Search results for "Component analysis"
showing 10 items of 562 documents
Detection of homogeneous precipitation regions at seasonal and annual time scales, northwest Iran
2017
Abstract Detection of homogeneous climate areas is a challenging issue, which can be affected by different criteria. One of the most prominent factors is choosing the time scale, which can lead to different spatial and temporal patterns. Total precipitation is a key factor in climatological studies, and studying its distribution is of utmost importance. The combination of principal components analysis and cluster analysis is used for homogeneous precipitation areas' detection. Hence, the spatial pattern of total precipitation was investigated in northwestern Iran during the past two decades (1991–2010) on seasonal and annual time scales. The results of clustering on each time scale were val…
Temporal variability of airborne bacterial community structure in an urban area
2006
International audience; Temporal airborne bacterial genetic community structure and meteorological factors were analysed above an urban area in the northwest of France from December 2003 to April 2004 with a sampling strategy considering different time intervals (from an hour to a month). Principal component analysis (PCA) of B-ARISA (Bacterial-Automated Ribosomal Intergenic Spacer Analysis) profiles revealed a hierarchy in the temporal variability of bacterial community: daily<weekly<seasonal. Co-inertia analysis between B-ARISA data and meteorological factors demonstrated the correlation between the seasonal variability in the bacterial community and climatic conditions such as temperatur…
REGEOTOP: New climatic data fields for East Asia based on localized relief information and geostatistical methods
2004
Climate data fields represent essential tools for climate, biogeographical and agricultural research to run models and to provide observational data for the verification of global climate models (GCM). Climate data fields are generated through interpolation of observations taken at meteorological stations. Most current interpolation procedures try to describe the influence of topography on spatial climatic variations by relating them directly to absolute elevation or by introducing simple relief variables such as exposure. In both cases this may not properly describe spatial climatic variations, particularly not those of precipitation. This paper describes a regionalization procedure (REGEO…
An Analysis of Regional and Intra-annual Precipitation Variability over Iran using Multivariate Statistical Methods
1998
The temporal and spatial precipitation regime of Iran was analysed using multivariate analyses of monthly mean precipitation records for 71 stations. A Principal Component Analysis was applied to the correlation matrix in order to describe the intra-annual variations of precipitation. The Principal Component scores were mapped to visualize the spatial structure of the three derived precipitation regimes. By applying an agglomerative clustering (WARD) of the three Principal Component scores, five homogeneous spatial clusters, representing five precipitation regions, were developed. The intra-annual types of precipitation distribution, shown by the five clusters, are described and discussed.
Trends of mean temperatures and warm extremes in northern tropical Africa (1961-2014) from observed and PPCA-reconstructed time series
2016
Trends in daily maximum (TX) and minimum (TN) temperatures and indices of warm extremes are studied in tropical North Africa, west of the eastern African highlands, from 1961 to 2014. The analysis is based on the concatenation and cross-checking of two observed databases. Due to the large number of missing entries (~25%), a statistical infilling using probabilistic principal component analysis was applied. Averaged over 90 stations, the linear trends of annual mean TX and TN equal respectively +0.021 °C/yr and +0.028 °C/yr. The frequency of very hot days (TX > 35°C) and tropical nights (TN > 20°C), as well as the frequency of daily TX and TN above the 90th percentile (p90) (“warm days” and …
PCA analysis of wind direction climate in the baltic states
2021
Wind direction is one of the fundamental parameters of weather. In this study we investigate the wind direction climate 10 m above surface level in the Baltic States (Estonia, Latvia, Lithuania). The analysis of wind direction over larger regions is usually hindered by the fact that wind direction is a circular variable, which means that averaged values are meaningless. Here we show how Principal Component Analysis (PCA) can be applied to give a large scale overview of typical wind direction patterns in the region. Here we apply PCA to both observational and reanalysis data. The most significant wind direction patterns are detected in both synoptic scale and mesoscale, and we attempt to lin…
Analysis of the authors’ rights collection frontier using PCA-MDEA: an application to the Valencia region
2002
The aim of this paper is to estimate the authors’ rights collection frontier within the collection zones into which the Valencia Region (Spain) has been divided. To be more exact, a nonparametric frontier technique (Modified Data Envelopment Analysis, MDEA) and the Principal Components Analysis (PCA) are jointly employed to map out an initial approach to the potential authors’ rights collection within this region (as divided into collection zones). The analysis has been carried out both jointly and by majority sectors (performing, musical, and audio-visual arts). Publicaciones Econcult: Área de Investigación en Economía de la Cultura y Turismo. Universitat de València
Detection of steering direction using EEG recordings based on sample entropy and time-frequency analysis.
2016
Monitoring driver's intentions beforehand is an ambitious aim, which will bring a huge impact on the society by preventing traffic accidents. Hence, in this preliminary study we recorded high resolution electroencephalography (EEG) from 5 subjects while driving a car under real conditions along with an accelerometer which detects the onset of steering. Two sensor-level analyses, sample entropy and time-frequency analysis, have been implemented to observe the dynamics before the onset of steering. Thus, in order to classify the steering direction we applied a machine learning algorithm consisting of: dimensionality reduction and classification using principal-component-analysis (PCA) and sup…
An adaptive-PCA algorithm for reflectance estimation from color images
2008
This paper deals with the problem of spectral reflectance estimation from color camera outputs. Because the reconstruction of such functions is an inverse problem, stabilizing the reconstruction process is highly desirable. One way to do this is to decompose reflectance function on a basis functions like PCA. The present work proposes an algorithm making PCA adaptive in reflectance estimation from a color camera output. We propose to adapt the PCA basis derivation by selecting, for each sample, the more relevant elements from the training set elements. The adaptivity criterion is achieved by a likelihood measurement. Finally, the spectral reflectance estimation results are evaluated with th…
Combined spike-related functional MRI and multiple source analysis in the non-invasive spike localization of benign rolandic epilepsy.
2007
Abstract Objective To localize the irritative zone in children by combined spike-related fMRI and EEG multiple source analysis (MSA) in children with benign rolandic epilepsy. Methods Interictal spikes were averaged and localized using MSA, and source locations were displayed in the anatomical 3D-MRI in 11 patients (5–12 yrs, median 10). Interictal spikes were additionally recorded during the fMRI acquisition (EEG-fMRI), and the fMRI sequences were correlated off-line with the EEG spikes. Results MSA revealed an initial central dipole in all patients, including the face or hand area. A second dipolar source was mostly consistent with propagated activity. BOLD activations from EEG-fMRI, cons…