periodograma
9.6.1. Aceleración del cálculo del periodograma
9.7. Incertidumbre
9.7.1. Parámetros lineales y no lineales del modelo funcional
9.7.2. Parámetros del modelo estocástico
9.8. Criterios de parada algorítmica
9.9. Entramado algorítmico
9.10. Simulación
10.Estudio de las series de posiciones de estaciones GPS
10.1. Introducción
10.2. Análisis de las series temporales residuales
10.3. Resultados y discusión
10.4. Conclusiones
11.Conclusiones y perspectivas
11.1. Conclusiones
11.2. Perspectivas
II EXTENDED SUMMARY: NON-LINEAR HARMO NIC MODELS FOR GEODETIC TIME SERIES
S.1. Introduction
S.2. The signal detection problem
S.2.1. Definition of time series
S.2.2. Spectral analysis
S.2.3. The signal detection problem
S.3. Non-linear harmonic analysis
S.4. Celestial Pole Offsets
S.4.1. IAU1980 Pole Offsets
S.4.2. IAU2000 Pole Offsets
S.4.3. Dynamic models for (óV,óe) prediction
S.5. Geocenter variations caused by continental water flux
S.5.1. Introduction
S.5.2. Data
S.5.3. Analysis and results
S.5.4. Conclusions
S.6. Spatio-temporal models for continental water flux
S.6.1. Introduction
S.6.2. Data
S.6.3. Methods
S.6.4. Results
S.6.5. Conclusions
S.7. Harmonio study of the length of day
S.7.1. Introduction
S.7.2. Data
S.7.3. Analysis and results
S.7.4. Conclusions
S.8. Noise
S.8.1. Typology
S.8.2. Covariance matrices
S.8.3. Relationship noise-periodogram
S.8.4. Noise in GPS observations
S.9. FHAST algorithm
S.9.1. Introduction
S.9.2. Stochastic model
S.9.3. Functional model
S.9.4. Component variance estimation
S.9.5. Modification of the periodogram
S.9.6. Stop criteria
S9.7. Algorithm
S.10. Study of the position time series of GPS stations
S.10.1.Introduction
S.10.2. Analysis and results
S.10.S.Conclusions
S.11 Conclusions and outlook
S.11.1.Conclusions
S.11.2. Outlook
III APÉNDICES
A. Ley de propagación del error
B, Acrónimos, abreviaturas y unidades
C. Modelos armónicos no lineales de algunas estaciones GPS
BibliografíaNuméro de notice : 10519 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : Bibliographie nature-HAL : Thèse DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=45141 Réservation
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Titre : Spectral Feature Selection for Data Mining Type de document : Monographie Auteurs : Zheng Alan Zhao, Auteur ; Huan Liu, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2011 Importance : 224 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-0-429-10719-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] traitement de donnéesRésumé : (éditeur)Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection.
The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its theoretical foundations, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. The authors also cover feature selection and feature extraction, including basic concepts, popular existing algorithms, and applications.
A timely introduction to spectral feature selection, this book illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection.Note de contenu : 1- Data of High Dimensionality and Challenges
2- Univariate Formulations for Spectral Feature Selection
3- Multivariate Formulations
4- Connections to Existing Algorithms
5- Large-Scale Spectral Feature Selection
6- Multi-Source Spectral Feature SelectionNuméro de notice : 25844 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie En ligne : https://www.taylorfrancis.com/books/9780429107191 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95251 Mathématiques pour le traitement du signal / M. Bergounioux (2010)PermalinkPan-European forest/non forest mapping with Landsat ETM+ and Corine Land Cover 2000 data / A. Pekkarinen in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 2 (March - April 2009)PermalinkAnalyse et traitement des signaux / E. Tisserand (2008)PermalinkA cybercartographic framework for audible mapping / Glenn Brauen in Geomatica, vol 61 n° 2 (June 2007)PermalinkLe traitement du signal sous Matlab / André Quinquis (2007)PermalinkMapping the effects of water stress on sphagnum: preliminary observations using airborne remote sensing / A. Harris in Remote sensing of environment, vol 100 n° 3 (15 february 2006)PermalinkEinführung in die Spektral- und Zeitreihenanalyse mit Beispielen aus der Geodäsie / A. Teusch (2006)PermalinkTraitement numérique du signal / M. Bellanger (2006)PermalinkEstimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain / Onisimo Mutanga in International Journal of Remote Sensing IJRS, vol 26 n° 6 (March 2005)PermalinkEstimation of gravity wave momentum flux with spectroscopic imaging / J. Tang in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)Permalink