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Sample records for component analysis pca

  1. PCA: Principal Component Analysis for spectra modeling

    Science.gov (United States)

    Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

    2012-07-01

    The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

  2. PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

    Directory of Open Access Journals (Sweden)

    Hermita Bus Umar

    2009-03-01

    Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.

  3. Sequential combination of k-t principle component analysis (PCA) and partial parallel imaging: k-t PCA GROWL.

    Science.gov (United States)

    Qi, Haikun; Huang, Feng; Zhou, Hongmei; Chen, Huijun

    2017-03-01

    k-t principle component analysis (k-t PCA) is a distinguished method for high spatiotemporal resolution dynamic MRI. To further improve the accuracy of k-t PCA, a combination with partial parallel imaging (PPI), k-t PCA/SENSE, has been tested. However, k-t PCA/SENSE suffers from long reconstruction time and limited improvement. This study aims to improve the combination of k-t PCA and PPI on both reconstruction speed and accuracy. A sequential combination scheme called k-t PCA GROWL (GRAPPA operator for wider readout line) was proposed. The GRAPPA operator was performed before k-t PCA to extend each readout line into a wider band, which improved the condition of the encoding matrix in the following k-t PCA reconstruction. k-t PCA GROWL was tested and compared with k-t PCA and k-t PCA/SENSE on cardiac imaging. k-t PCA GROWL consistently resulted in better image quality compared with k-t PCA/SENSE at high acceleration factors for both retrospectively and prospectively undersampled cardiac imaging, with a much lower computation cost. The improvement in image quality became greater with the increase of acceleration factor. By sequentially combining the GRAPPA operator and k-t PCA, the proposed k-t PCA GROWL method outperformed k-t PCA/SENSE in both reconstruction speed and accuracy, suggesting that k-t PCA GROWL is a better combination scheme than k-t PCA/SENSE. Magn Reson Med 77:1058-1067, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  4. Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

    International Nuclear Information System (INIS)

    Jung, Young Mee

    2003-01-01

    Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

  5. k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis

    DEFF Research Database (Denmark)

    Pedersen, Henrik; Kozerke, Sebastian; Ringgaard, Steffen

    2009-01-01

    in applications exhibiting a broad range of temporal frequencies such as free-breathing myocardial perfusion imaging. We show that temporal basis functions calculated by subjecting the training data to principal component analysis (PCA) can be used to constrain the reconstruction such that the temporal resolution...... is improved. The presented method is called k-t PCA....

  6. Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

    Science.gov (United States)

    Ghosh, Debasree; Chattopadhyay, Parimal

    2012-06-01

    The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

  7. Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA

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    Cucu Suhery

    2017-04-01

    Full Text Available Berbagai sistem monitoring presensi yang ada memiliki kekurangan dan kelebihan masing-masing, dan perlu  untuk terus dikembangkan sehingga memudahkan dalam proses pengolahan datanya. Pada penelitian ini dikembangkan suatu sistem monitoring presensi menggunakan deteksi wajah manusia yang diintegrasikan dengan basis data menggunakan bahasa pemrograman Python dan library opencv. Akuisisi data citra dilakukan dengan ponsel android, kemudian citra tersebut dideteksi dan dipotong sehingga hanya didapat bagian wajah saja.  Deteksi wajah menggunakan metode Haar-Cascade Classifier, kemudian ekstraksi fitur dilakukan menggunakan metode Principal Component Analysis (PCA. Hasil dari PCA diberi label sesuai dengan data manusia yang ada pada basis data. Semua citra yang telah memiliki nilai PCA dan tersimpan di basis data akan dicari kemiripannya dengan citra wajah pada proses pengujian menggunakan metoda Euclidian Distance. Pada penelitian ini basis data yang digunakan yaitu MySQL. Hasil deteksi citra wajah pada proses pelatihan memiliki tingkat keberhasilan 100% dan hasil identifikasi wajah pada proses pengujian memiliki tingkat keberhasilan 90%..   Kata kunci— android, haar-cascade classifier, principal component analysis, euclidian distance, MySQL, sistem monitoring presensi, deteksi wajah

  8. Use of principal components analysis (PCA) on estuarine sediment datasets: The effect of data pre-treatment

    International Nuclear Information System (INIS)

    Reid, M.K.; Spencer, K.L.

    2009-01-01

    Principal components analysis (PCA) is a multivariate statistical technique capable of discerning patterns in large environmental datasets. Although widely used, there is disparity in the literature with respect to data pre-treatment prior to PCA. This research examines the influence of commonly reported data pre-treatment methods on PCA outputs, and hence data interpretation, using a typical environmental dataset comprising sediment geochemical data from an estuary in SE England. This study demonstrated that applying the routinely used log (x + 1) transformation skewed the data and masked important trends. Removing outlying samples and correcting for the influence of grain size had the most significant effect on PCA outputs and data interpretation. Reducing the influence of grain size using granulometric normalisation meant that other factors affecting metal variability, including mineralogy, anthropogenic sources and distance along the salinity transect could be identified and interpreted more clearly. - Data pre-treatment can have a significant influence on the outcome of PCA.

  9. A Principal Component Analysis (PCA Approach to Seasonal and Zooplankton Diversity Relationships in Fishing Grounds of Mannar Gulf, India

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    Selvin J. PITCHAIKANI

    2017-06-01

    Full Text Available Principal component analysis (PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. The primary objective of the present study was to record information of zooplankton diversity in a systematic way and to study the variability and relationships among seasons prevailed in Gulf of Mannar. The PCA for the zooplankton seasonal diversity was investigated using the four seasonal datasets to understand the statistical significance among the four seasons. Two different principal components (PC were segregated in all the seasons homogeneously. PCA analyses revealed that Temora turbinata is an opportunistic species and zooplankton diversity was significantly different from season to season and principally, the zooplankton abundance and its dynamics in Gulf of Mannar is structured by seasonal current patterns. The factor loadings of zooplankton for different seasons in Tiruchendur coastal water (GOM is different compared with the Southwest coast of India; particularly, routine and opportunistic species were found within the positive and negative factors. The copepods Acrocalanus gracilis and Acartia erythrea were dominant in summer and Southwest monsoon due to the rainfall and freshwater discharge during the summer season; however, these species were replaced by Temora turbinata during Northeast monsoon season.

  10. Principle Component Analysis with Incomplete Data: A simulation of R pcaMethods package in Constructing an Environmental Quality Index with Missing Data

    Science.gov (United States)

    Missing data is a common problem in the application of statistical techniques. In principal component analysis (PCA), a technique for dimensionality reduction, incomplete data points are either discarded or imputed using interpolation methods. Such approaches are less valid when ...

  11. Estimating the number of components and detecting outliers using Angle Distribution of Loading Subspaces (ADLS) in PCA analysis.

    Science.gov (United States)

    Liu, Y J; Tran, T; Postma, G; Buydens, L M C; Jansen, J

    2018-08-22

    Principal Component Analysis (PCA) is widely used in analytical chemistry, to reduce the dimensionality of a multivariate data set in a few Principal Components (PCs) that summarize the predominant patterns in the data. An accurate estimate of the number of PCs is indispensable to provide meaningful interpretations and extract useful information. We show how existing estimates for the number of PCs may fall short for datasets with considerable coherence, noise or outlier presence. We present here how Angle Distribution of the Loading Subspaces (ADLS) can be used to estimate the number of PCs based on the variability of loading subspace across bootstrap resamples. Based on comprehensive comparisons with other well-known methods applied on simulated dataset, we show that ADLS (1) may quantify the stability of a PCA model with several numbers of PCs simultaneously; (2) better estimate the appropriate number of PCs when compared with the cross-validation and scree plot methods, specifically for coherent data, and (3) facilitate integrated outlier detection, which we introduce in this manuscript. We, in addition, demonstrate how the analysis of different types of real-life spectroscopic datasets may benefit from these advantages of ADLS. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  12. Regionalization and classification of bioclimatic zones in the central-northeastern region of Mexico using principal component analysis (PCA)

    Energy Technology Data Exchange (ETDEWEB)

    Pineda-Martinez, L.F.; Carbajal, N.; Medina-Roldan, E. [Instituto Potosino de Investigacion Cientifica y Tecnologica, A. C., San Luis Potosi (Mexico)]. E-mail: lpineda@ipicyt.edu.mx

    2007-04-15

    Applying principal component analysis (PCA), we determined climate zones in a topographic gradient in the central-northeastern part of Mexico. We employed nearly 30 years of monthly temperature and precipitation data at 173 meteorological stations. The climate classification was carried out applying the Koeppen system modified for the conditions of Mexico. PCA indicates a regionalization in agreement with topographic characteristics and vegetation. We describe the different bioclimatic zones, associated with typical vegetation, for each climate using geographical information systems (GIS). [Spanish] Utilizando un analisis de componentes principales, determinamos zonas climaticas en un gradiente topografico en la zona centro-noreste de Mexico. Se emplearon datos de precipitacion y temperatura medias mensuales por un periodo de 30 anos de 173 estaciones meteorologicas. La clasificacion del clima fue llevada a cabo de acuerdo con el sistema de Koeppen modificado para las condiciones de Mexico. El analisis de componentes principales indico una regionalizacion que concuerda con caracteristicas de topografia y vegetacion. Se describen zonas bioclimaticas, asociadas a vegetacion tipica para cada clima, usando sistemas de informacion geografica (SIG).

  13. Feature Selection pada Dataset Faktor Kesiapan Bencana pada Provinsi di Indonesia Menggunakan Metode PCA (Principal Component Analysis

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    Septa Firmansyah Putra

    2017-01-01

    Full Text Available Penelitian ini bertujuan untuk mengetahui atribut-atribut apa yang akan digunakan untuk klasterisasi provinsi di Indonesia berdasarkan faktor kesiapan dalam menghadapi bencana. Data yang digunakan terdiri dari tiga kelompok data yaitu data jumlah kejadian bencana yang terdiri dari 19 sub-atribut, data jumlah fasilitas kesehatan yang terdiri dari 14 sub-atribut dan data jumlah tenaga kesehatan yang terdiri dari 11 sub atribut. Penelitian ini dapat menjadi gambaran tentang bagaimana melakukan pembersihan dan pemilihan data sebelum digunakan dalam proses klasterisasi. Data-data ini akan dibersihkan dan dipilih sebelum nantinya digunakan pada proses klasterisasi. Proses pembersihan dan pemilihan data dilakukan dengan bantuan PCA (Principal Component Analysis namun sebelumnya dibersihkan telebih dahulu dengan cara manual. Penelitian dibagi menjadi 3 percobaan. Pada percobaan pertama didapatkan 31 sub-atribut yang siap digunakan, percobaan kedua didapatkan 29 sub-atribut yang siap digunakan dan pada percobaan ketiga didapatkan 24 sub-atribut yang siap digunakan.

  14. Investigação da qualidade de farinhas enriquecidas utilizando Análise por Componentes Principais (PCA Enriched flour quality investigation using Principal Component Analysis (PCA

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    Bruno Thiago Soeiro

    2010-09-01

    that corn and wheat flours have to be fortified with folic acid and iron. The main objective of this work was to investigate some enriched flour characteristics using Principal Component Analysis (PCA. Wheat and corn flours presented, on average, acceptable centesimal composition according to the Brazilian Legislation. For the wheat flours investigated, the folic acid concentration was, on average, close to the expected value. Corn flours contained higher amount of vitamin. For both types of flour, the iron content exceeded the label claim of the products. Parameters, such as folic acid, iron, protein, lipids, moisture, ash, and carbohydrates content were present in 30 flour packages purchased from the local market. A matrix with 30 rows and 7 columns was organized and the data was autoscaled. The first information observed was an expected discrimination according to the type of flour. The wheat flours were characterized by high protein, moisture, and ash content. On the other hand, the corn flours had high iron, carbohydrates, lipids, and folic acid. Another important observation was related to the package type. It was noted that the flours packed in plastic bags had less folic acid (152 µg.100 g-1, on average than those packed in paper (259 µg.100 g-1, on average bags. This behavior is probably due to the light incidence during the storage period. This study can be useful to help the governmental authorities in the enriched food program evaluation. In this case, it will be possible to set suitable rules for appropriate food packaging.

  15. [Pattern recognition of decorative papers with different visual characteristics using visible spectroscopy coupled with principal component analysis (PCA)].

    Science.gov (United States)

    Zhang, Mao-mao; Yang, Zhong; Lu, Bin; Liu, Ya-na; Sun, Xue-dong

    2015-02-01

    As one of the most important decorative materials for the modern household products, decorative papers impregnated with melamine not only have better decorative performance, but also could greatly improve the surface properties of materials. However, the appearance quality (such as color-difference evaluation and control) of decorative papers, as an important index for the surface quality of decorative paper, has been a puzzle for manufacturers and consumers. Nowadays, human eye is used to discriminate whether there exist color difference in the factory, which is not only of low efficiency but also prone to bring subjective error. Thus, it is of great significance to find an effective method in order to realize the fast recognition and classification of the decorative papers. In the present study, the visible spectroscopy coupled with principal component analysis (PCA) was used for the pattern recognition of decorative papers with different visual characteristics to investigate the feasibility of visible spectroscopy to rapidly recognize the types of decorative papers. The results showed that the correlation between visible spectroscopy and visual characteristics (L*, a* and b*) was significant, and the correlation coefficients wereup to 0.85 and some was even more than 0. 99, which might suggest that the visible spectroscopy reflected some information about visual characteristics on the surface of decorative papers. When using the visible spectroscopy coupled with PCA to recognize the types of decorative papers, the accuracy reached 94%-100%, which might suggest that the visible spectroscopy was a very potential new method for the rapid, objective and accurate recognition of decorative papers with different visual characteristics.

  16. Preliminary study of the application of Principal Components Analysis (PCA) to the determination of the origin of brandy type vodka

    International Nuclear Information System (INIS)

    Villalobos Chaves, Alberto E.

    2006-01-01

    Principal Components Analysis (PCA) was applied to the determination of the origin of samples of vodkas. Analytical parameters used were: the alcoholic degree, the difference between the alcoholic experimental degree and declared in the etiquette, the dried extract, the relative intensities of calcium atomic emission (beak area at 422,67 nm), sodium (sum of beaks areas Ca, Na / 588,99 and 589,59 nm) and potassium (sum of beaks areas to K/766,49 nm and 769,89 nm) and finally the ultraviolet absorbency to 200 nm. The accumulation of K-averages was used. The hypothesis of item is that the sample was constituted, approximately, for two big natural groupings, this is, national vodkas and foreign vodkas. Of the application of the above mentioned procedure there was obtained that really the components of the sample were distinguishable according to the national or foreign origin in two groups, which ellipses of confidence to 95 % not achieving , even if there were eliminated the variables of alcoholic degree and difference of the alcoholic degree. (author) [es

  17. Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis (CS-PCA): Demonstration in lung tumor tracking.

    Science.gov (United States)

    Dietz, Bryson; Yip, Eugene; Yun, Jihyun; Fallone, B Gino; Wachowicz, Keith

    2017-08-01

    This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis (CS-PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS-PCA algorithm. Using a database of fully sampled k-space, principal components (PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS-PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error (NMSE), as well as the dice coefficients (DC) and centroid displacement of the tumor segmentations. Our results demonstrate that CS-PCA performed superior than CS alone. The CS-PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS-PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710HQ CPU @ 2.5 GHz), depending on the chosen parameters. A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac-MRI system. Our CS-PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The

  18. Climate change adaptation: Uncovering constraints to the use of adaptation strategies among food crop farmers in South-west, Nigeria using principal component analysis (PCA

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    Moradeyo Adebanjo Otitoju

    2016-12-01

    Full Text Available This study focused on the constraints to the use of climate variability/change adaptation strategies in South-west Nigeria. Multistage random technique was employed to select the location and the respondents. Descriptive statistics and principal component analysis (PCA were the analytical tools engaged in this study. The constraints to climate variability and change examined before did not use PCA but generalized factor analysis. Hence, there is need to examine these constraints extensively using PCA. Uncovering the constraints to the use of climate variability/change adaptation strategies among crop framers is important to give a realistic direction in the development of farmer-inclusive climate policies in Nigeria. The PCA result showed that the principal constraints that the farmers faced in climate change adaptation were public, institutional and labour constraint; land, neighbourhood norms and religious beliefs constraint; high cost of inputs, technological and information constraint; farm distance, access to climate information, off-farm job and credit constraint; and poor agricultural programmes and service delivery constraint. These findings pointed out the need for both the government and non-government organizations to intensify efforts on institutional, technological and farmers’ friendly land tenure and information systems as effective measures to guide inclusive climate change adaptation policies and development in South-west Nigeria.

  19. Source apportionment of ambient non-methane hydrocarbons in Hong Kong: application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model.

    Science.gov (United States)

    Guo, H; Wang, T; Louie, P K K

    2004-06-01

    Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source contributions to air pollutant concentrations. In this study, a PCA/APCS model was applied to the data on non-methane hydrocarbons (NMHCs) measured from January to December 2001 at two sampling sites: Tsuen Wan (TW) and Central & Western (CW) Toxic Air Pollutants Monitoring Stations in Hong Kong. This multivariate method enables the identification of major air pollution sources along with the quantitative apportionment of each source to pollutant species. The PCA analysis identified four major pollution sources at TW site and five major sources at CW site. The extracted pollution sources included vehicular internal engine combustion with unburned fuel emissions, use of solvent particularly paints, liquefied petroleum gas (LPG) or natural gas leakage, and industrial, commercial and domestic sources such as solvents, decoration, fuel combustion, chemical factories and power plants. The results of APCS receptor model indicated that 39% and 48% of the total NMHCs mass concentrations measured at CW and TW were originated from vehicle emissions, respectively. 32% and 36.4% of the total NMHCs were emitted from the use of solvent and 11% and 19.4% were apportioned to the LPG or natural gas leakage, respectively. 5.2% and 9% of the total NMHCs mass concentrations were attributed to other industrial, commercial and domestic sources, respectively. It was also found that vehicle emissions and LPG or natural gas leakage were the main sources of C(3)-C(5) alkanes and C(3)-C(5) alkenes while aromatics were predominantly released from paints. Comparison of source contributions to ambient NMHCs at the two sites indicated that the contribution of LPG or natural gas at CW site was almost twice that at TW site. High correlation coefficients (R(2) > 0.8) between the measured and predicted values suggested that the PCA/APCS model was applicable for estimation

  20. Principle component analysis (PCA) for investigation of relationship between population dynamics of microbial pathogenesis, chemical and sensory characteristics in beef slices containing Tarragon essential oil.

    Science.gov (United States)

    Alizadeh Behbahani, Behrooz; Tabatabaei Yazdi, Farideh; Shahidi, Fakhri; Mortazavi, Seyed Ali; Mohebbi, Mohebbat

    2017-04-01

    Principle component analysis (PCA) was employed to examine the effect of the exerted treatments on the beef shelf life as well as discovering the correlations between the studied responses. Considering the variability of the dimensions of the responses, correlation coefficients were applied to form the matrix and extract the eigenvalue. Antimicrobial effect was evaluated on 10 pathogenic microorganisms through the methods of hole-plate diffusion method, disk diffusion method, pour plate method, minimum inhibitory concentration and minimum bactericidal/fungicidal concentration. Antioxidant potential and total phenolic content were examined through the method of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Folin-Ciocalteu method, respectively. The components were identified through gas chromatography and gas chromatography/mass spectrometry. Barhang seed mucilage (BSM) based edible coating containing 0, 0.5, 1 and 1.5% (w/w) Tarragon (T) essential oil mix were applied on beef slices to control the growth of pathogenic microorganisms. Microbiological (total viable count, psychrotrophic count, Escherichia coli, Staphylococcus aureus and fungi), chemical (thiobarbituric acid, peroxide value and pH) and sensory characteristics (odor, color and overall acceptability) analysis measurements were made during the storage periodically. PCA was employed to examine the effect of the exerted treatments on the beef shelf life as well as discovering the correlations between the studied responses. Considering the variability of the dimensions of the responses, correlation coefficients were applied to form the matrix and extract the eigenvalue. The PCA showed that the properties of the uncoated meat samples on the 9th, 12th, 15th and 18th days of storage are continuously changing independent of the exerted treatments on the other samples. This reveals the effect of the exerted treatments on the samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Application of the principal component analysis (PCA) to HVSR data aimed at the seismic characterization of earthquake prone areas

    Science.gov (United States)

    Paolucci, Enrico; Lunedei, Enrico; Albarello, Dario

    2017-10-01

    In this work, we propose a procedure based on principal component analysis on data sets consisting of many horizontal to vertical spectral ratio (HVSR or H/V) curves obtained by single-station ambient vibration acquisitions. This kind of analysis aimed at the seismic characterization of the investigated area by identifying sites characterized by similar HVSR curves. It also allows to extract the typical HVSR patterns of the explored area and to establish their relative importance, providing an estimate of the level of heterogeneity under the seismic point of view. In this way, an automatic explorative seismic characterization of the area becomes possible by only considering ambient vibration data. This also implies that the relevant outcomes can be safely compared with other available information (geological data, borehole measurements, etc.) without any conceptual trade-off. The whole algorithm is remarkably fast: on a common personal computer, the processing time takes few seconds for a data set including 100-200 HVSR measurements. The procedure has been tested in three study areas in the Central-Northern Italy characterized by different geological settings. Outcomes demonstrate that this technique is effective and well correlates with most significant seismostratigraphical heterogeneities present in each of the study areas.

  2. Euler principal component analysis

    NARCIS (Netherlands)

    Liwicki, Stephan; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    Principal Component Analysis (PCA) is perhaps the most prominent learning tool for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2-norm employed by standard PCA is not robust to outliers. In this paper, we propose a kernel PCA method for fast and robust PCA,

  3. Tracking polychlorinated biphenyls (PCBs) congener patterns in Newark Bay surface sediment using principal component analysis (PCA) and positive matrix factorization (PMF).

    Science.gov (United States)

    Saba, Tarek; Su, Steave

    2013-09-15

    PCB congener data for Newark Bay surface sediments were analyzed using PCA and PMF, and relationships between the outcomes from these two techniques were explored. The PCA scores plot separated the Lower Passaic River Mouth samples from North Newark Bay, thus indicating dissimilarity. Although PCA was able to identify subareas in the Bay system with specific PCB congener patterns (e.g., higher chlorinated congeners in Elizabeth River), further conclusions reading potential PCB source profiles or potential upland source areas were not clear for the PCA scores plot. PMF identified five source factors, and explained the Bay sample congener profiles as a mix of these Factors. This PMF solution was equivalent to (1) defining an envelope that encompasses all samples on the PCA scores plot, (2) defining source factors that plot on that envelope, and (3) explaining the congener profile for each Bay sediment sample (inside the scores plot envelope) as a mix of factors. PMF analysis allowed identifying characteristic features in the source factor congener distributions that allowed tracking of source factors to shoreline areas where PCB inputs to the Bay may have originated. The combined analysis from PCA and PMF showed that direct discharges to the Bay are likely the dominant sources of PCBs to the sediment. Review of historical upland activities and regulatory files will be needed, in addition to the PCA and PMF analysis, to fully reconstruct the history of operations and PCB releases around the Newark Bay area that impacted the Bay sediment. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Principle component analysis (PCA) and second-order global hard-modelling for the complete resolution of transition metal ions complex formation with 1,10-phenantroline

    Energy Technology Data Exchange (ETDEWEB)

    Shariati-Rad, Masoud [Faculty of Chemistry, Bu-Ali Sina University, Hamedan 65174 (Iran, Islamic Republic of); Hasani, Masoumeh, E-mail: hasani@basu.ac.ir [Faculty of Chemistry, Bu-Ali Sina University, Hamedan 65174 (Iran, Islamic Republic of)

    2009-08-19

    Second-order global hard-modelling was applied to resolve the complex formation between Co{sup 2+}, Ni{sup 2+}, and Cd{sup 2+} cations and 1,10-phenantroline. The highly correlated spectral and concentration profiles of the species in these systems and low concentration of some species in the individual collected data matrices prevent the well-resolution of the profiles. Therefore, a collection of six equilibrium data matrices including series of absorption spectra taken with pH changes at different reactant ratios were analyzed. Firstly, a precise principle component analysis (PCA) of different augmented arrangements of the individual data matrices was used to distinguish the number of species involved in the equilibria. Based on the results of PCA, the equilibria included in the data were specified and second-order global hard-modelling of the appropriate arrangement of six collected equilibrium data matrices resulted in well-resolved profiles and equilibrium constants. The protonation constant of the ligand (1,10-phenantroline) and spectral profiles of its protonated and unprotonated forms are the additional information obtained by global analysis. For comparison, multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the same data. The results showed that second-order global hard-modelling is more convenient compared with MCR-ALS especially for systems with completely known model. It can completely resolve the system and the concentration profiles which are closer to correct ones. Moreover, parameters showing the goodness of fit are better with second-order global hard-modelling.

  5. Principle component analysis (PCA) and second-order global hard-modelling for the complete resolution of transition metal ions complex formation with 1,10-phenantroline

    International Nuclear Information System (INIS)

    Shariati-Rad, Masoud; Hasani, Masoumeh

    2009-01-01

    Second-order global hard-modelling was applied to resolve the complex formation between Co 2+ , Ni 2+ , and Cd 2+ cations and 1,10-phenantroline. The highly correlated spectral and concentration profiles of the species in these systems and low concentration of some species in the individual collected data matrices prevent the well-resolution of the profiles. Therefore, a collection of six equilibrium data matrices including series of absorption spectra taken with pH changes at different reactant ratios were analyzed. Firstly, a precise principle component analysis (PCA) of different augmented arrangements of the individual data matrices was used to distinguish the number of species involved in the equilibria. Based on the results of PCA, the equilibria included in the data were specified and second-order global hard-modelling of the appropriate arrangement of six collected equilibrium data matrices resulted in well-resolved profiles and equilibrium constants. The protonation constant of the ligand (1,10-phenantroline) and spectral profiles of its protonated and unprotonated forms are the additional information obtained by global analysis. For comparison, multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the same data. The results showed that second-order global hard-modelling is more convenient compared with MCR-ALS especially for systems with completely known model. It can completely resolve the system and the concentration profiles which are closer to correct ones. Moreover, parameters showing the goodness of fit are better with second-order global hard-modelling.

  6. Nonlinear peculiar-velocity analysis and PCA

    Energy Technology Data Exchange (ETDEWEB)

    Dekel, A. [and others

    2001-02-20

    We allow for nonlinear effects in the likelihood analysis of peculiar velocities, and obtain {approximately}35%-lower values for the cosmological density parameter and for the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be of the flat {Lambda}CDM model (h = 0:65, n = 1) with only {Omega}{sub m} free. Since the likelihood is driven by the nonlinear regime, we break the power spectrum at k{sub b} {approximately} 0.2 (h{sup {minus}1} Mpc){sup {minus}1} and fit a two-parameter power-law at k > k{sub b} . This allows for an unbiased fit in the linear regime. Tests using improved mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark III and SFI data {Omega}{sub m} = 0.35 {+-} 0.09 with {sigma}{sub 8}{Omega}P{sub m}{sup 0.6} = 0.55 {+-} 0.10 (90% errors). When allowing deviations from {Lambda}CDM, we find an indication for a wiggle in the power spectrum in the form of an excess near k {approximately} 0.05 and a deficiency at k {approximately} 0.1 (h{sup {minus}1} Mpc){sup {minus}1}--a cold flow which may be related to a feature indicated from redshift surveys and the second peak in the CMB anisotropy. A {chi}{sup 2} test applied to principal modes demonstrates that the nonlinear procedure improves the goodness of fit. The Principal Component Analysis (PCA) helps identifying spatial features of the data and fine-tuning the theoretical and error models. We address the potential for optimal data compression using PCA.

  7. Theoretical analysis of the PCA experiment

    International Nuclear Information System (INIS)

    Minsart, G.

    1980-01-01

    A very brief description of the PCA-PVF facility is given, and the studied configurations are mentioned. The analysis of the experiment has been divided into two parts: study of the fission density distribution across the PCA core and neutronic analysis of the flux spectra and spatial distributions in the whole facility. For both parts, the procedure of calculation is explained: cross section sets, one- and two-dimensional models, group collapsing, choice of bucklings, ... . The obtained results are shortly compared with the measured values, and illustrated by a figure and several tables. The computations of the fission map in the PCA core yield results in good agreement with the experimental ones (within a few percents for nearly all points). The discrepancies observed for relative reaction rates and spectral indices of a series of threshold detectors at the selected locations in and between steel and iron layers in the water reflector are briefly discussed

  8. Comparative Effectiveness of Semantic Feature Analysis (SFA and Phonological Components Analysis (PCA for Anomia Treatment in Persian Speaking Patients With Aphasia

    Directory of Open Access Journals (Sweden)

    Zahra Sadeghi

    2017-09-01

    Discussion: While PCA is more effective for participants with phonological impairments, SFA is more effective for participants with semantic impairments. Therefore, a direct relationship between underlying functional deficit and response to specific treatment was established for all participants.

  9. 24 CFR 401.451 - PAE Physical Condition Analysis (PCA).

    Science.gov (United States)

    2010-04-01

    ... (PCA). 401.451 Section 401.451 Housing and Urban Development Regulations Relating to Housing and Urban... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review... of the project by means of a PCA. If the PAE finds any immediate threats to health and safety, the...

  10. NOAA TIFF Image - 4m Bathymetric Principal Component Analysis (PCA) of Red Snapper Research Areas in the South Atlantic Bight, 2010

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains unified Bathymetric PCA GeoTiffs with 4x4 meter cell resolution describing the topography of 15 areas along the shelf edge off the South...

  11. 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

    Science.gov (United States)

    Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

    2017-09-19

    A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

  12. Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014

    Science.gov (United States)

    Rahayu, Sri; Sugiarto, Teguh; Madu, Ludiro; Holiawati; Subagyo, Ahmad

    2017-01-01

    This study aims to apply the model principal component analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hong Kong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield…

  13. Análise multivariada aplicada na identificação de fármacos antidepressivos. Parte II: Análise por componentes principais (PCA e o método de classificação SIMCA Multivariate analysis to applied in the identification of antidepressants. Part II: principal components analysis (PCA and soft independent modeling of class analogies (SIMCA

    Directory of Open Access Journals (Sweden)

    Janusa Goelzer Sabin

    2004-09-01

    Full Text Available Neste trabalho a identificação e a discriminação de dois diferentes fármacos utilizados como antidepressivos foi estudada, empregando os espectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS, juntamente com a análise de componentes principais (PCA e o método de classificação SIMCA. Os espectros no infravermelho de amostras contendo diferentes concentrações dos princípios ativos cloridrato de amitriptilina e cloridrato de imipramina, foram coletados em um espectrofotômetro NICOLET Magna 550, sendo realizadas 2 réplicas para cada amostra, com resolução de 4 cm-1 e 32 varreduras. A análise de componentes principais confirmou a existência de dois grupos distintos, correspondendo aos dois diferentes princípios ativos utilizados, além de evidenciar a presença de amostras anômalas no conjunto de dados que, possivelmente, iriam interferir na modelagem. Já o método de classificação SIMCA possibilitou o reconhecimento de amostras dos princípios ativos cloridrato de imipramina e cloridrato de amitriptilina com resultados indicando 100% de classificação correta das classes modeladas.In this work the certification of two different drugs used as antidepressants was studied, using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS, together with the analysis of principal components (PCA and the method of soft independent modeling of class analogies (SIMCA. The DRIFT spectra of samples with different concentrations of the active principles amitriptiline and imipramine hydrochlorides had been collected in Magna 550 spectrofotometer, two spectra for each sample, with resolution of 4 cm-1 and 32 scans. The PCA confirmed the existence of two distinct groups, corresponding to the two different active principles used. Otherwise the method of classification SIMCA made possible the recognition of samples of the principles amitriptyline and imipramine hydrochlorides with results indicating

  14. Multiscale principal component analysis

    International Nuclear Information System (INIS)

    Akinduko, A A; Gorban, A N

    2014-01-01

    Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between data projections. This definition opens up more flexibility in the analysis of principal components which is useful in enhancing PCA. In this paper we introduce scales into PCA by maximizing only the sum of pairwise distances between projections for pairs of datapoints with distances within a chosen interval of values [l,u]. The resulting principal component decompositions in Multiscale PCA depend on point (l,u) on the plane and for each point we define projectors onto principal components. Cluster analysis of these projectors reveals the structures in the data at various scales. Each structure is described by the eigenvectors at the medoid point of the cluster which represent the structure. We also use the distortion of projections as a criterion for choosing an appropriate scale especially for data with outliers. This method was tested on both artificial distribution of data and real data. For data with multiscale structures, the method was able to reveal the different structures of the data and also to reduce the effect of outliers in the principal component analysis

  15. Principal component analysis (PCA of volatile terpene compounds dataset emitted by genetically modified sweet orange fruits and juices in which a D-limonene synthase was either up- or down-regulated vs. empty vector controls

    Directory of Open Access Journals (Sweden)

    Ana Rodríguez

    2016-12-01

    Full Text Available We have categorized the dataset from content and emission of terpene volatiles of peel and juice in both Navelina and Pineapple sweet orange cultivars in which D-limonene was either up- (S, down-regulated (AS or non-altered (EV; control (“Impact of D-limonene synthase up- or down-regulation on sweet orange fruit and juice odor perception”(A. Rodríguez, J.E. Peris, A. Redondo, T. Shimada, E. Costell, I. Carbonell, C. Rojas, L. Peña, (2016 [1]. Data from volatile identification and quantification by HS-SPME and GC–MS were classified by Principal Component Analysis (PCA individually or as chemical groups. AS juice was characterized by the higher influence of the oxygen fraction, and S juice by the major influence of ethyl esters. S juices emitted less linalool compared to AS and EV juices.

  16. The Use of Near-Infrared (NIR) Spectroscopy and Principal Component Analysis (PCA) to Discriminate Bark and Wood of the Most Common Species of the Pellet Sector

    DEFF Research Database (Denmark)

    Toscano, Giuseppe; Rinnan, Åsmund; Pizzi, Andrea

    2017-01-01

    related to origin and source, difficult to investigate through traditional analyses, such as the type of wood (hardwood/softwood) and the presence of bark. The development of a rapid technique able to provide this information could be an advantageous tool for the energy sector proving indications....../bark blends (2%-20% (w/w)) were analyzed, indicating the ability of the system to recognize blends from pure material. This study has shown that spectroscopy coupled with multivariate data analysis is a useful tool verifying the compliance of producer declarations and assisting experts in evaluation...

  17. Human Classification Based on Gestural Motions by Using Components of PCA

    International Nuclear Information System (INIS)

    Aziz, Azri A; Wan, Khairunizam; Za'aba, S K; Shahriman A B; Asyekin H; Zuradzman M R; Adnan, Nazrul H

    2013-01-01

    Lately, a study of human capabilities with the aim to be integrated into machine is the famous topic to be discussed. Moreover, human are bless with special abilities that they can hear, see, sense, speak, think and understand each other. Giving such abilities to machine for improvement of human life is researcher's aim for better quality of life in the future. This research was concentrating on human gesture, specifically arm motions for differencing the individuality which lead to the development of the hand gesture database. We try to differentiate the human physical characteristic based on hand gesture represented by arm trajectories. Subjects are selected from different type of the body sizes, and then acquired data undergo resampling process. The results discuss the classification of human based on arm trajectories by using Principle Component Analysis (PCA)

  18. The Feasibility Study for Multigeometries Identification of Uranium Components Using PCA-LSSVM Based on Correlation Measurements

    Directory of Open Access Journals (Sweden)

    Mi Zhou

    2018-01-01

    Full Text Available The geometry of uranium components is one of the key characteristics and strictly confidential. The geometry identification of metal uranium components was studied using 252Cf source-driven correlation measurement method. For the 3 uranium samples with the same mass and enrichment, there are subtle differences in neutron signals. Even worse, the correlation functions were disturbed by scatter neutrons and include “accidental” coincidence, which is not conductive to the geometry identification. In this paper, we proposed an identification method combining principal component analysis and least-square support vector machine (PCA-LSSVM. The results based on PCA-LSSVM showed that the training precision was 100% and the test precision was 95.83% of the identification model. The total precision of the identification model was 98.41%, which indicated that the identification model was an effective way to identify the geometry properties with the correlation functions.

  19. AlleleCoder: a PERL script for coding codominant polymorphism data for PCA analysis

    Science.gov (United States)

    A useful biological interpretation of diploid heterozygotes is in terms of the dose of the common allele (0, 1 or 2 copies). We have developed a PERL script that converts FASTA files into coded spreadsheets suitable for Principal Component Analysis (PCA). In combination with R and R Commander, two- ...

  20. Investigation of domain walls in PPLN by confocal raman microscopy and PCA analysis

    Science.gov (United States)

    Shur, Vladimir Ya.; Zelenovskiy, Pavel; Bourson, Patrice

    2017-07-01

    Confocal Raman microscopy (CRM) is a powerful tool for investigation of ferroelectric domains. Mechanical stresses and electric fields existed in the vicinity of neutral and charged domain walls modify frequency, intensity and width of spectral lines [1], thus allowing to visualize micro- and nanodomain structures both at the surface and in the bulk of the crystal [2,3]. Stresses and fields are naturally coupled in ferroelectrics due to inverse piezoelectric effect and hardly can be separated in Raman spectra. PCA is a powerful statistical method for analysis of large data matrix providing a set of orthogonal variables, called principal components (PCs). PCA is widely used for classification of experimental data, for example, in crystallization experiments, for detection of small amounts of components in solid mixtures etc. [4,5]. In Raman spectroscopy PCA was applied for analysis of phase transitions and provided critical pressure with good accuracy [6]. In the present work we for the first time applied Principal Component Analysis (PCA) method for analysis of Raman spectra measured in periodically poled lithium niobate (PPLN). We found that principal components demonstrate different sensitivity to mechanical stresses and electric fields in the vicinity of the domain walls. This allowed us to separately visualize spatial distribution of fields and electric fields at the surface and in the bulk of PPLN.

  1. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  2. Antineoplastic and immunomodulatory effect of polyphenolic components of Achyranthes aspera (PCA) extract on urethane induced lung cancer in vivo.

    Science.gov (United States)

    Narayan, Chandradeo; Kumar, Arvind

    2014-01-01

    Polyphenolic compounds of Achyranthes aspera (PCA) extract is evaluated for anti-cancerous and cytokine based immunomodulatory effects. The PCA extract contains known components of phenolic acid and flavonoids such as mixture of quinic acid, chlorogenic acid, kaempferol, quercetin and chrysin along with many unknown components. PCA has been orally feed to urethane (ethyl carbamate) primed lung cancerous mice at a dosage of 100 mg/kg body weight for 30 consecutive days. 100 mg powder of A. aspera contains 2.4 mg phenolic acid and 1.1 mg flavonoid (2:1 ratio). Enhanced activities and expression of antioxidant enzymes GST, GR, CAT, SOD, while down regulated expression and activation of LDH enzymes in PCA feed urethane primed lung cancerous tissues as compared to PCA non-feed urethane primed lung cancerous tissues were observed. PCA feed urethane primed lung tissues showed down regulated expression of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α along with TFs, NF-κB and Stat3 while the expression of pro-apoptotic proteins Bax and p53 were enhanced in PCA feed urethane primed lung tissues. FTIR and CD spectroscopy data revealed that PCA resisted the urethane mediated conformational changes of DNA which is evident by the shift in guanine and thymine bands in FTIR from 1,708 to 1,711 cm(-1) and 1,675 to 1,671 cm(-1), respectively in PCA feed urethane primed lung cancerous tissues DNA in comparison to urethane primed lung cancerous tissues DNA. The present study suggests that PCA components have synergistic anti-cancerous and cytokine based immunomodulatory role and DNA conformation restoring effects. However, more research is required to show the effects of each component separately and in combination for effective therapeutic use to cure and prevent lung cancer including other cancers.

  3. Principal Component Analysis as an Efficient Performance ...

    African Journals Online (AJOL)

    This paper uses the principal component analysis (PCA) to examine the possibility of using few explanatory variables (X's) to explain the variation in Y. It applied PCA to assess the performance of students in Abia State Polytechnic, Aba, Nigeria. This was done by estimating the coefficients of eight explanatory variables in a ...

  4. Cost effectiveness analysis of screening in the early diagnosis of prostate cancer (PCA)

    International Nuclear Information System (INIS)

    Mueller-Lisse, U.G.; Mueller-Lisse, U.L.

    2002-01-01

    Purpose. The authors attempted to provide an overview of current concepts and the status of research in the field of cost effectiveness analysis (CEA) of screening for prostate cancer (PCA).Material and methods. Basic concepts and methods of CEA were reviewed. Examples of CEA-related studies of PCA were obtained from pertinent literature through medical databases.Results. Screening for PCA has so far been restricted to limited groups of health care recipients, usually within the framework of clinical trials. In those trials, screening for PCA usually results in higher numbers of PCAs being detected at lower average stages in a given population. As a consequence of screening, the rate of potentially curable PCAs increases. However, it has not yet been demonstrated that screening for PCA decreases PCA-related mortality or morbidity from metastatic PCA. On the other hand, additional costs are associated with the screening measure and with increased use of resources for diagnosis and treatment of the additional PCAs detected through screening.Conclusions. Throughout the European Union and North America, mass screening for PCA has not been implemented. This may chiefly be due to the current lack of information on long term benefits of PCA screening, particularly disease-specific survival. Currently, major studies are underway to assess the effects of PCA screening and its cost effectiveness. These studies include the US-American prostate, lung, colon and ovary trials (PLCO) and the European randomised study of Screening for Prostate Cancer (ERSPC). (orig.) [de

  5. Comparative analysis of the PCA3 gene expression in sediments and exosomes isolated from urine

    Directory of Open Access Journals (Sweden)

    D. S. Mikhaylenko

    2017-01-01

    Full Text Available Introduction. Prostate cancer (PCa is one of the common oncological diseases in men. Expression of the PCA3 gene in urine is currently used as a molecular genetic marker of PCa.Objective: to comparative analysis of the PCA3 expression in urine sediments and exosomes for the determination of the biomaterial, which allows detecting the PCA3 expression in more efficient manner.Materials and methods. The 12 patients with different stages of PCa and 8 control samples were examined.Results. The diagnostic accuracy of the PCA3 gene expression analysis in this cohort exceeded 90 %. We had not obtained significant differences in the sensitivity and specificity of the PCA3 hyperexpression in the urine sediments compared with exosomes. This result indicates in favor to using urine sediment for the PCA3 analysis as a biomaterial with less time-consuming sample preparation, although the possible advantage of exosomes for the analysis of the expression marker panels requires further studies.

  6. Constrained principal component analysis and related techniques

    CERN Document Server

    Takane, Yoshio

    2013-01-01

    In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concre

  7. Sparse Principal Component Analysis in Medical Shape Modeling

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

    2006-01-01

    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

  8. Integrating Data Transformation in Principal Components Analysis

    KAUST Repository

    Maadooliat, Mehdi; Huang, Jianhua Z.; Hu, Jianhua

    2015-01-01

    Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior

  9. NEPR Principle Component Analysis - NOAA TIFF Image

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This GeoTiff is a representation of seafloor topography in Northeast Puerto Rico derived from a bathymetry model with a principle component analysis (PCA). The area...

  10. Principal Component Analysis: Most Favourite Tool in Chemometrics

    Indian Academy of Sciences (India)

    Abstract. Principal component analysis (PCA) is the most commonlyused chemometric technique. It is an unsupervised patternrecognition technique. PCA has found applications in chemistry,biology, medicine and economics. The present work attemptsto understand how PCA work and how can we interpretits results.

  11. Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

    Science.gov (United States)

    Tsai, Jinn-Tsong; Chou, Ping-Yi; Chou, Jyh-Horng

    2015-11-01

    The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.

  12. Nonlinear principal component analysis and its applications

    CERN Document Server

    Mori, Yuichi; Makino, Naomichi

    2016-01-01

    This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...

  13. Parallel GPU implementation of iterative PCA algorithms.

    Science.gov (United States)

    Andrecut, M

    2009-11-01

    Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. For large data sets, the common approach to PCA computation is based on the standard NIPALS-PCA algorithm, which unfortunately suffers from loss of orthogonality, and therefore its applicability is usually limited to the estimation of the first few components. Here we present an algorithm based on Gram-Schmidt orthogonalization (called GS-PCA), which eliminates this shortcoming of NIPALS-PCA. Also, we discuss the GPU (Graphics Processing Unit) parallel implementation of both NIPALS-PCA and GS-PCA algorithms. The numerical results show that the GPU parallel optimized versions, based on CUBLAS (NVIDIA), are substantially faster (up to 12 times) than the CPU optimized versions based on CBLAS (GNU Scientific Library).

  14. Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

    OpenAIRE

    Wang, Quan

    2012-01-01

    Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied ...

  15. Use of Sparse Principal Component Analysis (SPCA) for Fault Detection

    DEFF Research Database (Denmark)

    Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet

    2016-01-01

    Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...

  16. Coarse-to-fine markerless gait analysis based on PCA and Gauss-Laguerre decomposition

    Science.gov (United States)

    Goffredo, Michela; Schmid, Maurizio; Conforto, Silvia; Carli, Marco; Neri, Alessandro; D'Alessio, Tommaso

    2005-04-01

    Human movement analysis is generally performed through the utilization of marker-based systems, which allow reconstructing, with high levels of accuracy, the trajectories of markers allocated on specific points of the human body. Marker based systems, however, show some drawbacks that can be overcome by the use of video systems applying markerless techniques. In this paper, a specifically designed computer vision technique for the detection and tracking of relevant body points is presented. It is based on the Gauss-Laguerre Decomposition, and a Principal Component Analysis Technique (PCA) is used to circumscribe the region of interest. Results obtained on both synthetic and experimental tests provide significant reduction of the computational costs, with no significant reduction of the tracking accuracy.

  17. Dimensionality Reduction Methods: Comparative Analysis of methods PCA, PPCA and KPCA

    Directory of Open Access Journals (Sweden)

    Jorge Arroyo-Hernández

    2016-01-01

    Full Text Available The dimensionality reduction methods are algorithms mapping the set of data in subspaces derived from the original space, of fewer dimensions, that allow a description of the data at a lower cost. Due to their importance, they are widely used in processes associated with learning machine. This article presents a comparative analysis of PCA, PPCA and KPCA dimensionality reduction methods. A reconstruction experiment of worm-shape data was performed through structures of landmarks located in the body contour, with methods having different number of main components. The results showed that all methods can be seen as alternative processes. Nevertheless, thanks to the potential for analysis in the features space and the method for calculation of its preimage presented, KPCA offers a better method for recognition process and pattern extraction

  18. Preliminary identification of unicellular algal genus by using combined confocal resonance Raman spectroscopy with PCA and DPLS analysis

    Science.gov (United States)

    He, Shixuan; Xie, Wanyi; Zhang, Ping; Fang, Shaoxi; Li, Zhe; Tang, Peng; Gao, Xia; Guo, Jinsong; Tlili, Chaker; Wang, Deqiang

    2018-02-01

    The analysis of algae and dominant alga plays important roles in ecological and environmental fields since it can be used to forecast water bloom and control its potential deleterious effects. Herein, we combine in vivo confocal resonance Raman spectroscopy with multivariate analysis methods to preliminary identify the three algal genera in water blooms at unicellular scale. Statistical analysis of characteristic Raman peaks demonstrates that certain shifts and different normalized intensities, resulting from composition of different carotenoids, exist in Raman spectra of three algal cells. Principal component analysis (PCA) scores and corresponding loading weights show some differences from Raman spectral characteristics which are caused by vibrations of carotenoids in unicellular algae. Then, discriminant partial least squares (DPLS) classification method is used to verify the effectiveness of algal identification with confocal resonance Raman spectroscopy. Our results show that confocal resonance Raman spectroscopy combined with PCA and DPLS could handle the preliminary identification of dominant alga for forecasting and controlling of water blooms.

  19. Recent progresses of neural network unsupervised learning: I. Independent component analyses generalizing PCA

    Science.gov (United States)

    Szu, Harold H.

    1999-03-01

    The early vision principle of redundancy reduction of 108 sensor excitations is understandable from computer vision viewpoint toward sparse edge maps. It is only recently derived using a truly unsupervised learning paradigm of artificial neural networks (ANN). In fact, the biological vision, Hubel- Wiesel edge maps, is reproduced seeking the underlying independent components analyses (ICA) among 102 image samples by maximizing the ANN output entropy (partial)H(V)/(partial)[W] equals (partial)[W]/(partial)t. When a pair of newborn eyes or ears meet the bustling and hustling world without supervision, they seek ICA by comparing 2 sensory measurements (x1(t), x2(t))T equalsV X(t). Assuming a linear and instantaneous mixture model of the external world X(t) equals [A] S(t), where both the mixing matrix ([A] equalsV [a1, a2] of ICA vectors and the source percentages (s1(t), s2(t))T equalsV S(t) are unknown, we seek the independent sources approximately equals [I] where the approximated sign indicates that higher order statistics (HOS) may not be trivial. Without a teacher, the ANN weight matrix [W] equalsV [w1, w2] adjusts the outputs V(t) equals tanh([W]X(t)) approximately equals [W]X(t) until no desired outputs except the (Gaussian) 'garbage' (neither YES '1' nor NO '-1' but at linear may-be range 'origin 0') defined by Gaussian covariance G equals [I] equals [W][A] the internal knowledge representation [W], as the inverse of the external world matrix [A]-1. To unify IC, PCA, ANN & HOS theories since 1991 (advanced by Jutten & Herault, Comon, Oja, Bell-Sejnowski, Amari-Cichocki, Cardoso), the LYAPONOV function L(v1,...,vn, w1,...wn,) equals E(v1,...,vn) - H(w1,...wn) is constructed as the HELMHOTZ free energy to prove both convergences of supervised energy E and unsupervised entropy H learning. Consequently, rather using the faithful but dumb computer: 'GARBAGE-IN, GARBAGE-OUT,' the smarter neurocomputer will be equipped with an unsupervised learning that extracts

  20. Principal components analysis in clinical studies.

    Science.gov (United States)

    Zhang, Zhongheng; Castelló, Adela

    2017-09-01

    In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

  1. Simultaneous Estimation of Hydrochlorothiazide, Hydralazine Hydrochloride, and Reserpine Using PCA, NAS, and NAS-PCA.

    Science.gov (United States)

    Sharma, Chetan; Badyal, Pragya Nand; Rawal, Ravindra K

    2015-01-01

    In this study, new and feasible UV-visible spectrophotometric and multivariate spectrophotometric methods were described for the simultaneous determination of hydrochlorothiazide (HCTZ), hydralazine hydrochloride (H.HCl), and reserpine (RES) in combined pharmaceutical tablets. Methanol was used as a solvent for analysis and the whole UV region was scanned from 200-400 nm. The resolution was obtained by using multivariate methods such as the net analyte signal method (NAS), principal component analysis (PCA), and net analyte signal-principal component analysis (NAS-PCA) applied to the UV spectra of the mixture. The results obtained from all of the three methods were compared. NAS-PCA showed a lot of resolved data as compared to NAS and PCA. Thus, the NAS-PCA technique is a combination of NAS and PCA methods which is advantageous to obtain the information from overlapping results.

  2. Controversies in using urine samples for prostate cancer detection: PSA and PCA3 expression analysis

    Directory of Open Access Journals (Sweden)

    S. Fontenete

    2011-12-01

    Full Text Available PURPOSE: Prostate cancer (PCa is one of the most commonly diagnosed malignancies in the world. Although PSA utilization as a serum marker has improved prostate cancer detection it still presents some limitations, mainly regarding its specificity. The expression of this marker, along with the detection of PCA3 mRNA in urine samples, has been suggested as a new approach for PCa detection. The goal of this work was to evaluate the efficacy of the urinary detection of PCA3 mRNA and PSA mRNA without performing the somewhat embarrassing prostate massage. It was also intended to optimize and implement a methodological protocol for this kind of sampling. MATERIALS AND METHODS: Urine samples from 57 patients with suspected prostate disease were collected, without undergoing prostate massage. Increased serum PSA levels were confirmed by medical records review. RNA was extracted by different methods and a preamplification step was included in order to improve gene detection by Real-Time PCR. RESULTS: An increase in RNA concentration with the use of TriPure Isolation Reagent. Despite this optimization, only 15.8% of the cases showed expression of PSA mRNA and only 3.8% of prostate cancer patients presented detectable levels of PCA3 mRNA. The use of a preamplification step revealed no improvement in the results obtained. CONCLUSION: This work confirms that prostate massage is important before urine collection for gene expression analysis. Since PSA and PCA3 are prostate specific, it is necessary to promote the passage of cells from prostate to urinary tract, in order to detect these genetic markers in urine samples.

  3. Principal component analysis of image gradient orientations for face recognition

    NARCIS (Netherlands)

    Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data

  4. Retrieving the correlation matrix from a truncated PCA solution : The inverse principal component problem

    NARCIS (Netherlands)

    ten Berge, Jos M.F.; Kiers, Henk A.L.

    When r Principal Components are available for k variables, the correlation matrix is approximated in the least squares sense by the loading matrix times its transpose. The approximation is generally not perfect unless r = k. In the present paper it is shown that, when r is at or above the Ledermann

  5. COPD phenotype description using principal components analysis

    DEFF Research Database (Denmark)

    Roy, Kay; Smith, Jacky; Kolsum, Umme

    2009-01-01

    BACKGROUND: Airway inflammation in COPD can be measured using biomarkers such as induced sputum and Fe(NO). This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal components analysis (PCA). SUBJECTS...... AND METHODS: In 127 COPD patients (mean FEV1 61%), pulmonary function, Fe(NO), plasma CRP and TNF-alpha, sputum differential cell counts and sputum IL8 (pg/ml) were measured. Principal components analysis as well as multivariate analysis was performed. RESULTS: PCA identified four main components (% variance...... associations between the variables within components 1 and 2. CONCLUSION: COPD is a multi dimensional disease. Unrelated components of disease were identified, including neutrophilic airway inflammation which was associated with systemic inflammation, and sputum eosinophils which were related to increased Fe...

  6. A Genealogical Interpretation of Principal Components Analysis

    Science.gov (United States)

    McVean, Gil

    2009-01-01

    Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557

  7. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho

    2010-09-01

    We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

  8. Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.

    2017-01-01

    Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA...

  9. PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS

    Energy Technology Data Exchange (ETDEWEB)

    Correia, C.; Medeiros, J. R. De [Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, 59072-970, Natal (Brazil); Lazarian, A. [Astronomy Department, University of Wisconsin, Madison, 475 N. Charter St., WI 53711 (United States); Burkhart, B. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St, MS-20, Cambridge, MA 02138 (United States); Pogosyan, D., E-mail: caioftc@dfte.ufrn.br [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON (Canada)

    2016-02-20

    In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information.

  10. PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS

    International Nuclear Information System (INIS)

    Correia, C.; Medeiros, J. R. De; Lazarian, A.; Burkhart, B.; Pogosyan, D.

    2016-01-01

    In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information

  11. Integrating Data Transformation in Principal Components Analysis

    KAUST Repository

    Maadooliat, Mehdi

    2015-01-02

    Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.

  12. Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

    Directory of Open Access Journals (Sweden)

    Chang Liu

    2014-01-01

    Full Text Available To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA, incremental principal component analysis (IPCA, and multilinear principal component analysis (MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.

  13. Volatilization from PCA steel alloy

    Energy Technology Data Exchange (ETDEWEB)

    Hagrman, D.L.; Smolik, G.R.; McCarthy, K.A.; Petti, D.A.

    1996-08-01

    The mobilizations of key components from Primary Candidate Alloy (PCA) steel alloy have been measured with laboratory-scale experiments. The experiments indicate most of the mobilization from PCA steel is due to oxide formation and spalling but that the spalled particles are large enough to settle rapidly. Based on the experiments, models for the volatization of iron, manganese, and cobalt from PCA steel in steam and molybdenum from PCA steel in air have been derived.

  14. Group-wise Principal Component Analysis for Exploratory Data Analysis

    NARCIS (Netherlands)

    Camacho, J.; Rodriquez-Gomez, Rafael A.; Saccenti, E.

    2017-01-01

    In this paper, we propose a new framework for matrix factorization based on Principal Component Analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new

  15. Probabilistic Principal Component Analysis for Metabolomic Data.

    LENUS (Irish Health Repository)

    Nyamundanda, Gift

    2010-11-23

    Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

  16. Priority of VHS Development Based in Potential Area using Principal Component Analysis

    Science.gov (United States)

    Meirawan, D.; Ana, A.; Saripudin, S.

    2018-02-01

    The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

  17. On Bayesian Principal Component Analysis

    Czech Academy of Sciences Publication Activity Database

    Šmídl, Václav; Quinn, A.

    2007-01-01

    Roč. 51, č. 9 (2007), s. 4101-4123 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Principal component analysis ( PCA ) * Variational bayes (VB) * von-Mises–Fisher distribution Subject RIV: BC - Control Systems Theory Impact factor: 1.029, year: 2007 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4MYD60N-6&_user=10&_coverDate=05%2F15%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b8ea629d48df926fe18f9e5724c9003a

  18. Cluster analysis of commercial samples of Bauhinia spp. using HPLC-UV/PDA and MCR-ALS/PCA without peak alignment procedure.

    Science.gov (United States)

    Ardila, Jorge Armando; Funari, Cristiano Soleo; Andrade, André Marques; Cavalheiro, Alberto José; Carneiro, Renato Lajarim

    2015-01-01

    Bauhinia forficata Link. is recognised by the Brazilian Health Ministry as a treatment of hypoglycemia and diabetes. Analytical methods are useful to assess the plant identity due the similarities found in plants from Bauhinia spp. HPLC-UV/PDA in combination with chemometric tools is an alternative widely used and suitable for authentication of plant material, however, the shifts of retention times for similar compounds in different samples is a problem. To perform comparisons between the authentic medicinal plant (Bauhinia forficata Link.) and samples commercially available in drugstores claiming to be "Bauhinia spp. to treat diabetes" and to evaluate the performance of multivariate curve resolution - alternating least squares (MCR-ALS) associated to principal component analysis (PCA) when compared to pure PCA. HPLC-UV/PDA data obtained from extracts of leaves were evaluated employing a combination of MCR-ALS and PCA, which allowed the use of the full chromatographic and spectrometric information without the need of peak alignment procedures. The use of MCR-ALS/PCA showed better results than the conventional PCA using only one wavelength. Only two of nine commercial samples presented characteristics similar to the authentic Bauhinia forficata spp., considering the full HPLC-UV/PDA data. The combination of MCR-ALS and PCA is very useful when applied to a group of samples where a general alignment procedure could not be applied due to the different chromatographic profiles. This work also demonstrates the need of more strict control from the health authorities regarding herbal products available on the market. Copyright © 2015 John Wiley & Sons, Ltd.

  19. Nonlinear PCA: characterizing interactions between modes of brain activity.

    OpenAIRE

    Friston, K; Phillips, J; Chawla, D; Büchel, C

    2000-01-01

    This paper presents a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time-series. The critical aspect of this technique is that, in relation to conventional PCA, the sources can interact to produce (second-order) spatial modes that represent the modulation of one (first-order) spatial mode by another. This nonlinear PCA uses a simple neural network architecture that embodies a spec...

  20. PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

    Directory of Open Access Journals (Sweden)

    Kanokmon Rujirakul

    2014-01-01

    Full Text Available Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

  1. PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.

    Science.gov (United States)

    Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar

    2014-01-01

    Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

  2. Principal component analysis of 1/fα noise

    International Nuclear Information System (INIS)

    Gao, J.B.; Cao Yinhe; Lee, J.-M.

    2003-01-01

    Principal component analysis (PCA) is a popular data analysis method. One of the motivations for using PCA in practice is to reduce the dimension of the original data by projecting the raw data onto a few dominant eigenvectors with large variance (energy). Due to the ubiquity of 1/f α noise in science and engineering, in this Letter we study the prototypical stochastic model for 1/f α processes--the fractional Brownian motion (fBm) processes using PCA, and find that the eigenvalues from PCA of fBm processes follow a power-law, with the exponent being the key parameter defining the fBm processes. We also study random-walk-type processes constructed from DNA sequences, and find that the eigenvalue spectrum from PCA of those random-walk processes also follow power-law relations, with the exponent characterizing the correlation structures of the DNA sequence. In fact, it is observed that PCA can automatically remove linear trends induced by patchiness in the DNA sequence, hence, PCA has a similar capability to the detrended fluctuation analysis. Implications of the power-law distributed eigenvalue spectrum are discussed

  3. Source Signals Separation and Reconstruction Following Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    WANG Cheng

    2014-02-01

    Full Text Available For separation and reconstruction of source signals from observed signals problem, the physical significance of blind source separation modal and independent component analysis is not very clear, and its solution is not unique. Aiming at these disadvantages, a new linear and instantaneous mixing model and a novel source signals separation reconstruction solving method from observed signals based on principal component analysis (PCA are put forward. Assumption of this new model is statistically unrelated rather than independent of source signals, which is different from the traditional blind source separation model. A one-to-one relationship between linear and instantaneous mixing matrix of new model and linear compound matrix of PCA, and a one-to-one relationship between unrelated source signals and principal components are demonstrated using the concept of linear separation matrix and unrelated of source signals. Based on this theoretical link, source signals separation and reconstruction problem is changed into PCA of observed signals then. The theoretical derivation and numerical simulation results show that, in despite of Gauss measurement noise, wave form and amplitude information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal and normalized; only wave form information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal but not normalized, unrelated source signal cannot be separated and reconstructed by PCA when mixing matrix is not column orthogonal or linear.

  4. Denoising by semi-supervised kernel PCA preimaging

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2014-01-01

    Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

  5. Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy

    International Nuclear Information System (INIS)

    Jesse, Stephen; Kalinin, Sergei V

    2009-01-01

    An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

  6. Principal Component Analysis In Radar Polarimetry

    Directory of Open Access Journals (Sweden)

    A. Danklmayer

    2005-01-01

    Full Text Available Second order moments of multivariate (often Gaussian joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix. In Radar Polarimetry the application of the covariance matrix is known as target decomposition theory, which is a special application of the extremely versatile Principle Component Analysis (PCA. The basic idea of PCA is to convert a data set, consisting of correlated random variables into a new set of uncorrelated variables and order the new variables according to the value of their variances. It is important to stress that uncorrelatedness does not necessarily mean independent which is used in the much stronger concept of Independent Component Analysis (ICA. Both concepts agree for multivariate Gaussian distribution functions, representing the most random and least structured distribution. In this contribution, we propose a new approach in applying the concept of PCA to Radar Polarimetry. Therefore, new uncorrelated random variables will be introduced by means of linear transformations with well determined loading coefficients. This in turn, will allow the decomposition of the original random backscattering target variables into three point targets with new random uncorrelated variables whose variances agree with the eigenvalues of the covariance matrix. This allows a new interpretation of existing decomposition theorems.

  7. SVD vs PCA: Comparison of Performance in an Imaging Spectrometer

    Directory of Open Access Journals (Sweden)

    Wilma Oblefias

    2004-12-01

    Full Text Available The calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular-value decomposition (SVD and principal component analysis (PCA in terms of estimation performance with respect to resolution, presence of noise, intensity variation, and quantization error. Results show that SVD is robust in intensity variation while PCA is not. However, PCA performs better with signals of low signal-to-noise ratio. No significant difference is seen between SVD and PCA in terms of resolution and quantization error.

  8. Towards Cognitive Component Analysis

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Ahrendt, Peter; Larsen, Jan

    2005-01-01

    Cognitive component analysis (COCA) is here defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. We have earlier demonstrated that independent components analysis is relevant for representing...

  9. Visualizing solvent mediated phase transformation behavior of carbamazepine polymorphs by principal component analysis

    DEFF Research Database (Denmark)

    Tian, Fang; Rades, Thomas; Sandler, Niklas

    2008-01-01

    The purpose of this research is to gain a greater insight into the hydrate formation processes of different carbamazepine (CBZ) anhydrate forms in aqueous suspension, where principal component analysis (PCA) was applied for data analysis. The capability of PCA to visualize and to reveal simplified...

  10. An application of principal component analysis to the clavicle and clavicle fixation devices.

    LENUS (Irish Health Repository)

    Daruwalla, Zubin J

    2010-01-01

    Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories.

  11. An application of principal component analysis to the clavicle and clavicle fixation devices

    OpenAIRE

    Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

    2010-01-01

    Abstract Background Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and method...

  12. Semi-Supervised Kernel PCA

    DEFF Research Database (Denmark)

    Walder, Christian; Henao, Ricardo; Mørup, Morten

    We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

  13. Efficient real time OD matrix estimation based on principal component analysis

    NARCIS (Netherlands)

    Djukic, T.; Flötteröd, G.; Van Lint, H.; Hoogendoorn, S.P.

    2012-01-01

    In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy.

  14. Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

    Science.gov (United States)

    Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

    2013-06-01

    Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

  15. Fundamental flow and fracture analysis of prime candidate alloy (PCA) for path a (austenitics)

    International Nuclear Information System (INIS)

    Lucas, G.E.; Jayakumar, M.; Maziasz, P.J.

    1982-01-01

    Room temperature microhardness tests have been performed on samples of Prime Candidate Alloy (PCA) for the austenitics (Path A) subjected to various thermomechanical treatments (TMT). The TMTs have effected various microstructures, which have been well characterized by optical metallography and TEM. For comparison, microhardness tests have been performed on samples of N-lot, DO heat and MFE 316 stainless steel with similar TMTs. The results indicate that the TMTs investigated can significantly alter the microhardness of the PCA in a manner which is consistent with microstructural changes. Moreover, while PCA had the lowest microhardness of the four alloys types after cold working, its microhardness increased while the others decreased to comparable values after aging for 2 h at 750 0 C

  16. Kernel principal component analysis for change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Morton, J.C.

    2008-01-01

    region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

  17. Radar fall detection using principal component analysis

    Science.gov (United States)

    Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

    2016-05-01

    Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

  18. Sparse principal component analysis in medical shape modeling

    Science.gov (United States)

    Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

    2006-03-01

    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

  19. Elemental concentration analysis in PCa, BPH and normal prostate tissues using SR-TXRF

    International Nuclear Information System (INIS)

    Leitao, Roberta G.; Anjos, Marcelino J.; Canellas, Catarine G.L.; Lopes, Ricardo T.

    2009-01-01

    Prostate cancer (PCa) is one of the main causes of illness and death all over the world. In Brazil, prostate cancer currently represents the second most prevalent malignant neoplasia in men, representing 21% of all cancer cases. Benign Prostate Hyperplasia (BPH) is an illness prevailing in men above the age of 50, close to 90% after the age of 80. The prostate presents a high zinc concentration, about 10-fold higher than any other body tissue. In this work, samples of human prostate tissues with cancer (PCa), BPH and normal tissue were analyzed utilizing the total reflection X-ray fluorescence spectroscopy using synchrotron radiation technique (SRTXRF) to investigate the differences in the elemental concentrations in these tissues. SR-TXRF analyses were performed at the X-Ray fluorescence beamline at Brazilian National Synchrotron Light Laboratory (LNLS), in Campinas, Sao Paulo. It was possible to determine the concentrations of the following elements: P, S, K, Ca, Fe, Cu, Zn, Br and Rb. By using Mann-Whitney U test it was observed that almost all elements presented concentrations with significant differences α = 0.05) between the groups studied. The elements and groups were: S, K, Ca, Fe, Zn, Br and Rb (PCa X Normal); S, Fe, Zn and Br (PCa X BPH); K, Ca, Fe, Zn, Br and Rb (BPH X Normal). (author)

  20. Scalable Robust Principal Component Analysis Using Grassmann Averages

    DEFF Research Database (Denmark)

    Hauberg, Søren; Feragen, Aasa; Enficiaud, Raffi

    2016-01-01

    In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortu...

  1. Stability and chaos of LMSER PCA learning algorithm

    International Nuclear Information System (INIS)

    Lv Jiancheng; Y, Zhang

    2007-01-01

    LMSER PCA algorithm is a principal components analysis algorithm. It is used to extract principal components on-line from input data. The algorithm has both stability and chaotic dynamic behavior under some conditions. This paper studies the local stability of the LMSER PCA algorithm via a corresponding deterministic discrete time system. Conditions for local stability are derived. The paper also explores the chaotic behavior of this algorithm. It shows that the LMSER PCA algorithm can produce chaos. Waveform plots, Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior of this algorithm

  2. Fast principal component analysis for stacking seismic data

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-04-01

    Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

  3. Demixed principal component analysis of neural population data.

    Science.gov (United States)

    Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

    2016-04-12

    Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

  4. EEG frequency PCA in EEG-ERP dynamics.

    Science.gov (United States)

    Barry, Robert J; De Blasio, Frances M

    2018-05-01

    Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

  5. Análise exploratória de adoçantes de mesa via espectroscopia no infravermelho (FTIR e análise por componentes principais (ACP Exploratory analysis of commercial sweeteners by infrared spectroscopy (FTIR and principal component analysis (PCA

    Directory of Open Access Journals (Sweden)

    Adriana Tozetto

    2007-12-01

    Full Text Available Nos últimos vinte anos, o consumo de alimentos diet e light tem aumentado sistematicamente, o que tem propiciado o constante desenvolvimento de produtos desse gênero. Grande ênfase tem sido dada àqueles produtos que substituem sacarose por edulcorantes de baixos conteúdos calóricos ou não calóricos. Seguindo esta tendência, adoçantes de mesa têm sido desenvolvidos variando-se amplamente o veículo e o tipo de edulcorante empregado. Neste trabalho, a análise de componentes principais associada à espectroscopia na região do infravermelho médio foi utilizada com sucesso para diferenciar os veículos empregados na produção destes adoçantes, sendo que esta metodologia quimiométrica reduziu o espaço dimensional para dois fatores, explicando cerca de 82-% da variância total dos dados. As variáveis responsáveis por esta discriminação estão localizadas na região da impressão digital do espectro de infravermelho (752,2 a 1284,5 cm-1. A análise exploratória mostrou-se útil para a visualização destes dados, gerando informações semiquantitativas para os adoçantes constituídos por lactose/aspartame, observações que seriam dificilmente visualizadas sem o recurso quimiométrico aplicado.In the last twenty years, the consumption of diet and light foods has grown steadily, leading to the constant development of such products. Much emphasis has been placed on products that replace sucrose with sweeteners of low or zero calorie content. The development of new commercial sweeteners illustrates this tendency. In this work, principal component analysis and infrared spectroscopy were used to successfully differentiate the vehicles (mediums employed in the production of sweeteners. This chemometric methodology reduced the dimensional space to two factors, accounting for 82% of the total variance of the data. The variables responsible for this discrimination were localized in the fingerprint region of the infrared spectrum (752.2 to

  6. Assessment of oil weathering by gas chromatography-mass spectrometry, time warping and principal component analysis

    DEFF Research Database (Denmark)

    Malmquist, Linus M.V.; Olsen, Rasmus R.; Hansen, Asger B.

    2007-01-01

    weathering state and to distinguish between various weathering processes is investigated and discussed. The method is based on comprehensive and objective chromatographic data processing followed by principal component analysis (PCA) of concatenated sections of gas chromatography–mass spectrometry...

  7. Northeast Puerto Rico and Culebra Island Principle Component Analysis - NOAA TIFF Image

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This GeoTiff is a representation of seafloor topography in Northeast Puerto Rico derived from a bathymetry model with a principle component analysis (PCA). The area...

  8. Cognitive Component Analysis

    DEFF Research Database (Denmark)

    Feng, Ling

    2008-01-01

    This dissertation concerns the investigation of the consistency of statistical regularities in a signaling ecology and human cognition, while inferring appropriate actions for a speech-based perceptual task. It is based on unsupervised Independent Component Analysis providing a rich spectrum...... of audio contexts along with pattern recognition methods to map components to known contexts. It also involves looking for the right representations for auditory inputs, i.e. the data analytic processing pipelines invoked by human brains. The main ideas refer to Cognitive Component Analysis, defined...... as the process of unsupervised grouping of generic data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. Its hypothesis runs ecologically: features which are essentially independent in a context defined ensemble, can be efficiently coded as sparse...

  9. MODEL APPLICATION MULTIVARIATE ANALYSIS OF STATISTICAL TECHNIQUES PCA AND HCA ASSESSMENT QUESTIONNAIRE ON CUSTOMER SATISFACTION: CASE STUDY IN A METALLURGICAL COMPANY OF METAL CONTAINERS

    Directory of Open Access Journals (Sweden)

    Cláudio Roberto Rosário

    2012-07-01

    Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.

  10. Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

    Science.gov (United States)

    Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

    2018-02-01

    The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not

  11. Bayesian Independent Component Analysis

    DEFF Research Database (Denmark)

    Winther, Ole; Petersen, Kaare Brandt

    2007-01-01

    In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...

  12. Memory Efficient PCA Methods for Large Group ICA.

    Science.gov (United States)

    Rachakonda, Srinivas; Silva, Rogers F; Liu, Jingyu; Calhoun, Vince D

    2016-01-01

    Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT). The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads), accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4 GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations ideal for big

  13. Memory efficient PCA methods for large group ICA

    Directory of Open Access Journals (Sweden)

    Srinivas eRachakonda

    2016-02-01

    Full Text Available Principal component analysis (PCA is widely used for data reduction in group independent component analysis (ICA of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT. The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads, accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations

  14. Multistage principal component analysis based method for abdominal ECG decomposition

    International Nuclear Information System (INIS)

    Petrolis, Robertas; Krisciukaitis, Algimantas; Gintautas, Vladas

    2015-01-01

    Reflection of fetal heart electrical activity is present in registered abdominal ECG signals. However this signal component has noticeably less energy than concurrent signals, especially maternal ECG. Therefore traditionally recommended independent component analysis, fails to separate these two ECG signals. Multistage principal component analysis (PCA) is proposed for step-by-step extraction of abdominal ECG signal components. Truncated representation and subsequent subtraction of cardio cycles of maternal ECG are the first steps. The energy of fetal ECG component then becomes comparable or even exceeds energy of other components in the remaining signal. Second stage PCA concentrates energy of the sought signal in one principal component assuring its maximal amplitude regardless to the orientation of the fetus in multilead recordings. Third stage PCA is performed on signal excerpts representing detected fetal heart beats in aim to perform their truncated representation reconstructing their shape for further analysis. The algorithm was tested with PhysioNet Challenge 2013 signals and signals recorded in the Department of Obstetrics and Gynecology, Lithuanian University of Health Sciences. Results of our method in PhysioNet Challenge 2013 on open data set were: average score: 341.503 bpm 2 and 32.81 ms. (paper)

  15. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

    Directory of Open Access Journals (Sweden)

    Gagan S Sidhu

    2012-11-01

    Full Text Available This article explores various preprocessing tools that select/create features to help a learner produce a classifier that can use fMRI data to effectively discriminate Attention-Deficit Hyperactivity Disorder (ADHD patients from healthy controls. We consider four different learning tasks: predicting either two (ADHD vs control or three classes (ADHD-1 vs ADHD-3 vs control, where each use either the imaging data only, or the phenotypic and imaging data. After averaging, BOLD-signal normalization, and masking of the fMRI images, we considered applying Fast Fourier Transform (FFT, possibly followed by some Principal Component Analysis (PCA variant (over time: PCA-t; over space and time: PCA-st or the kernelized variant, kPCA-st, to produce inputs to a learner, to determine which learned classifier performs the best – or at least better than the baseline of 64.2%, which is the proportion of the majority class (here, controls.In the two-class setting, PCA-t and PCA-st did not perform statistically better than baseline, whereas FFT and kPCA-st did (FFT, 68.4%; kPCA-st, 70.3%; when combined with the phenotypic data, which by itself produces 72.9% accuracy, all methods performed statistically better than the baseline, but none did better than using the phenotypic data. In the three-class setting, neither the PCA variants, or the phenotypic data classifiers, performed statistically better than the baseline.We next used the FFT output as input to the PCA variants. In the two-class setting, the PCA variants performed statistically better than the baseline using either the FFTed waveforms only (FFT+PCA-t, 69.6%,; FFT+PCA-st, 69.3% ; FFT+kPCA-st, 68.7%, or combining them with the phenotypic data (FFT+PCA-t, 70.6%; FFT+PCA-st, 70.6%; kPCA-st, 76%. In both settings, combining FFT+kPCA-st’s features with the phenotypic data was better than using only the phenotypic data, with the result in the two-class setting being statistically better.

  16. Linearization of the Principal Component Analysis method for radiative transfer acceleration: Application to retrieval algorithms and sensitivity studies

    International Nuclear Information System (INIS)

    Spurr, R.; Natraj, V.; Lerot, C.; Van Roozendael, M.; Loyola, D.

    2013-01-01

    Principal Component Analysis (PCA) is a promising tool for enhancing radiative transfer (RT) performance. When applied to binned optical property data sets, PCA exploits redundancy in the optical data, and restricts the number of full multiple-scatter calculations to those optical states corresponding to the most important principal components, yet still maintaining high accuracy in the radiance approximations. We show that the entire PCA RT enhancement process is analytically differentiable with respect to any atmospheric or surface parameter, thus allowing for accurate and fast approximations of Jacobian matrices, in addition to radiances. This linearization greatly extends the power and scope of the PCA method to many remote sensing retrieval applications and sensitivity studies. In the first example, we examine accuracy for PCA-derived UV-backscatter radiance and Jacobian fields over a 290–340 nm window. In a second application, we show that performance for UV-based total ozone column retrieval is considerably improved without compromising the accuracy. -- Highlights: •Principal Component Analysis (PCA) of spectrally-binned atmospheric optical properties. •PCA-based accelerated radiative transfer with 2-stream model for fast multiple-scatter. •Atmospheric and surface property linearization of this PCA performance enhancement. •Accuracy of PCA enhancement for radiances and bulk-property Jacobians, 290–340 nm. •Application of PCA speed enhancement to UV backscatter total ozone retrievals

  17. Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate

    International Nuclear Information System (INIS)

    Lv Jiancheng; Yi Zhang

    2007-01-01

    The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm

  18. Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate

    Energy Technology Data Exchange (ETDEWEB)

    Lv Jiancheng [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China); Yi Zhang [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)]. E-mail: zhangyi@uestc.edu.cn

    2007-05-15

    The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm.

  19. Condition Monitoring of Sensors in a NPP Using Optimized PCA

    Directory of Open Access Journals (Sweden)

    Wei Li

    2018-01-01

    Full Text Available An optimized principal component analysis (PCA framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

  20. How Many Separable Sources? Model Selection In Independent Components Analysis

    Science.gov (United States)

    Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

    2015-01-01

    Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

  1. Time-of-flight secondary ion mass spectrometry of a range of coal samples: a chemometrics (PCA, cluster, and PLS) analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lei Pei; Guilin Jiang; Bonnie J. Tyler; Larry L. Baxter; Matthew R. Linford [Brigham Young University, Provo, UT (United States). Department of Chemistry and Biochemistry

    2008-03-15

    This paper documents time-of-flight secondary ion mass spectrometry (ToF-SIMS) analyses of 34 different coal samples. In many cases, the inorganic Na{sup +}, Al{sup +}, Si{sup +}, and K{sup +} ions dominate the spectra, eclipsing the organic peaks. A scores plot of principal component 1 (PC1) versus principal component 2 (PC2) in a principal components analysis (PCA) effectively separates the coal spectra into a triangular pattern, where the different vertices of this pattern come from (I) spectra that have a strong inorganic signature that is dominated by Na{sup +}, (ii) spectra that have a strong inorganic signature that is dominated by Al{sup +}, Si{sup +}, and K{sup +}, and (iii) spectra that have a strong organic signature. Loadings plots of PC1 and PC2 confirm these observations. The spectra with the more prominent inorganic signatures come from samples with higher ash contents. Cluster analysis with the K-means algorithm was also applied to the data. The progressive clustering revealed in the dendrogram correlates extremely well with the clustering of the data points found in the scores plot of PC1 versus PC2 from the PCA. In addition, this clustering often correlates with properties of the coal samples, as measured by traditional analyses. Partial least-squares (PLS), which included the use of interval PLS and a genetic algorithm for variable selection, shows a good correlation between ToF-SIMS spectra and some of the properties measured by traditional means. Thus, ToF-SIMS appears to be a promising technique for the analysis of this important fuel. 33 refs., 9 figs., 5 tabs.

  2. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    Science.gov (United States)

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2018-03-01

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  3. PEMBUATAN PERANGKAT LUNAK PENGENALAN WAJAH MENGGUNAKAN PRINCIPAL COMPONENTS ANALYSIS

    Directory of Open Access Journals (Sweden)

    Kartika Gunadi

    2001-01-01

    Full Text Available Face recognition is one of many important researches, and today, many applications have implemented it. Through development of techniques like Principal Components Analysis (PCA, computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched. Principal Components Analysis was used to reduce facial image dimension into fewer variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification Abstract in Bahasa Indonesia : Pengenalan wajah manusia merupakan salah satu bidang penelitian yang penting, dan dewasa ini banyak aplikasi yang dapat menerapkannya. Melalui pengembangan suatu teknik seperti Principal Components Analysis (PCA, komputer sekarang dapat melebihi kemampuan otak manusia dalam berbagai tugas pengenalan wajah, terutama tugas-tugas yang membutuhkan pencarian pada database wajah yang besar. Principal Components Analysis digunakan untuk mereduksi dimensi gambar wajah sehingga menghasilkan variabel yang lebih sedikit yang lebih mudah untuk diobsevasi dan ditangani. Hasil yang diperoleh kemudian akan dimasukkan ke suatu jaringan saraf tiruan dengan metode Backpropagation untuk mengenali gambar wajah yang telah diinputkan ke dalam sistem. Hasil pengujian sistem menunjukkan bahwa penggunaan PCA untuk pengenalan wajah dapat memberikan tingkat akurasi yang cukup tinggi. Untuk gambar wajah yang diikutsertakankan dalam latihan, dapat diperoleh 100% identifikasi yang benar. Dari beberapa kombinasi jaringan yang

  4. Principal Component Analysis - A Powerful Tool in Computing Marketing Information

    Directory of Open Access Journals (Sweden)

    Constantin C.

    2014-12-01

    Full Text Available This paper is about an instrumental research regarding a powerful multivariate data analysis method which can be used by the researchers in order to obtain valuable information for decision makers that need to solve the marketing problem a company face with. The literature stresses the need to avoid the multicollinearity phenomenon in multivariate analysis and the features of Principal Component Analysis (PCA in reducing the number of variables that could be correlated with each other to a small number of principal components that are uncorrelated. In this respect, the paper presents step-by-step the process of applying the PCA in marketing research when we use a large number of variables that naturally are collinear.

  5. Analysis and Extension of the PCA Method, Estimating a Noise Curve from a Single Image

    Directory of Open Access Journals (Sweden)

    Miguel Colom

    2016-12-01

    Full Text Available In the article 'Image Noise Level Estimation by Principal Component Analysis', S. Pyatykh, J. Hesser, and L. Zheng propose a new method to estimate the variance of the noise in an image from the eigenvalues of the covariance matrix of the overlapping blocks of the noisy image. Instead of using all the patches of the noisy image, the authors propose an iterative strategy to adaptively choose the optimal set containing the patches with lowest variance. Although the method measures uniform Gaussian noise, it can be easily adapted to deal with signal-dependent noise, which is realistic with the Poisson noise model obtained by a CMOS or CCD device in a digital camera.

  6. Principal component analysis networks and algorithms

    CERN Document Server

    Kong, Xiangyu; Duan, Zhansheng

    2017-01-01

    This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

  7. A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions......: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA....... As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen....

  8. Comprehensive analysis and evaluation of big data for main transformer equipment based on PCA and Apriority

    Science.gov (United States)

    Guo, Lijuan; Yan, Haijun; Hao, Yongqi; Chen, Yun

    2018-01-01

    With the power supply level of urban power grid toward high reliability development, it is necessary to adopt appropriate methods for comprehensive evaluation of existing equipment. Considering the wide and multi-dimensional power system data, the method of large data mining is used to explore the potential law and value of power system equipment. Based on the monitoring data of main transformer and the records of defects and faults, this paper integrates the data of power grid equipment environment. Apriori is used as an association identification algorithm to extract the frequent correlation factors of the main transformer, and the potential dependence of the big data is analyzed by the support and confidence. Then, the integrated data is analyzed by PCA, and the integrated quantitative scoring model is constructed. It is proved to be effective by using the test set to validate the evaluation algorithm and scheme. This paper provides a new idea for data fusion of smart grid, and provides a reference for further evaluation of big data of power grid equipment.

  9. On the Link Between L1-PCA and ICA.

    Science.gov (United States)

    Martin-Clemente, Ruben; Zarzoso, Vicente

    2017-03-01

    Principal component analysis (PCA) based on L1-norm maximization is an emerging technique that has drawn growing interest in the signal processing and machine learning research communities, especially due to its robustness to outliers. The present work proves that L1-norm PCA can perform independent component analysis (ICA) under the whitening assumption. However, when the source probability distributions fulfil certain conditions, the L1-norm criterion needs to be minimized rather than maximized, which can be accomplished by simple modifications on existing optimal algorithms for L1-PCA. If the sources have symmetric distributions, we show in addition that L1-PCA is linked to kurtosis optimization. A number of numerical experiments illustrate the theoretical results and analyze the comparative performance of different algorithms for ICA via L1-PCA. Although our analysis is asymptotic in the sample size, this equivalence opens interesting new perspectives for performing ICA using optimal algorithms for L1-PCA with guaranteed global convergence while inheriting the increased robustness to outliers of the L1-norm criterion.

  10. Fault detection of feed water treatment process using PCA-WD with parameter optimization.

    Science.gov (United States)

    Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling

    2017-05-01

    Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. A feasibility study on age-related factors of wrist pulse using principal component analysis.

    Science.gov (United States)

    Jang-Han Bae; Young Ju Jeon; Sanghun Lee; Jaeuk U Kim

    2016-08-01

    Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

  12. Research on Air Quality Evaluation based on Principal Component Analysis

    Science.gov (United States)

    Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

    2018-01-01

    Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

  13. Comparative study of PCA in classification of multichannel EMG signals.

    Science.gov (United States)

    Geethanjali, P

    2015-06-01

    Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal-Wallis (KW) statistical test shows that there is a significant reduction (P PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in off-line with TMSLF2407 digital signal controller (DSC).

  14. How Many Separable Sources? Model Selection In Independent Components Analysis

    DEFF Research Database (Denmark)

    Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

    2015-01-01

    among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....

  15. Developing and Evaluating Creativity Gamification Rehabilitation System: The Application of PCA-ANFIS Based Emotions Model

    Science.gov (United States)

    Su, Chung-Ho; Cheng, Ching-Hsue

    2016-01-01

    This study aims to explore the factors in a patient's rehabilitation achievement after a total knee replacement (TKR) patient exercises, using a PCA-ANFIS emotion model-based game rehabilitation system, which combines virtual reality (VR) and motion capture technology. The researchers combine a principal component analysis (PCA) and an adaptive…

  16. Component Analysis of Long-Lag, Wide-Pulse Gamma-Ray Burst ...

    Indian Academy of Sciences (India)

    Principal Component Analysis of Long-Lag, Wide-Pulse Gamma-Ray. Burst Data. Zhao-Yang Peng. ∗. & Wen-Shuai Liu. Department of Physics, Yunnan Normal University, Kunming 650500, China. ∗ e-mail: pzy@ynao.ac.cn. Abstract. We have carried out a Principal Component Analysis (PCA) of the temporal and spectral ...

  17. Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

    Science.gov (United States)

    Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis

    2013-11-01

    Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.

  18. IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

    International Nuclear Information System (INIS)

    Casini, R.; Lites, B. W.; Ramos, A. Asensio; Ariste, A. López

    2013-01-01

    We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2 4n bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2 4n as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing

  19. A stable systemic risk ranking in China's banking sector: Based on principal component analysis

    Science.gov (United States)

    Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

    2018-02-01

    In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

  20. Use of Geochemistry Data Collected by the Mars Exploration Rover Spirit in Gusev Crater to Teach Geomorphic Zonation through Principal Components Analysis

    Science.gov (United States)

    Rodrigue, Christine M.

    2011-01-01

    This paper presents a laboratory exercise used to teach principal components analysis (PCA) as a means of surface zonation. The lab was built around abundance data for 16 oxides and elements collected by the Mars Exploration Rover Spirit in Gusev Crater between Sol 14 and Sol 470. Students used PCA to reduce 15 of these into 3 components, which,…

  1. Chemical fingerprinting of petroleum biomakers using time warping and PCA

    DEFF Research Database (Denmark)

    Christensen, Jan H.; Tomasi, Giorgio; Hansen, Asger B.

    2005-01-01

    A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization...

  2. Principal Component Analysis of Process Datasets with Missing Values

    Directory of Open Access Journals (Sweden)

    Kristen A. Severson

    2017-07-01

    Full Text Available Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA, which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets.

  3. Principal components analysis of protein structure ensembles calculated using NMR data

    International Nuclear Information System (INIS)

    Howe, Peter W.A.

    2001-01-01

    One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations

  4. Independent component analysis: recent advances

    OpenAIRE

    Hyv?rinen, Aapo

    2013-01-01

    Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in th...

  5. Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI.

    Science.gov (United States)

    Wang, Yiran; Chen, Zhifeng; Wang, Jing; Yuan, Lixia; Xia, Ling; Liu, Feng

    2017-01-01

    The k - t principal component analysis ( k - t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k - t PCA that combines the k - t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k - t PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled k - t space from the original reconstructed k - t space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard k - t PCA algorithm, the sparse k - t PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.

  6. Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard

    2012-01-01

    We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising...... procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...

  7. pcaGoPromoter--an R package for biological and regulatory interpretation of principal components in genome-wide gene expression data

    DEFF Research Database (Denmark)

    Hansen, Morten; Gerds, Thomas Alexander; Nielsen, Ole Haagen

    2012-01-01

    Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome.......g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-¿B activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides...

  8. Shifted Independent Component Analysis

    DEFF Research Database (Denmark)

    Mørup, Morten; Madsen, Kristoffer Hougaard; Hansen, Lars Kai

    2007-01-01

    Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and financial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried...

  9. An efficient algorithm for weighted PCA

    NARCIS (Netherlands)

    Krijnen, W.P.; Kiers, H.A.L.

    1995-01-01

    The method for analyzing three-way data where one of the three components matrices in TUCKALS3 is chosen to have one column is called Replicated PCA. The corresponding algorithm is relatively inefficient. This is shown by offering an alternative algorithm called Weighted PCA. Specifically it is

  10. APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

    International Nuclear Information System (INIS)

    STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.

    1999-01-01

    Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content

  11. Using Principal Component Analysis (PCA) to Speed up Radiative Transfer (RT) Computations

    Science.gov (United States)

    Natraj, Vijay

    2012-01-01

    Multiple scattering RT calculations time-consuming. Need a speed improvement of about 1000 (for OCO)! Solution: Make use of redundancies in spectra. Correlated-k (Lacis and Wang, Lacis and Oinas, Goody et al, Fu and Liou) Problem: Assume that spectral variation of atmospheric optical properties spatially correlated at all points along optical path. High accuracy (HI) and 2-stream (2S) calculations have high correlation. Single scattering (SS) computations highly scenario-dependent, but not time consuming. Perform SS and 2S calculations at every wavelength. Perform small number of HI computations. Need to compute correction factor B at every wavelength.

  12. Linking PCA and time derivatives of dynamic systems

    NARCIS (Netherlands)

    Stanimirovic, Olja; Hoefsloot, Huub C. J.; de Bokx, Pieter K.; Smilde, Age K.

    2006-01-01

    Low dimensional approximate descriptions of the high dimensional phase space of dynamic processes are very useful. Principal component analysis (PCA) is the most used technique to find the low dimensional subspace of interest. Here, it will be shown that mean centering of the process data across

  13. Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.

    Science.gov (United States)

    Wang, Huiwen; Chen, Meiling; Shi, Xiaojun; Li, Nan

    2016-02-01

    This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.

  14. Radiosurgical treatment planning for intracranial AVM based on images generated by principal component analysis. A simulation study

    International Nuclear Information System (INIS)

    Kawaguchi, Osamu; Kunieda, Etsuo; Nyui, Yoshiyuki

    2009-01-01

    One of the most important factors in stereotactic radiosurgery (SRS) for intracranial arteriovenous malformation (AVM) is to determine accurate target delineation of the nidus. However, since intracranial AVMs are complicated in structure, it is often difficult to clearly determine the target delineation. The purpose of this study was to investigate the usefulness of principal component analysis (PCA) on intra-arterial contrast enhanced dynamic CT (IADCT) images as a tool for delineating accurate target volumes for stereotactic radiosurgery of AVMs. IADCT and intravenous contrast-enhanced CT (IVCT) were used to examine 4 randomly selected cases of AVM. PCA images were generated from the IADCT data. The first component images were considered feeding artery predominant, the second component images were considered draining vein predominant, and the third component images were considered background. Target delineations were first carried out from IVCT, and then again while referring to the first and second components of the PCA images. Dose calculation simulations for radiosurgical treatment plans with IVCT and PCA images were performed. Dose volume histograms of the vein areas as well as the target volumes were compared. In all cases, the calculated target volumes based on IVCT images were larger than those based on PCA images, and the irradiation doses for the vein areas were reduced. In this study, we simulated radiosurgical treatment planning for intracranial AVM based on PCA images. By using PCA images, the irradiation doses for the vein areas were substantially reduced. (author)

  15. Adaptive online monitoring for ICU patients by combining just-in-time learning and principal component analysis.

    Science.gov (United States)

    Li, Xuejian; Wang, Youqing

    2016-12-01

    Offline general-type models are widely used for patients' monitoring in intensive care units (ICUs), which are developed by using past collected datasets consisting of thousands of patients. However, these models may fail to adapt to the changing states of ICU patients. Thus, to be more robust and effective, the monitoring models should be adaptable to individual patients. A novel combination of just-in-time learning (JITL) and principal component analysis (PCA), referred to learning-type PCA (L-PCA), was proposed for adaptive online monitoring of patients in ICUs. JITL was used to gather the most relevant data samples for adaptive modeling of complex physiological processes. PCA was used to build an online individual-type model and calculate monitoring statistics, and then to judge whether the patient's status is normal or not. The adaptability of L-PCA lies in the usage of individual data and the continuous updating of the training dataset. Twelve subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and five vital signs of each subject were chosen. The proposed method was compared with the traditional PCA and fast moving-window PCA (Fast MWPCA). The experimental results demonstrated that the fault detection rates respectively increased by 20 % and 47 % compared with PCA and Fast MWPCA. L-PCA is first introduced into ICU patients monitoring and achieves the best monitoring performance in terms of adaptability to changes in patient status and sensitivity for abnormality detection.

  16. pyPcazip: A PCA-based toolkit for compression and analysis of molecular simulation data

    Directory of Open Access Journals (Sweden)

    Ardita Shkurti

    2016-01-01

    Full Text Available The biomolecular simulation community is currently in need of novel and optimised software tools that can analyse and process, in reasonable timescales, the large generated amounts of molecular simulation data. In light of this, we have developed and present here pyPcazip: a suite of software tools for compression and analysis of molecular dynamics (MD simulation data. The software is compatible with trajectory file formats generated by most contemporary MD engines such as AMBER, CHARMM, GROMACS and NAMD, and is MPI parallelised to permit the efficient processing of very large datasets. pyPcazip is a Unix based open-source software (BSD licenced written in Python.

  17. The PCa Tumor Microenvironment.

    Science.gov (United States)

    Sottnik, Joseph L; Zhang, Jian; Macoska, Jill A; Keller, Evan T

    2011-12-01

    The tumor microenvironment (TME) is a very complex niche that consists of multiple cell types, supportive matrix and soluble factors. Cells in the TME consist of both host cells that are present at tumor site at the onset of tumor growth and cells that are recruited in either response to tumor- or host-derived factors. PCa (PCa) thrives on crosstalk between tumor cells and the TME. Crosstalk results in an orchestrated evolution of both the tumor and microenvironment as the tumor progresses. The TME reacts to PCa-produced soluble factors as well as direct interaction with PCa cells. In return, the TME produces soluble factors, structural support and direct contact interactions that influence the establishment and progression of PCa. In this review, we focus on the host side of the equation to provide a foundation for understanding how different aspects of the TME contribute to PCa progression. We discuss immune effector cells, specialized niches, such as the vascular and bone marrow, and several key protein factors that mediate host effects on PCa. This discussion highlights the concept that the TME offers a potentially very fertile target for PCa therapy.

  18. Multiview Bayesian Correlated Component Analysis

    DEFF Research Database (Denmark)

    Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai

    2015-01-01

    are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....

  19. Bayes PCA Revisited

    DEFF Research Database (Denmark)

    Sporring, Jon

    Principle Component Analysis is a simple tool to obtain linear models for stochastic data and is used both for a data reduction or equivalently noise elim- ination and for data analysis. Principle Component Analysis ts a multivariate Gaussian distribution to the data, and the typical method is by...

  20. Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    CHEN, R.

    2017-11-01

    Full Text Available In this paper, based on Kernel Principal Component Analysis (KPCA of Phasor Measurement Units (PMU data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.

  1. The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

    Science.gov (United States)

    Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

    2018-08-15

    Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. Grassmann Averages for Scalable Robust PCA

    DEFF Research Database (Denmark)

    Hauberg, Søren; Feragen, Aasa; Black, Michael J.

    2014-01-01

    As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase—“big data” implies “big outliers”. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can...... to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements...

  3. Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

    Science.gov (United States)

    Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

    2015-12-01

    A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

  4. Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

    Science.gov (United States)

    Jaiswal, Abeg Kumar; Banka, Haider

    2017-01-01

    Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space

  5. Contact- and distance-based principal component analysis of protein dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Ernst, Matthias; Sittel, Florian; Stock, Gerhard, E-mail: stock@physik.uni-freiburg.de [Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg (Germany)

    2015-12-28

    To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

  6. The PCA3 test for guiding repeat biopsy of prostate cancer and its cut-off score: a systematic review and meta-analysis

    Directory of Open Access Journals (Sweden)

    Yong Luo

    2014-06-01

    Full Text Available The specificity of prostate-specific antigen (PSA for early intervention in repeat biopsy is unsatisfactory. Prostate cancer antigen 3 (PCA3 may be more accurate in outcome prediction than other methods for the early detection of prostate cancer (PCa. However, the results were inconsistent in repeated biopsies. Therefore, we performed a systematic review and meta-analysis to evaluate the role of PCA3 in outcome prediction. A systematic bibliographic search was conducted for articles published before April 2013, using PubMed, Medline, Web of Science, Embase and other databases from health technology assessment agencies. The quality of the studies was assessed on the basis of QUADAS criteria. Eleven studies of diagnostic tests with moderate to high quality were selected. A meta-analysis was carried out to synthesize the results. The results of the meta-analyses were heterogeneous among studies. We performed a subgroup analysis (with or without inclusion of high-grade prostatic intraepithelial neoplasia (HGPIN and atypical small acinar proliferation (ASAP. Using a PCA3 cutoff of 20 or 35, in the two sub-groups, the global sensitivity values were 0.93 or 0.80 and 0.79 or 0.75, specificities were 0.65 or 0.44 and 0.78 or 0.70, positive likelihood ratios were 1.86 or 1.58 and 2.49 or 1.78, negative likelihood ratios were 0.81 or 0.43 and 0.91 or 0.82 and diagnostic odd ratios (ORs were 5.73 or 3.45 and 7.13 or 4.11, respectively. The areas under the curve (AUCs of the summary receiver operating characteristic curve were 0.85 or 0.72 and 0.81 or 0.69, respectively. PCA3 can be used for repeat biopsy of the prostate to improve accuracy of PCa detection. Unnecessary biopsies can be avoided by using a PCa cutoff score of 20.

  7. GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

    Science.gov (United States)

    Wagner, Florian

    2015-01-01

    Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

  8. Advances in independent component analysis and learning machines

    CERN Document Server

    Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko

    2015-01-01

    In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

  9. THE TURBULENCE SPECTRUM OF MOLECULAR CLOUDS IN THE GALACTIC RING SURVEY: A DENSITY-DEPENDENT PRINCIPAL COMPONENT ANALYSIS CALIBRATION

    International Nuclear Information System (INIS)

    Roman-Duval, Julia; Jackson, James; Federrath, Christoph; Klessen, Ralf S.; Brunt, Christopher; Heyer, Mark

    2011-01-01

    Turbulence plays a major role in the formation and evolution of molecular clouds. Observationally, turbulent velocities are convolved with the density of an observed region. To correct for this convolution, we investigate the relation between the turbulence spectrum of model clouds, and the statistics of their synthetic observations obtained from principal component analysis (PCA). We apply PCA to spectral maps generated from simulated density and velocity fields, obtained from hydrodynamic simulations of supersonic turbulence, and from fractional Brownian motion (fBm) fields with varying velocity, density spectra, and density dispersion. We examine the dependence of the slope of the PCA pseudo-structure function, α PCA , on intermittency, on the turbulence velocity (β v ) and density (β n ) spectral indexes, and on density dispersion. We find that PCA is insensitive to β n and to the log-density dispersion σ s , provided σ s ≤ 2. For σ s > 2, α PCA increases with σ s due to the intermittent sampling of the velocity field by the density field. The PCA calibration also depends on intermittency. We derive a PCA calibration based on fBm structures with σ s ≤ 2 and apply it to 367 13 CO spectral maps of molecular clouds in the Galactic Ring Survey. The average slope of the PCA structure function, (α PCA ) = 0.62 ± 0.2, is consistent with the hydrodynamic simulations and leads to a turbulence velocity exponent of (β v ) = 2.06 ± 0.6 for a non-intermittent, low density dispersion flow. Accounting for intermittency and density dispersion, the coincidence between the PCA slope of the GRS clouds and the hydrodynamic simulations suggests β v ≅ 1.9, consistent with both Burgers and compressible intermittent turbulence.

  10. ECG-derived respiration methods: adapted ICA and PCA.

    Science.gov (United States)

    Tiinanen, Suvi; Noponen, Kai; Tulppo, Mikko; Kiviniemi, Antti; Seppänen, Tapio

    2015-05-01

    Respiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  11. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    Science.gov (United States)

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department

    KAUST Repository

    Harrou, Fouzi; Kadri, Farid; Chaabane, Sondé s; Tahon, Christian; Sun, Ying

    2015-01-01

    Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling’s T2T2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

  13. Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department

    KAUST Repository

    Harrou, Fouzi

    2015-07-03

    Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling’s T2T2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

  14. Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations.

    Science.gov (United States)

    Ioele, Giuseppina; De Luca, Michele; Dinç, Erdal; Oliverio, Filomena; Ragno, Gaetano

    2011-01-01

    A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.

  15. Integrative sparse principal component analysis of gene expression data.

    Science.gov (United States)

    Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

    2017-12-01

    In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

  16. Classification of soil samples according to their geographic origin using gamma-ray spectrometry and principal component analysis

    International Nuclear Information System (INIS)

    Dragovic, Snezana; Onjia, Antonije

    2006-01-01

    A principal component analysis (PCA) was used for classification of soil samples from different locations in Serbia and Montenegro. Based on activities of radionuclides ( 226 Ra, 238 U, 235 U, 4 K, 134 Cs, 137 Cs, 232 Th and 7 Be) detected by gamma-ray spectrometry, the classification of soils according to their geographical origin was performed. Application of PCA to our experimental data resulted in satisfactory classification rate (86.0% correctly classified samples). The obtained results indicate that gamma-ray spectrometry in conjunction with PCA is a viable tool for soil classification

  17. Application of principal component analysis to time series of daily air pollution and mortality

    NARCIS (Netherlands)

    Quant C; Fischer P; Buringh E; Ameling C; Houthuijs D; Cassee F; MGO

    2004-01-01

    We investigated whether cause-specific daily mortality can be attributed to specific sources of air pollution. To construct indicators of source-specific air pollution, we applied a principal component analysis (PCA) on routinely collected air pollution data in the Netherlands during the period

  18. Principal Component Analysis: Resources for an Essential Application of Linear Algebra

    Science.gov (United States)

    Pankavich, Stephen; Swanson, Rebecca

    2015-01-01

    Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

  19. Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream

    OpenAIRE

    Byrne, Patrick; Runkel, Robert L.; Walton-Day, Katherine

    2017-01-01

    Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on str...

  20. Variability search in M 31 using principal component analysis and the Hubble Source Catalogue

    Science.gov (United States)

    Moretti, M. I.; Hatzidimitriou, D.; Karampelas, A.; Sokolovsky, K. V.; Bonanos, A. Z.; Gavras, P.; Yang, M.

    2018-06-01

    Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18 152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long-period variables (LPVs) and non-variables. This projection recovered more than 90 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.

  1. Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

    Science.gov (United States)

    Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

    2017-08-01

    Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

  2. Meta-Analysis of the Ease of Care From the Nurses' Perspective Comparing Fentanyl Iontophoretic Transdermal System (ITS) Vs Morphine Intravenous Patient-Controlled Analgesia (IV PCA) in Postoperative Pain Management.

    Science.gov (United States)

    Pestano, Cecile R; Lindley, Pam; Ding, Li; Danesi, Hassan; Jones, James B

    2017-08-01

    The aim of this meta-analysis was to compare the ease of care (EOC) of fentanyl iontophoretic transdermal system (ITS) vs the morphine intravenous patient-controlled analgesia (IV PCA) as assessed by the nurse. Meta-analysis of three phase 3B randomized active-comparator trials. This meta-analysis according to Cochrane's approach assessed EOC using a validated nurse questionnaire (22 items grouped into three subscales, which include time efficiency, convenience, and satisfaction) in adult patients treated with fentanyl ITS or morphine IV PCA for postoperative pain management. The weighted mean difference (WMD) between treatments was calculated. EOC analyses were based on responses to questionnaires from 848 (fentanyl ITS) and 761 (morphine IV PCA) nurses. Fentanyl ITS was reported to provide significant advantages compared with morphine IV PCA in terms of nurses' overall EOC (WMD = -0.57, P PCA. Copyright © 2016 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.

  3. GND-PCA-based statistical modeling of diaphragm motion extracted from 4D MRI.

    Science.gov (United States)

    Swastika, Windra; Masuda, Yoshitada; Xu, Rui; Kido, Shoji; Chen, Yen-Wei; Haneishi, Hideaki

    2013-01-01

    We analyzed a statistical model of diaphragm motion using regular principal component analysis (PCA) and generalized N-dimensional PCA (GND-PCA). First, we generate 4D MRI of respiratory motion from 2D MRI using an intersection profile method. We then extract semiautomatically the diaphragm boundary from the 4D-MRI to get subject-specific diaphragm motion. In order to build a general statistical model of diaphragm motion, we normalize the diaphragm motion in time and spatial domains and evaluate the diaphragm motion model of 10 healthy subjects by applying regular PCA and GND-PCA. We also validate the results using the leave-one-out method. The results show that the first three principal components of regular PCA contain more than 98% of the total variation of diaphragm motion. However, validation using leave-one-out method gives up to 5.0 mm mean of error for right diaphragm motion and 3.8 mm mean of error for left diaphragm motion. Model analysis using GND-PCA provides about 1 mm margin of error and is able to reconstruct the diaphragm model by fewer samples.

  4. Biopsy and treatment decisions in the initial management of prostate cancer and the role of PCA3; a systematic analysis of expert opinion

    NARCIS (Netherlands)

    Tombal, Bertrand; Ameye, Filip; de la Taille, Alexandre; de Reijke, Theo; Gontero, Paolo; Haese, Alexander; Kil, Paul; Perrin, Paul; Remzi, Mesut; Schröder, Jörg; Speakman, Mark; Volpe, Alessandro; Meesen, Bianca; Stoevelaar, Herman

    2012-01-01

    The Prostate CAncer gene 3 (PCA3) assay may guide prostate biopsy decisions and predict prostate cancer (PCa) aggressiveness. This study explored the appropriateness of (1) PCA3 testing; (2) biopsy; (3) active surveillance (AS) and the value of the PCA3 Score for biopsy and AS decisions. Using the

  5. Can we use PCA to detect small signals in noisy data?

    Science.gov (United States)

    Spiegelberg, Jakob; Rusz, Ján

    2017-01-01

    Principal component analysis (PCA) is among the most commonly applied dimension reduction techniques suitable to denoise data. Focusing on its limitations to detect low variance signals in noisy data, we discuss how statistical and systematical errors occur in PCA reconstructed data as a function of the size of the data set, which extends the work of Lichtert and Verbeeck, (2013) [16]. Particular attention is directed towards the estimation of bias introduced by PCA and its influence on experiment design. Aiming at the denoising of large matrices, nullspace based denoising (NBD) is introduced. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual

    International Nuclear Information System (INIS)

    Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.

    1981-01-01

    A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained

  7. Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

    DEFF Research Database (Denmark)

    Kotwa, Ewelina Katarzyna; Jørgensen, Bo Munk; Brockhoff, Per B.

    2013-01-01

    In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis...... models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome...... this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum...

  8. The applications of PCA in QSAR studies: A case study on CCR5 antagonists.

    Science.gov (United States)

    Yoo, ChangKyoo; Shahlaei, Mohsen

    2018-01-01

    Principal component analysis (PCA), as a well-known multivariate data analysis and data reduction technique, is an important and useful algebraic tool in drug design and discovery. PCA, in a typical quantitative structure-activity relationship (QSAR) study, analyzes an original data matrix in which molecules are described by several intercorrelated quantitative dependent variables (molecular descriptors). Although extensively applied, there is disparity in the literature with respect to the applications of PCA in the QSAR studies. This study investigates the different applications of PCA in QSAR studies using a dataset including CCR5 inhibitors. The different types of preprocessing are used to compare the PCA performances. The use of PC plots in the exploratory investigation of matrix of descriptors is described. This work is also proved PCA analysis to be a powerful technique for exploring complex datasets in QSAR studies for identification of outliers. This study shows that PCA is able to easily apply to the pool of calculated structural descriptors and also the extracted information can be used to help decide upon an appropriate harder model for further analysis. © 2017 John Wiley & Sons A/S.

  9. InterFace: A software package for face image warping, averaging, and principal components analysis.

    Science.gov (United States)

    Kramer, Robin S S; Jenkins, Rob; Burton, A Mike

    2017-12-01

    We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the "face space" produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.

  10. A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.

    Science.gov (United States)

    Reese, Sarah E; Archer, Kellie J; Therneau, Terry M; Atkinson, Elizabeth J; Vachon, Celine M; de Andrade, Mariza; Kocher, Jean-Pierre A; Eckel-Passow, Jeanette E

    2013-11-15

    Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well. The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article. reesese@vcu.edu

  11. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  12. Principal component analysis of the CT density histogram to generate parametric response maps of COPD

    Science.gov (United States)

    Zha, N.; Capaldi, D. P. I.; Pike, D.; McCormack, D. G.; Cunningham, I. A.; Parraga, G.

    2015-03-01

    Pulmonary x-ray computed tomography (CT) may be used to characterize emphysema and airways disease in patients with chronic obstructive pulmonary disease (COPD). One analysis approach - parametric response mapping (PMR) utilizes registered inspiratory and expiratory CT image volumes and CT-density-histogram thresholds, but there is no consensus regarding the threshold values used, or their clinical meaning. Principal-component-analysis (PCA) of the CT density histogram can be exploited to quantify emphysema using data-driven CT-density-histogram thresholds. Thus, the objective of this proof-of-concept demonstration was to develop a PRM approach using PCA-derived thresholds in COPD patients and ex-smokers without airflow limitation. Methods: Fifteen COPD ex-smokers and 5 normal ex-smokers were evaluated. Thoracic CT images were also acquired at full inspiration and full expiration and these images were non-rigidly co-registered. PCA was performed for the CT density histograms, from which the components with the highest eigenvalues greater than one were summed. Since the values of the principal component curve correlate directly with the variability in the sample, the maximum and minimum points on the curve were used as threshold values for the PCA-adjusted PRM technique. Results: A significant correlation was determined between conventional and PCA-adjusted PRM with 3He MRI apparent diffusion coefficient (p<0.001), with CT RA950 (p<0.0001), as well as with 3He MRI ventilation defect percent, a measurement of both small airways disease (p=0.049 and p=0.06, respectively) and emphysema (p=0.02). Conclusions: PRM generated using PCA thresholds of the CT density histogram showed significant correlations with CT and 3He MRI measurements of emphysema, but not airways disease.

  13. Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA wastewater data

    Directory of Open Access Journals (Sweden)

    Stefania Salvatore

    2016-07-01

    Full Text Available Abstract Background Wastewater-based epidemiology (WBE is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA and to wavelet principal component analysis (WPCA which is more flexible temporally. Methods We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. Results The first three principal components (PCs, functional principal components (FPCs and wavelet principal components (WPCs explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. Conclusion FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

  14. Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.

    Science.gov (United States)

    de Pierrefeu, Amicie; Lofstedt, Tommy; Hadj-Selem, Fouad; Dubois, Mathieu; Jardri, Renaud; Fovet, Thomas; Ciuciu, Philippe; Frouin, Vincent; Duchesnay, Edouard

    2018-02-01

    Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., -dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach

  15. Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Xiaoming Xu

    2017-01-01

    Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

  16. Application of PCA and SIMCA statistical analysis of FT-IR spectra for the classification and identification of different slag types with environmental origin.

    Science.gov (United States)

    Stumpe, B; Engel, T; Steinweg, B; Marschner, B

    2012-04-03

    In the past, different slag materials were often used for landscaping and construction purposes or simply dumped. Nowadays German environmental laws strictly control the use of slags, but there is still a remaining part of 35% which is uncontrolled dumped in landfills. Since some slags have high heavy metal contents and different slag types have typical chemical and physical properties that will influence the risk potential and other characteristics of the deposits, an identification of the slag types is needed. We developed a FT-IR-based statistical method to identify different slags classes. Slags samples were collected at different sites throughout various cities within the industrial Ruhr area. Then, spectra of 35 samples from four different slags classes, ladle furnace (LF), blast furnace (BF), oxygen furnace steel (OF), and zinc furnace slags (ZF), were determined in the mid-infrared region (4000-400 cm(-1)). The spectra data sets were subject to statistical classification methods for the separation of separate spectral data of different slag classes. Principal component analysis (PCA) models for each slag class were developed and further used for soft independent modeling of class analogy (SIMCA). Precise classification of slag samples into four different slag classes were achieved using two different SIMCA models stepwise. At first, SIMCA 1 was used for classification of ZF as well as OF slags over the total spectral range. If no correct classification was found, then the spectrum was analyzed with SIMCA 2 at reduced wavenumbers for the classification of LF as well as BF spectra. As a result, we provide a time- and cost-efficient method based on FT-IR spectroscopy for processing and identifying large numbers of environmental slag samples.

  17. Zero drift and solid Earth tide extracted from relative gravimetric data with principal component analysis

    OpenAIRE

    Hongjuan Yu; Jinyun Guo; Jiulong Li; Dapeng Mu; Qiaoli Kong

    2015-01-01

    Zero drift and solid Earth tide corrections to static relative gravimetric data cannot be ignored. In this paper, a new principal component analysis (PCA) algorithm is presented to extract the zero drift and the solid Earth tide, as signals, from static relative gravimetric data assuming that the components contained in the relative gravimetric data are uncorrelated. Static relative gravity observations from Aug. 15 to Aug. 23, 2014 are used as statistical variables to separate the signal and...

  18. A Biometric Face Recognition System Using an Algorithm Based on the Principal Component Analysis Technique

    Directory of Open Access Journals (Sweden)

    Gheorghe Gîlcă

    2015-06-01

    Full Text Available This article deals with a recognition system using an algorithm based on the Principal Component Analysis (PCA technique. The recognition system consists only of a PC and an integrated video camera. The algorithm is developed in MATLAB language and calculates the eigenfaces considered as features of the face. The PCA technique is based on the matching between the facial test image and the training prototype vectors. The mathcing score between the facial test image and the training prototype vectors is calculated between their coefficient vectors. If the matching is high, we have the best recognition. The results of the algorithm based on the PCA technique are very good, even if the person looks from one side at the video camera.

  19. Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

    Directory of Open Access Journals (Sweden)

    Wenjing Zhao

    2018-01-01

    Full Text Available SGK (sequential generalization of K-means dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1 The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2 The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3 Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

  20. Technology Marketing using PCA , SOM, and STP Strategy Modeling

    OpenAIRE

    Sunghae Jun

    2011-01-01

    Technology marketing is a total processing about identifying and meeting the technological needs of human society. Most technology results exist in intellectual properties like patents. In our research, we consider patent document as a technology. So patent data are analyzed by Principal Component Analysis (PCA) and Self Organizing Map (SOM) for STP(Segmentation, Targeting, and Positioning) strategy modeling. STP is a popular approach for developing marketing strategies. We use STP strategy m...

  1. Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

    Science.gov (United States)

    Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

    2018-01-16

    Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.

  2. On a PCA-based lung motion model

    Energy Technology Data Exchange (ETDEWEB)

    Li Ruijiang; Lewis, John H; Jia Xun; Jiang, Steve B [Department of Radiation Oncology and Center for Advanced Radiotherapy Technologies, University of California San Diego, 3855 Health Sciences Dr, La Jolla, CA 92037-0843 (United States); Zhao Tianyu; Wuenschel, Sara; Lamb, James; Yang Deshan; Low, Daniel A [Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Pl, St. Louis, MO 63110-1093 (United States); Liu Weifeng, E-mail: sbjiang@ucsd.edu [Amazon.com Inc., 701 5th Ave. Seattle, WA 98104 (United States)

    2011-09-21

    Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1

  3. On a PCA-based lung motion model.

    Science.gov (United States)

    Li, Ruijiang; Lewis, John H; Jia, Xun; Zhao, Tianyu; Liu, Weifeng; Wuenschel, Sara; Lamb, James; Yang, Deshan; Low, Daniel A; Jiang, Steve B

    2011-09-21

    Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1

  4. On a PCA-based lung motion model

    International Nuclear Information System (INIS)

    Li Ruijiang; Lewis, John H; Jia Xun; Jiang, Steve B; Zhao Tianyu; Wuenschel, Sara; Lamb, James; Yang Deshan; Low, Daniel A; Liu Weifeng

    2011-01-01

    Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1

  5. TARGETED PRINCIPLE COMPONENT ANALYSIS: A NEW MOTION ARTIFACT CORRECTION APPROACH FOR NEAR-INFRARED SPECTROSCOPY.

    Science.gov (United States)

    Yücel, Meryem A; Selb, Juliette; Cooper, Robert J; Boas, David A

    2014-03-01

    As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson's correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson's correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data.

  6. TARGETED PRINCIPLE COMPONENT ANALYSIS: A NEW MOTION ARTIFACT CORRECTION APPROACH FOR NEAR-INFRARED SPECTROSCOPY

    Science.gov (United States)

    YÜCEL, MERYEM A.; SELB, JULIETTE; COOPER, ROBERT J.; BOAS, DAVID A.

    2014-01-01

    As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson’s correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson’s correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data. PMID:25360181

  7. Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

    Science.gov (United States)

    Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

    2014-01-01

    The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

  8. Mapping ash properties using principal components analysis

    Science.gov (United States)

    Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Ubeda, Xavier; Novara, Agata; Francos, Marcos; Rodrigo-Comino, Jesus; Bogunovic, Igor; Khaledian, Yones

    2017-04-01

    In post-fire environments ash has important benefits for soils, such as protection and source of nutrients, crucial for vegetation recuperation (Jordan et al., 2016; Pereira et al., 2015a; 2016a,b). The thickness and distribution of ash are fundamental aspects for soil protection (Cerdà and Doerr, 2008; Pereira et al., 2015b) and the severity at which was produced is important for the type and amount of elements that is released in soil solution (Bodi et al., 2014). Ash is very mobile material, and it is important were it will be deposited. Until the first rainfalls are is very mobile. After it, bind in the soil surface and is harder to erode. Mapping ash properties in the immediate period after fire is complex, since it is constantly moving (Pereira et al., 2015b). However, is an important task, since according the amount and type of ash produced we can identify the degree of soil protection and the nutrients that will be dissolved. The objective of this work is to apply to map ash properties (CaCO3, pH, and select extractable elements) using a principal component analysis (PCA) in the immediate period after the fire. Four days after the fire we established a grid in a 9x27 m area and took ash samples every 3 meters for a total of 40 sampling points (Pereira et al., 2017). The PCA identified 5 different factors. Factor 1 identified high loadings in electrical conductivity, calcium, and magnesium and negative with aluminum and iron, while Factor 3 had high positive loadings in total phosphorous and silica. Factor 3 showed high positive loadings in sodium and potassium, factor 4 high negative loadings in CaCO3 and pH, and factor 5 high loadings in sodium and potassium. The experimental variograms of the extracted factors showed that the Gaussian model was the most precise to model factor 1, the linear to model factor 2 and the wave hole effect to model factor 3, 4 and 5. The maps produced confirm the patternd observed in the experimental variograms. Factor 1 and 2

  9. Principal component analysis as a tool for library design: a case study investigating natural products, brand-name drugs, natural product-like libraries, and drug-like libraries.

    Science.gov (United States)

    Wenderski, Todd A; Stratton, Christopher F; Bauer, Renato A; Kopp, Felix; Tan, Derek S

    2015-01-01

    Principal component analysis (PCA) is a useful tool in the design and planning of chemical libraries. PCA can be used to reveal differences in structural and physicochemical parameters between various classes of compounds by displaying them in a convenient graphical format. Herein, we demonstrate the use of PCA to gain insight into structural features that differentiate natural products, synthetic drugs, natural product-like libraries, and drug-like libraries, and show how the results can be used to guide library design.

  10. Statistical Significance of the Contribution of Variables to the PCA Solution: An Alternative Permutation Strategy

    Science.gov (United States)

    Linting, Marielle; van Os, Bart Jan; Meulman, Jacqueline J.

    2011-01-01

    In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…

  11. Functional Generalized Structured Component Analysis.

    Science.gov (United States)

    Suk, Hye Won; Hwang, Heungsun

    2016-12-01

    An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.

  12. Classification of alloys using laser induced breakdown spectroscopy with principle component analysis

    Science.gov (United States)

    Syuhada Mangsor, Aneez; Haider Rizvi, Zuhaib; Chaudhary, Kashif; Safwan Aziz, Muhammad

    2018-05-01

    The study of atomic spectroscopy has contributed to a wide range of scientific applications. In principle, laser induced breakdown spectroscopy (LIBS) method has been used to analyse various types of matter regardless of its physical state, either it is solid, liquid or gas because all elements emit light of characteristic frequencies when it is excited to sufficiently high energy. The aim of this work was to analyse the signature spectrums of each element contained in three different types of samples. Metal alloys of Aluminium, Titanium and Brass with the purities of 75%, 80%, 85%, 90% and 95% were used as the manipulated variable and their LIBS spectra were recorded. The characteristic emission lines of main elements were identified from the spectra as well as its corresponding contents. Principal component analysis (PCA) was carried out using the data from LIBS spectra. Three obvious clusters were observed in 3-dimensional PCA plot which corresponding to the different group of alloys. Findings from this study showed that LIBS technology with the help of principle component analysis could conduct the variety discrimination of alloys demonstrating the capability of LIBS-PCA method in field of spectro-analysis. Thus, LIBS-PCA method is believed to be an effective method for classifying alloys with different percentage of purifications, which was high-cost and time-consuming before.

  13. On the structure of dynamic principal component analysis used in statistical process monitoring

    DEFF Research Database (Denmark)

    Vanhatalo, Erik; Kulahci, Murat; Bergquist, Bjarne

    2017-01-01

    When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time...... for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using...... driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method...

  14. PCA based clustering for brain tumor segmentation of T1w MRI images.

    Science.gov (United States)

    Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

    2017-03-01

    Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  15. Principal component analysis of FDG PET in amnestic MCI

    International Nuclear Information System (INIS)

    Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido; Salmaso, Dario; Morbelli, Silvia; Piccardo, Arnoldo; Larsson, Stig A.; Pagani, Marco

    2008-01-01

    The purpose of the study is to evaluate the combined accuracy of episodic memory performance and 18 F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). 18 F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and 18 F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

  16. Principal component analysis of FDG PET in amnestic MCI

    Energy Technology Data Exchange (ETDEWEB)

    Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido [University of Genoa, Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, Genoa (Italy); S. Martino Hospital, Alzheimer Evaluation Unit, Genoa (Italy); S. Martino Hospital, Head-Neck Department, Genoa (Italy); Salmaso, Dario [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Morbelli, Silvia [University of Genoa, Nuclear Medicine Unit, Department of Internal Medicine, Genoa (Italy); Piccardo, Arnoldo [Galliera Hospital, Nuclear Medicine Unit, Department of Imaging Diagnostics, Genoa (Italy); Larsson, Stig A. [Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden); Pagani, Marco [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden)

    2008-12-15

    The purpose of the study is to evaluate the combined accuracy of episodic memory performance and {sup 18}F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). {sup 18}F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and {sup 18}F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

  17. Principal component analysis of NEXAFS spectra for molybdenum speciation in hydrotreating catalysts

    International Nuclear Information System (INIS)

    Faro Junior, Arnaldo da C.; Rodrigues, Victor de O.; Eon, Jean-G.; Rocha, Angela S.

    2010-01-01

    Bulk and supported molybdenum based catalysts, modified by nickel, phosphorous or tungsten were studied by NEXAFS spectroscopy at the Mo L III and L II edges. The techniques of principal component analysis (PCA) together with a linear combination analysis (LCA) allowed the detection and quantification of molybdenum atoms in two different coordination states in the oxide form of the catalysts, namely tetrahedral and octahedral coordination. (author)

  18. Development of motion image prediction method using principal component analysis

    International Nuclear Information System (INIS)

    Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

    2012-01-01

    Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

  19. Interpretable functional principal component analysis.

    Science.gov (United States)

    Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

    2016-09-01

    Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.

  20. An application of principal component analysis to the clavicle and clavicle fixation devices.

    Science.gov (United States)

    Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

    2010-03-26

    Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

  1. An application of principal component analysis to the clavicle and clavicle fixation devices

    Directory of Open Access Journals (Sweden)

    Fitzpatrick David

    2010-03-01

    Full Text Available Abstract Background Principal component analysis (PCA enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and methods Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. Results The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. Discussion And Conclusions This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

  2. Understanding deformation mechanisms during powder compaction using principal component analysis of compression data.

    Science.gov (United States)

    Roopwani, Rahul; Buckner, Ira S

    2011-10-14

    Principal component analysis (PCA) was applied to pharmaceutical powder compaction. A solid fraction parameter (SF(c/d)) and a mechanical work parameter (W(c/d)) representing irreversible compression behavior were determined as functions of applied load. Multivariate analysis of the compression data was carried out using PCA. The first principal component (PC1) showed loadings for the solid fraction and work values that agreed with changes in the relative significance of plastic deformation to consolidation at different pressures. The PC1 scores showed the same rank order as the relative plasticity ranking derived from the literature for common pharmaceutical materials. The utility of PC1 in understanding deformation was extended to binary mixtures using a subset of the original materials. Combinations of brittle and plastic materials were characterized using the PCA method. The relationships between PC1 scores and the weight fractions of the mixtures were typically linear showing ideal mixing in their deformation behaviors. The mixture consisting of two plastic materials was the only combination to show a consistent positive deviation from ideality. The application of PCA to solid fraction and mechanical work data appears to be an effective means of predicting deformation behavior during compaction of simple powder mixtures. Copyright © 2011 Elsevier B.V. All rights reserved.

  3. Structural analysis of nuclear components

    International Nuclear Information System (INIS)

    Ikonen, K.; Hyppoenen, P.; Mikkola, T.; Noro, H.; Raiko, H.; Salminen, P.; Talja, H.

    1983-05-01

    THe report describes the activities accomplished in the project 'Structural Analysis Project of Nuclear Power Plant Components' during the years 1974-1982 in the Nuclear Engineering Laboratory at the Technical Research Centre of Finland. The objective of the project has been to develop Finnish expertise in structural mechanics related to nuclear engineering. The report describes the starting point of the research work, the organization of the project and the research activities on various subareas. Further the work done with computer codes is described and also the problems which the developed expertise has been applied to. Finally, the diploma works, publications and work reports, which are mainly in Finnish, are listed to give a view of the content of the project. (author)

  4. Sparx PCA Module

    Energy Technology Data Exchange (ETDEWEB)

    2017-04-25

    Sparx, a new environment for Cryo-EM image processing; Cryo-EM, Single particle reconstruction, principal component analysis; Hardware Req.: PC, MAC, Supercomputer, Mainframe, Multiplatform, Workstation. Software Req.: operating system is Unix; Compiler C++; type of files: source code, object library, executable modules, compilation instructions; sample problem input data. Location/transmission: http://sparx-em.org; User manual & paper: http://sparx-em.org;

  5. Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

    Directory of Open Access Journals (Sweden)

    A. Ahmad

    2012-06-01

    Full Text Available Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA of Moderate Resolution Imaging Spectroradiometer (MODIS data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4, three thermal bands (29, 31 and 32, the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

  6. Classification of calcium supplements through application of principal component analysis: a study by inaa and aas

    International Nuclear Information System (INIS)

    Waheed, S.; Rahman, S.; Siddique, N.

    2013-01-01

    Different types of Ca supplements are available in the local markets of Pakistan. It is sometimes difficult to classify these with respect to their composition. In the present work principal component analysis (PCA) technique was applied to classify different Ca supplements on the basis of their elemental data obtained using instrumental neutron activation analysis (INAA) and atomic absorption spectrometry (AAS) techniques. The graphical representation of principal component analysis (PCA) scores utilizing intricate analytical data successfully generated four different types of Ca supplements with compatible samples grouped together. These included Ca supplements with CaCO/sub 3/as Ca source along with vitamin C, the supplements with CaCO/sub 3/ as Ca source along with vitamin D, Supplements with Ca from bone meal and supplements with chelated calcium. (author)

  7. Sensor Failure Detection of FASSIP System using Principal Component Analysis

    Science.gov (United States)

    Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

    2018-02-01

    In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

  8. 1H NMR and PCA-based analysis revealed variety dependent changes in phenolic contents of apple fruit after drying.

    Science.gov (United States)

    Francini, Alessandra; Romeo, Stefania; Cifelli, Mario; Gori, Daniele; Domenici, Valentina; Sebastiani, Luca

    2017-04-15

    Dry and fresh apples have been studied monitoring their polyphenolic profiles through 1 H NMR, antioxidant capacity and total polyphenol content. Six ancient and underutilized apple varieties (Mantovana, Mora, Nesta, Cipolla, Ruggina, Sassola) and a commercial one (Golden Delicious) were dried with an air-drying system at 45°C for 19h. Although some of their polyphenol constituents were lost during drying, the antioxidant capacity of some apple varieties remained higher compared to Golden Delicious. This result is very important for ancient and underutilized varieties that are not consumed on large scale as fresh product since they have low attractiveness, due to their ugly appearance. Combining quantitative NMR spectroscopy with principal component analysis we have identified and quantified several polyphenols (such as catechin, epicathechin, and chlorogenic acid) that are important to establish the nutraceutical value of the different investigated apple varieties. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

    Science.gov (United States)

    Gupta, Rajarshi

    2016-05-01

    Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

  10. Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit

    Directory of Open Access Journals (Sweden)

    Selin Aviyente

    2010-01-01

    Full Text Available Joint time-frequency representations offer a rich representation of event related potentials (ERPs that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.

  11. Preliminary study of soil permeability properties using principal component analysis

    Science.gov (United States)

    Yulianti, M.; Sudriani, Y.; Rustini, H. A.

    2018-02-01

    Soil permeability measurement is undoubtedly important in carrying out soil-water research such as rainfall-runoff modelling, irrigation water distribution systems, etc. It is also known that acquiring reliable soil permeability data is rather laborious, time-consuming, and costly. Therefore, it is desirable to develop the prediction model. Several studies of empirical equations for predicting permeability have been undertaken by many researchers. These studies derived the models from areas which soil characteristics are different from Indonesian soil, which suggest a possibility that these permeability models are site-specific. The purpose of this study is to identify which soil parameters correspond strongly to soil permeability and propose a preliminary model for permeability prediction. Principal component analysis (PCA) was applied to 16 parameters analysed from 37 sites consist of 91 samples obtained from Batanghari Watershed. Findings indicated five variables that have strong correlation with soil permeability, and we recommend a preliminary permeability model, which is potential for further development.

  12. Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

    International Nuclear Information System (INIS)

    Yang Hong-Xing; Fu Hong-Bo; Wang Hua-Dong; Jia Jun-Wei; Dong Feng-Zhong; Sigrist, Markus W

    2016-01-01

    Laser-induced breakdown spectroscopy (LIBS) is a versatile tool for both qualitative and quantitative analysis. In this paper, LIBS combined with principal component analysis (PCA) and support vector machine (SVM) is applied to rock analysis. Fourteen emission lines including Fe, Mg, Ca, Al, Si, and Ti are selected as analysis lines. A good accuracy (91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA. It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program, but also solve the problem of linear inseparability by combining PCA and SVM. By this method, the ability of LIBS to classify rock is validated. (paper)

  13. [Principal component analysis and cluster analysis of inorganic elements in sea cucumber Apostichopus japonicus].

    Science.gov (United States)

    Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie

    2011-11-01

    The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.

  14. THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

    OpenAIRE

    HRISTIAN Liliana; OSTAFE Maria Magdalena; BORDEIANU Demetra Lacramioara; APOSTOL Laura Liliana

    2017-01-01

    The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA). There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A s...

  15. Latitude-Time Total Electron Content Anomalies as Precursors to Japan's Large Earthquakes Associated with Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Jyh-Woei Lin

    2011-01-01

    Full Text Available The goal of this study is to determine whether principal component analysis (PCA can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies. PCA is applied to latitude-time (mean-of-a-month ionospheric total electron content (TEC records collected from the Japan GEONET network to detect TEC anomalies associated with 18 earthquakes in Japan (M≥6.0 from 2000 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 18 earthquakes occurred. After reviewing months when no M≥6.0 earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 18 earthquakes indicate earthquake associated TEC anomalies. Previously PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers, who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window.

  16. Identification of Tibicen cicada species by a Principal Components Analysis of their songs

    Directory of Open Access Journals (Sweden)

    Eiji Ohya

    2004-06-01

    Full Text Available Specific identification of three Tibicen cicadas, T. japonicus, T. flammatus and T. bihamatus, by their chirping sounds was carried out using Principal Components Analysis (PCA. High quality recordings of each species were used as the standards. The peak and mean frequencies and the pulse rate were used as the variables. Out of 12 samples recorded in the fields one fell in the vicinity of T. japonicus and all other were positioned near T. bihamatus. Then the cluster analysis of the PCA scores clearly separated each species and allocated the samples in the same way.A identificação de três espécies de cigarras do gênero Tibicen, T. japonicus, T. flammatus e T. bihamatus, através de seus sons estridentes foi realizada por meio da Análise de Componentes Principais (PCA. Gravações de alta fidelidade de cada espécie foram usadas como referencias. As variáveis usadas foram as freqüências máxima e média e a taxa de pulsos. Das 12 amostras gravadas no campo, uma foi colocada perto de T. japonicus e as outras perto de T. bihamatus. A análise de conglomerados dos valores da PCA separou claramente cada espécie e posicionou as amostras da mesma maneira.

  17. Association test based on SNP set: logistic kernel machine based test vs. principal component analysis.

    Directory of Open Access Journals (Sweden)

    Yang Zhao

    Full Text Available GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM and principal components analysis based approach (PCA using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

  18. Pengenalan Wajah Pada Sistem Presensi Menggunakan Metode Dynamic Times Wrapping, Principal Component Analysis dan Gabor Wavelet

    Directory of Open Access Journals (Sweden)

    Romi Wiryadinata

    2016-03-01

    Full Text Available Presensi is a logging attendance, part of activity reporting an institution, or a component institution itself which contains the presence data compiled and arranged so that it is easy to search for and used when required at any time by the parties concerned. Computer application developed in the presensi system is a computer application that can recognize a person's face using only a webcam. Face recognition in this study using a webcam to capture an image of the room at any given time who later identified the existing faces. Some of the methods used in the research here is a method of the Dynamic Times Wrapping (DTW, Principal Component Analysis (PCA and Gabor Wavelet. This system, used in testing with normal facial image expression. The success rate of the introduction with the normal expression of face image using DTW amounting to 80%, 100% and PCA Gabor wavelet 97%

  19. Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

    Science.gov (United States)

    Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

    2017-09-01

    Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

  20. Portable XRF and principal component analysis for bill characterization in forensic science.

    Science.gov (United States)

    Appoloni, C R; Melquiades, F L

    2014-02-01

    Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Principal component regression analysis with SPSS.

    Science.gov (United States)

    Liu, R X; Kuang, J; Gong, Q; Hou, X L

    2003-06-01

    The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

  2. Petrology of Antarctic Eucrites PCA 91078 and PCA 91245

    Science.gov (United States)

    Howard, L. M.; Domanik, K. J.; Drake, M. J.; Mittlefehldt, D. W.

    2002-01-01

    Antarctic eucrites PCA 91078 and PCA 91245, are petrographically characterized and found to be unpaired, type 6, basaltic eucrites. Observed textures that provide insight into the petrogenesis of these meteorites are also discussed. Additional information is contained in the original extended abstract.

  3. Health status monitoring for ICU patients based on locally weighted principal component analysis.

    Science.gov (United States)

    Ding, Yangyang; Ma, Xin; Wang, Youqing

    2018-03-01

    Intelligent status monitoring for critically ill patients can help medical stuff quickly discover and assess the changes of disease and then make appropriate treatment strategy. However, general-type monitoring model now widely used is difficult to adapt the changes of intensive care unit (ICU) patients' status due to its fixed pattern, and a more robust, efficient and fast monitoring model should be developed to the individual. A data-driven learning approach combining locally weighted projection regression (LWPR) and principal component analysis (PCA) is firstly proposed and applied to monitor the nonlinear process of patients' health status in ICU. LWPR is used to approximate the complex nonlinear process with local linear models, in which PCA could be further applied to status monitoring, and finally a global weighted statistic will be acquired for detecting the possible abnormalities. Moreover, some improved versions are developed, such as LWPR-MPCA and LWPR-JPCA, which also have superior performance. Eighteen subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and two vital signs of each subject were chosen for online monitoring. The proposed method was compared with several existing methods including traditional PCA, Partial least squares (PLS), just in time learning combined with modified PCA (L-PCA), and Kernel PCA (KPCA). The experimental results demonstrated that the mean fault detection rate (FDR) of PCA can be improved by 41.7% after adding LWPR. The mean FDR of LWPR-MPCA was increased by 8.3%, compared with the latest reported method L-PCA. Meanwhile, LWPR spent less training time than others, especially KPCA. LWPR is first introduced into ICU patients monitoring and achieves the best monitoring performance including adaptability to changes in patient status, sensitivity for abnormality detection as well as its fast learning speed and low computational complexity. The algorithm

  4. Model reduction by weighted Component Cost Analysis

    Science.gov (United States)

    Kim, Jae H.; Skelton, Robert E.

    1990-01-01

    Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called 'component cost' to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. A reduced-order model of the given system may be obtained by deleting those components that have the smallest component costs. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. Closed-form analytical expressions of component costs are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems. A numerical example for MINIMAST system is presented.

  5. Enhancing the discussion of alternatives in EIA using principle component analysis leads to improved public involvement

    International Nuclear Information System (INIS)

    Kamijo, Tetsuya; Huang, Guangwei

    2017-01-01

    The purpose of this study is to show the effectiveness of principle component analysis (PCA) as a method of alternatives analysis useful for improving the discussion of alternatives and public involvement. This study examined public consultations by applying quantitative text analysis (QTA) to the minutes of meetings and showed a positive correlation between the discussion of alternatives and the sense of public involvement. The discussion of alternatives may improve public involvement. A table of multiple criteria analysis for alternatives with detailed scores may exclude the public from involvement due to the general public's limited capacity to understand the mathematical algorithm and to process too much information. PCA allowed for the reduction of multiple criteria down to a small number of uncorrelated variables (principle components), a display of the merits and demerits of the alternatives, and potentially made the identification of preferable alternatives by the stakeholders easier. PCA is likely to enhance the discussion of alternatives and as a result, lead to improved public involvement.

  6. Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.

    Science.gov (United States)

    Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G B

    2016-04-01

    To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  7. Pipeline monitoring using acoustic principal component analysis recognition with the Mel scale

    International Nuclear Information System (INIS)

    Wan, Chunfeng; Mita, Akira

    2009-01-01

    In modern cities, many important pipelines are laid underground. In order to prevent these lifeline infrastructures from accidental damage, monitoring systems are becoming indispensable. Third party activities were shown by recent reports to be a major cause of pipeline damage. Potential damage threat to the pipeline can be identified by detecting dangerous construction equipment nearby by studying the surrounding noise. Sound recognition technologies are used to identify them by their sounds, which can easily be captured by small sensors deployed along the pipelines. Pattern classification methods based on principal component analysis (PCA) were used to recognize the sounds from road cutters. In this paper, a Mel residual, i.e. the PCA residual in the Mel scale, is proposed to be the recognition feature. Determining if a captured sound belongs to a road cutter only requires checking how large its Mel residual is. Experiments were conducted and results showed that the proposed Mel-residual-based PCA recognition worked very well. The proposed Mel PCA residual recognition method will be very useful for pipeline monitoring systems to prevent accidental breakage and to ensure the safety of underground lifeline infrastructures

  8. Regularized Pre-image Estimation for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

  9. Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

    Science.gov (United States)

    Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.; hide

    2015-01-01

    The traditional method of applying a digital optimal filter to measure X-ray pulses from transition-edge sensor (TES) devices does not achieve the best energy resolution when the signals have a highly non-linear response to energy, or the noise is non-stationary during the pulse. We present an implementation of a method to analyze X-ray data from TESs, which is based upon principal component analysis (PCA). Our method separates the X-ray signal pulse into orthogonal components that have the largest variance. We typically recover pulse height, arrival time, differences in pulse shape, and the variation of pulse height with detector temperature. These components can then be combined to form a representation of pulse energy. An added value of this method is that by reporting information on more descriptive parameters (as opposed to a single number representing energy), we generate a much more complete picture of the pulse received. Here we report on progress in developing this technique for future implementation on X-ray telescopes. We used an 55Fe source to characterize Mo/Au TESs. On the same dataset, the PCA method recovers a spectral resolution that is better by a factor of two than achievable with digital optimal filters.

  10. Fusion-component lifetime analysis

    International Nuclear Information System (INIS)

    Mattas, R.F.

    1982-09-01

    A one-dimensional computer code has been developed to examine the lifetime of first-wall and impurity-control components. The code incorporates the operating and design parameters, the material characteristics, and the appropriate failure criteria for the individual components. The major emphasis of the modeling effort has been to calculate the temperature-stress-strain-radiation effects history of a component so that the synergystic effects between sputtering erosion, swelling, creep, fatigue, and crack growth can be examined. The general forms of the property equations are the same for all materials in order to provide the greatest flexibility for materials selection in the code. The individual coefficients within the equations are different for each material. The code is capable of determining the behavior of a plate, composed of either a single or dual material structure, that is either totally constrained or constrained from bending but not from expansion. The code has been utilized to analyze the first walls for FED/INTOR and DEMO and to analyze the limiter for FED/INTOR

  11. Three-Dimensional X-Ray Photoelectron Tomography on the Nanoscale: Limits of Data Processing by Principal Component Analysis

    DEFF Research Database (Denmark)

    Hajati, S.; Walton, J.; Tougaard, S.

    2013-01-01

    In a previous article, we studied the influence of spectral noise on a new method for three-dimensional X-ray photoelectron spectroscopy (3D XPS) imaging, which is based on analysis of the XPS peak shape [Hajati, S., Tougaard, S., Walton, J. & Fairley, N. (2008). Surf Sci 602, 3064-3070]. Here, we...... study in more detail the influence of noise reduction by principal component analysis (PCA) on 3D XPS images of carbon contamination of a patterned oxidized silicon sample and on 3D XPS images of Ag covered by a nanoscale patterned octadiene layer. PCA is very efficient for noise reduction, and using...... acquisition time. A small additional amount of information is obtained by using up to five PCA factors, but due to the increased noise level, this information can only be extracted if the intensity of the start and end points for each spectrum are obtained as averages over several energy points....

  12. Component of the risk analysis

    International Nuclear Information System (INIS)

    Martinez, I.; Campon, G.

    2013-01-01

    The power point presentation reviews issues like analysis of risk (Codex), management risk, preliminary activities manager, relationship between government and industries, microbiological danger and communication of risk

  13. Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China.

    Science.gov (United States)

    Zhang, Zhiming; Ouyang, Zhiyun; Xiao, Yi; Xiao, Yang; Xu, Weihua

    2017-06-01

    Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10 3  km 2 , which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

  14. An analytics of electricity consumption characteristics based on principal component analysis

    Science.gov (United States)

    Feng, Junshu

    2018-02-01

    Abstract . More detailed analysis of the electricity consumption characteristics can make demand side management (DSM) much more targeted. In this paper, an analytics of electricity consumption characteristics based on principal component analysis (PCA) is given, which the PCA method can be used in to extract the main typical characteristics of electricity consumers. Then, electricity consumption characteristics matrix is designed, which can make a comparison of different typical electricity consumption characteristics between different types of consumers, such as industrial consumers, commercial consumers and residents. In our case study, the electricity consumption has been mainly divided into four characteristics: extreme peak using, peak using, peak-shifting using and others. Moreover, it has been found that industrial consumers shift their peak load often, meanwhile commercial and residential consumers have more peak-time consumption. The conclusions can provide decision support of DSM for the government and power providers.

  15. Processing of spectral X-ray data with principal components analysis

    CERN Document Server

    Butler, A P H; Cook, N J; Butzer, J; Schleich, N; Tlustos, L; Scott, N; Grasset, R; de Ruiter, N; Anderson, N G

    2011-01-01

    The goal of the work was to develop a general method for processing spectral x-ray image data. Principle component analysis (PCA) is a well understood technique for multivariate data analysis and so was investigated. To assess this method, spectral (multi-energy) computed tomography (CT) data was obtained using a Medipix2 detector in a MARS-CT (Medipix All Resolution System). PCA was able to separate bone (calcium) from two elements with k-edges in the X-ray spectrum used (iodine and barium) within a mouse. This has potential clinical application in dual-energy CT systems and future Medipix3 based spectral imaging where up to eight energies can be recorded simultaneously with excellent energy resolution. (c) 2010 Elsevier B.V. All rights reserved.

  16. Model Reduction via Principe Component Analysis and Markov Chain Monte Carlo (MCMC) Methods

    Science.gov (United States)

    Gong, R.; Chen, J.; Hoversten, M. G.; Luo, J.

    2011-12-01

    Geophysical and hydrogeological inverse problems often include a large number of unknown parameters, ranging from hundreds to millions, depending on parameterization and problems undertaking. This makes inverse estimation and uncertainty quantification very challenging, especially for those problems in two- or three-dimensional spatial domains. Model reduction technique has the potential of mitigating the curse of dimensionality by reducing total numbers of unknowns while describing the complex subsurface systems adequately. In this study, we explore the use of principal component analysis (PCA) and Markov chain Monte Carlo (MCMC) sampling methods for model reduction through the use of synthetic datasets. We compare the performances of three different but closely related model reduction approaches: (1) PCA methods with geometric sampling (referred to as 'Method 1'), (2) PCA methods with MCMC sampling (referred to as 'Method 2'), and (3) PCA methods with MCMC sampling and inclusion of random effects (referred to as 'Method 3'). We consider a simple convolution model with five unknown parameters as our goal is to understand and visualize the advantages and disadvantages of each method by comparing their inversion results with the corresponding analytical solutions. We generated synthetic data with noise added and invert them under two different situations: (1) the noised data and the covariance matrix for PCA analysis are consistent (referred to as the unbiased case), and (2) the noise data and the covariance matrix are inconsistent (referred to as biased case). In the unbiased case, comparison between the analytical solutions and the inversion results show that all three methods provide good estimates of the true values and Method 1 is computationally more efficient. In terms of uncertainty quantification, Method 1 performs poorly because of relatively small number of samples obtained, Method 2 performs best, and Method 3 overestimates uncertainty due to inclusion

  17. Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

    Energy Technology Data Exchange (ETDEWEB)

    Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

    2008-01-01

    Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

  18. Registration of dynamic dopamine D2receptor images using principal component analysis

    International Nuclear Information System (INIS)

    Acton, P.D.; Ell, P.J.; Pilowsky, L.S.; Brammer, M.J.; Suckling, J.

    1997-01-01

    This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D 2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D 2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P 123 I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the χ 2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.)

  19. PCA as a practical indicator of OPLS-DA model reliability.

    Science.gov (United States)

    Worley, Bradley; Powers, Robert

    Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

  20. Pattern recognition on X-ray fluorescence records from Copenhagen lake sediments using principal component analysis

    DEFF Research Database (Denmark)

    Schreiber, Norman; Garcia, Emanuel; Kroon, Aart

    2014-01-01

    Principle Component Analysis (PCA) was performed on chemical data of two sediment cores from an urban fresh-water lake in Copenhagen, Denmark. X-ray fluorescence (XRF) core scanning provided the underlying datasets on 13 variables (Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Cd, Pb). Principle......, Fe, Rb) and characterized the content of minerogenic material in the sediment. In case of both cores, PC2 was a good descriptor emphasized as the contamination component. It showed strong linkages with heavy metals (Cu, Zn, Pb), disclosing changing heavy-metal contamination trends across different...

  1. Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Renfu Jia

    2016-01-01

    Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

  2. Mapping brain activity in gradient-echo functional MRI using principal component analysis

    Science.gov (United States)

    Khosla, Deepak; Singh, Manbir; Don, Manuel

    1997-05-01

    The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

  3. Performance evaluation of PCA-based spike sorting algorithms.

    Science.gov (United States)

    Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

    2008-09-01

    Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

  4. Principal Component Analysis of Working Memory Variables during Child and Adolescent Development.

    Science.gov (United States)

    Barriga-Paulino, Catarina I; Rodríguez-Martínez, Elena I; Rojas-Benjumea, María Ángeles; Gómez, Carlos M

    2016-10-03

    Correlation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6-18 years old. The correlation analysis (p 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

  5. Analysis of differences between Western and East-Asian faces based on facial region segmentation and PCA for facial expression recognition

    Science.gov (United States)

    Benitez-Garcia, Gibran; Nakamura, Tomoaki; Kaneko, Masahide

    2017-01-01

    Darwin was the first one to assert that facial expressions are innate and universal, which are recognized across all cultures. However, recent some cross-cultural studies have questioned this assumed universality. Therefore, this paper presents an analysis of the differences between Western and East-Asian faces of the six basic expressions (anger, disgust, fear, happiness, sadness and surprise) focused on three individual facial regions of eyes-eyebrows, nose and mouth. The analysis is conducted by applying PCA for two feature extraction methods: appearance-based by using the pixel intensities of facial parts, and geometric-based by handling 125 feature points from the face. Both methods are evaluated using 4 standard databases for both racial groups and the results are compared with a cross-cultural human study applied to 20 participants. Our analysis reveals that differences between Westerns and East-Asians exist mainly on the regions of eyes-eyebrows and mouth for expressions of fear and disgust respectively. This work presents important findings for a better design of automatic facial expression recognition systems based on the difference between two racial groups.

  6. Principal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy.

    Directory of Open Access Journals (Sweden)

    Susannah G Ellsworth

    Full Text Available Radiation treatment (RT stimulates the release of many immunohumoral factors, complicating the identification of clinically significant cytokine expression patterns. This study used principal component analysis (PCA to analyze cytokines in non-small cell lung cancer (NSCLC patients undergoing RT and explore differences in changes after hypofractionated stereotactic body radiation therapy (SBRT and conventionally fractionated RT (CFRT without or with chemotherapy.The dataset included 141 NSCLC patients treated on prospective clinical protocols; PCA was based on the 128 patients who had complete CK values at baseline and during treatment. Patients underwent SBRT (n = 16, CFRT (n = 18, or CFRT (n = 107 with concurrent chemotherapy (ChRT. Levels of 30 cytokines were measured from prospectively collected platelet-poor plasma samples at baseline, during RT, and after RT. PCA was used to study variations in cytokine levels in patients at each time point.Median patient age was 66, and 22.7% of patients were female. PCA showed that sCD40l, fractalkine/C3, IP10, VEGF, IL-1a, IL-10, and GMCSF were responsible for most variability in baseline cytokine levels. During treatment, sCD40l, IP10, MIP-1b, fractalkine, IFN-r, and VEGF accounted for most changes in cytokine levels. In SBRT patients, the most important players were sCD40l, IP10, and MIP-1b, whereas fractalkine exhibited greater variability in CFRT alone patients. ChRT patients exhibited variability in IFN-γ and VEGF in addition to IP10, MIP-1b, and sCD40l.PCA can identify potentially significant patterns of cytokine expression after fractionated RT. Our PCA showed that inflammatory cytokines dominate post-treatment cytokine profiles, and the changes differ after SBRT versus CFRT, with vs without chemotherapy. Further studies are planned to validate these findings and determine the clinical significance of the cytokine profiles identified by PCA.

  7. Differentiation of live and dead salmonella cells using fourier transform infrared (FTIR) spectroscopy and principle component analysis (PCA) technique

    Science.gov (United States)

    Various technologies have been developed for pathogen detection using optical, electrochemical, biochemical and physical properties. Conventional microbiological methods need time from days to week to get the result. Though this method is very sensitive and accurate, a rapid detection of pathogens i...

  8. Principal Components Analysis (PCA) Image used to characterize the complexity of the seafloor around St. John, USVI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Eight complexity surfaces (mean depth, standard deviation of depth, curvature, plan curvature, profile curvature, rugosity, slope, and slope of slope) were stacked...

  9. An improved principal component analysis based region matching method for fringe direction estimation

    Science.gov (United States)

    He, A.; Quan, C.

    2018-04-01

    The principal component analysis (PCA) and region matching combined method is effective for fringe direction estimation. However, its mask construction algorithm for region matching fails in some circumstances, and the algorithm for conversion of orientation to direction in mask areas is computationally-heavy and non-optimized. We propose an improved PCA based region matching method for the fringe direction estimation, which includes an improved and robust mask construction scheme, and a fast and optimized orientation-direction conversion algorithm for the mask areas. Along with the estimated fringe direction map, filtered fringe pattern by automatic selective reconstruction modification and enhanced fast empirical mode decomposition (ASRm-EFEMD) is used for Hilbert spiral transform (HST) to demodulate the phase. Subsequently, windowed Fourier ridge (WFR) method is used for the refinement of the phase. The robustness and effectiveness of proposed method are demonstrated by both simulated and experimental fringe patterns.

  10. QIM blind video watermarking scheme based on Wavelet transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Nisreen I. Yassin

    2014-12-01

    Full Text Available In this paper, a blind scheme for digital video watermarking is proposed. The security of the scheme is established by using one secret key in the retrieval of the watermark. Discrete Wavelet Transform (DWT is applied on each video frame decomposing it into a number of sub-bands. Maximum entropy blocks are selected and transformed using Principal Component Analysis (PCA. Quantization Index Modulation (QIM is used to quantize the maximum coefficient of the PCA blocks of each sub-band. Then, the watermark is embedded into the selected suitable quantizer values. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility. The computed average PSNR exceeds 45 dB. Finally, the scheme is applied on two medical videos. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment in both cases of regular videos and medical videos.

  11. Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra

    International Nuclear Information System (INIS)

    Li, Boyan; Calvet, Amandine; Casamayou-Boucau, Yannick; Ryder, Alan G.

    2016-01-01

    A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present. CRA correction was achieved by spectral replacement where, the nearest neighbor (NN) spectrum, most spectroscopically similar to the CRA contaminated spectrum and principal components (PCs) obtained by kPCA were both used to generate a robust, best curve fit to the CRA contaminated spectrum. This best fit spectrum then replaced the CRA contaminated spectrum in the dataset. KPCARD efficacy was demonstrated by using simulated data and real Raman spectra collected from solid-state materials. The results showed that KPCARD was fast ( 1 million) Raman datasets. - Highlights: • New rapid, automatable method for cosmic ray artifact correction of Raman spectra. • Uses combination of kernel PCA and noise estimation for artifact identification. • Implements a best fit spectrum replacement correction approach.

  12. Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

    Directory of Open Access Journals (Sweden)

    Shokri Saeid

    2015-01-01

    Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.

  13. Contribution to a Taxonomic Revision of the Sicilian Helichrysum Taxa by PCA Analysis of Their Essential-Oil Compositions.

    Science.gov (United States)

    Maggio, Antonella; Bruno, Maurizio; Guarino, Riccardo; Senatore, Felice; Ilardi, Vincenzo

    2016-02-01

    The chemical profile of the essential oils in ten populations of the genus Helichrysum Mill. (Asteraceae), collected in the loci classici of the nomenclatural types of the taxa endemic to Sicily, were analyzed. Our results confirm that the analysis of secondary metabolites can be used to fingerprint wild populations of Helichrysum, the chemical profiles being coherent with the systematic arrangement of the investigated populations in three main clusters, referring to the aggregates of H. stoechas, H. rupestre, and H. italicum, all belonging to the section Stoechadina. The correct nomenclatural designation of the investigated populations is discussed and the following two new combinations are proposed: Helichrysum preslianum subsp. compactum (Guss.) Maggio, Bruno, Guarino, Senatore & Ilardi and Helichrysum panormitanum subsp. latifolium Maggio, Bruno, Guarino, Senatore & Ilardi. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

  14. 2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

    Directory of Open Access Journals (Sweden)

    Patrik Kamencay

    2014-03-01

    Full Text Available This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA algorithm using canonical correlation analysis (CCA to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method based on PCA-level fusion achieved a very good recognition score of 85%.

  15. Aerodynamic multi-objective integrated optimization based on principal component analysis

    Directory of Open Access Journals (Sweden)

    Jiangtao HUANG

    2017-08-01

    Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

  16. The use of principal component, discriminate and rough sets analysis methods of radiological data

    International Nuclear Information System (INIS)

    Seddeek, M.K.; Kozae, A.M.; Sharshar, T.; Badran, H.M.

    2006-01-01

    In this work, computational methods of finding clusters of multivariate data points were explored using principal component analysis (PCA), discriminate analysis (DA) and rough set analysis (RSA) methods. The variables were the concentrations of four natural isotopes and the texture characteristics of 100 sand samples from the coast of North Sinai, Egypt. Beach and dune sands are the two types of samples included. These methods were used to reduce the dimensionality of multivariate data and as classification and clustering methods. The results showed that the classification of sands in the environment of North Sinai is dependent upon the radioactivity contents of the naturally occurring radioactive materials and not upon the characteristics of the sand. The application of DA enables the creation of a classification rule for sand type and it revealed that samples with high negatively values of the first score have the highest contamination of black sand. PCA revealed that radioactivity concentrations alone can be considered to predict the classification of other samples. The results of RSA showed that only one of the concentrations of 238 U, 226 Ra and 232 Th with 40 K content, can characterize the clusters together with characteristics of the sand. Both PCA and RSA result in the following conclusion: 238 U, 226 Ra and 232 Th behave similarly. RSA revealed that one/two of them may not be considered without affecting the body of knowledge

  17. Principal Component Analysis-Based Pattern Analysis of Dose-Volume Histograms and Influence on Rectal Toxicity

    International Nuclear Information System (INIS)

    Soehn, Matthias; Alber, Markus; Yan Di

    2007-01-01

    Purpose: The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. Methods and Materials: PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as 'eigenmodes,' which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Results: Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe ∼94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses (∼40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. Conclusions: PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches

  18. Circle of Willis Variants: Fetal PCA

    Directory of Open Access Journals (Sweden)

    Amir Shaban

    2013-01-01

    Full Text Available We sought to determine the prevalence of fetal posterior cerebral artery (fPCA and if fPCA was associated with specific stroke etiology and vessel territory affected. This paper is a retrospective review of prospectively identified patients with acute ischemic stroke from July 2008 to December 2010. We defined complete fPCA as absence of a P1 segment linking the basilar with the PCA and partial fPCA as small segment linking the basilar with the PCA. Patients without intracranial vascular imaging were excluded. We compared patients with complete fPCA, partial fPCA, and without fPCA in terms of demographics, stroke severity, distribution, and etiology and factored in whether the stroke was ipsilateral to the fPCA. Of the 536 included patients, 9.5% ( had complete fPCA and 15.1% ( had partial fPCA. Patients with complete fPCA were older and more often female than partial fPCA and no fPCA patients, and significant variation in TOAST classification was detected across groups (. Patients with complete fPCA had less small vessel and more large vessel strokes than patients with no fPCA and partial fPCA. Fetal PCA may predispose to stroke mechanism, but is not associated with vascular distribution, stroke severity, or early outcome.

  19. COMPARING INDEPENDENT COMPONENT ANALYSIS WITH PRINCIPLE COMPONENT ANALYSIS IN DETECTING ALTERATIONS OF PORPHYRY COPPER DEPOSIT (CASE STUDY: ARDESTAN AREA, CENTRAL IRAN

    Directory of Open Access Journals (Sweden)

    S. Mahmoudishadi

    2017-09-01

    Full Text Available The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA and Independent Component Analysis (ICA has been evaluated for the visible and near-infrared (VNIR and Shortwave infrared (SWIR subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6 were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

  20. Comparing Independent Component Analysis with Principle Component Analysis in Detecting Alterations of Porphyry Copper Deposit (case Study: Ardestan Area, Central Iran)

    Science.gov (United States)

    Mahmoudishadi, S.; Malian, A.; Hosseinali, F.

    2017-09-01

    The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

  1. A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

    Science.gov (United States)

    Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

    2018-04-01

    For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

  2. Diagnose Test-Taker's Profile in Terms of Core Profile Patterns: Principal Component (PC) vs. Profile Analysis via MDS (PAMS) Approaches.

    Science.gov (United States)

    Kim, Se-Kang; Davison, Mark L.

    A study was conducted to examine how principal components analysis (PCA) and Profile Analysis via Multidimensional Scaling (PAMS) can be used to diagnose individuals observed score profiles in terms of core profile patterns identified by each method. The standardization sample from the Wechsler Intelligence Scale for Children, Third Edition…

  3. Oil classification using X-ray scattering and principal component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T., E-mail: dani.almeida84@gmail.com, E-mail: ricardo@lin.ufrj.br, E-mail: amandass@bioqmed.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil); Oliveira, Davi F.; Anjos, Marcelino J., E-mail: davi.oliveira@uerj.br, E-mail: marcelin@uerj.br [Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ (Brazil). Inst. de Fisica Armando Dias Tavares

    2015-07-01

    X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

  4. Oil classification using X-ray scattering and principal component analysis

    International Nuclear Information System (INIS)

    Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T.; Oliveira, Davi F.; Anjos, Marcelino J.

    2015-01-01

    X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

  5. THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

    Directory of Open Access Journals (Sweden)

    HRISTIAN Liliana

    2017-05-01

    Full Text Available The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA. There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A solution to this problem can be the application of a method of factorial analysis, the so-called Principal Component Analysis, with the final goal of establishing and analyzing those variables which influence in a significant manner the internal structure of combed wool fabrics according to armire type. By applying PCA it is obtained a small number of the linear combinations (principal components from a set of variables, describing the internal structure of the fabrics, which can hold as much information as possible from the original variables. Data analysis is an important initial step in decision making, allowing identification of the causes that lead to a decision- making situations. Thus it is the action of transforming the initial data in order to extract useful information and to facilitate reaching the conclusions. The process of data analysis can be defined as a sequence of steps aimed at formulating hypotheses, collecting primary information and validation, the construction of the mathematical model describing this phenomenon and reaching these conclusions about the behavior of this model.

  6. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

    Science.gov (United States)

    Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

    2015-01-01

    In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

  7. Research on distributed heterogeneous data PCA algorithm based on cloud platform

    Science.gov (United States)

    Zhang, Jin; Huang, Gang

    2018-05-01

    Principal component analysis (PCA) of heterogeneous data sets can solve the problem that centralized data scalability is limited. In order to reduce the generation of intermediate data and error components of distributed heterogeneous data sets, a principal component analysis algorithm based on heterogeneous data sets under cloud platform is proposed. The algorithm performs eigenvalue processing by using Householder tridiagonalization and QR factorization to calculate the error component of the heterogeneous database associated with the public key to obtain the intermediate data set and the lost information. Experiments on distributed DBM heterogeneous datasets show that the model method has the feasibility and reliability in terms of execution time and accuracy.

  8. Evaluation of Staining-Dependent Colour Changes in Resin Composites Using Principal Component Analysis.

    Science.gov (United States)

    Manojlovic, D; Lenhardt, L; Milićević, B; Antonov, M; Miletic, V; Dramićanin, M D

    2015-10-09

    Colour changes in Gradia Direct™ composite after immersion in tea, coffee, red wine, Coca-Cola, Colgate mouthwash, and distilled water were evaluated using principal component analysis (PCA) and the CIELAB colour coordinates. The reflection spectra of the composites were used as input data for the PCA. The output data (scores and loadings) provided information about the magnitude and origin of the surface reflection changes after exposure to the staining solutions. The reflection spectra of the stained samples generally exhibited lower reflection in the blue spectral range, which was manifested in the lower content of the blue shade for the samples. Both analyses demonstrated the high staining abilities of tea, coffee, and red wine, which produced total colour changes of 4.31, 6.61, and 6.22, respectively, according to the CIELAB analysis. PCA revealed subtle changes in the reflection spectra of composites immersed in Coca-Cola, demonstrating Coca-Cola's ability to stain the composite to a small degree.

  9. [Determination of the Plant Origin of Licorice Oil Extract, a Natural Food Additive, by Principal Component Analysis Based on Chemical Components].

    Science.gov (United States)

    Tada, Atsuko; Ishizuki, Kyoko; Sugimoto, Naoki; Yoshimatsu, Kayo; Kawahara, Nobuo; Suematsu, Takako; Arifuku, Kazunori; Fukai, Toshio; Tamura, Yukiyoshi; Ohtsuki, Takashi; Tahara, Maiko; Yamazaki, Takeshi; Akiyama, Hiroshi

    2015-01-01

    "Licorice oil extract" (LOE) (antioxidant agent) is described in the notice of Japanese food additive regulations as a material obtained from the roots and/or rhizomes of Glycyrrhiza uralensis, G. inflata or G. glabra. In this study, we aimed to identify the original Glycyrrhiza species of eight food additive products using LC/MS. Glabridin, a characteristic compound in G. glabra, was specifically detected in seven products, and licochalcone A, a characteristic compound in G. inflata, was detected in one product. In addition, Principal Component Analysis (PCA) (a kind of multivariate analysis) using the data of LC/MS or (1)H-NMR analysis was performed. The data of thirty-one samples, including LOE products used as food additives, ethanol extracts of various Glycyrrhiza species and commercially available Glycyrrhiza species-derived products were assessed. Based on the PCA results, the majority of LOE products was confirmed to be derived from G. glabra. This study suggests that PCA using (1)H-NMR analysis data is a simple and useful method to identify the plant species of origin of natural food additive products.

  10. Structured Performance Analysis for Component Based Systems

    OpenAIRE

    Salmi , N.; Moreaux , Patrice; Ioualalen , M.

    2012-01-01

    International audience; The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. % This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed...

  11. Avoiding Optimal Mean ℓ2,1-Norm Maximization-Based Robust PCA for Reconstruction.

    Science.gov (United States)

    Luo, Minnan; Nie, Feiping; Chang, Xiaojun; Yang, Yi; Hauptmann, Alexander G; Zheng, Qinghua

    2017-04-01

    Robust principal component analysis (PCA) is one of the most important dimension-reduction techniques for handling high-dimensional data with outliers. However, most of the existing robust PCA presupposes that the mean of the data is zero and incorrectly utilizes the average of data as the optimal mean of robust PCA. In fact, this assumption holds only for the squared [Formula: see text]-norm-based traditional PCA. In this letter, we equivalently reformulate the objective of conventional PCA and learn the optimal projection directions by maximizing the sum of projected difference between each pair of instances based on [Formula: see text]-norm. The proposed method is robust to outliers and also invariant to rotation. More important, the reformulated objective not only automatically avoids the calculation of optimal mean and makes the assumption of centered data unnecessary, but also theoretically connects to the minimization of reconstruction error. To solve the proposed nonsmooth problem, we exploit an efficient optimization algorithm to soften the contributions from outliers by reweighting each data point iteratively. We theoretically analyze the convergence and computational complexity of the proposed algorithm. Extensive experimental results on several benchmark data sets illustrate the effectiveness and superiority of the proposed method.

  12. Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

    Science.gov (United States)

    Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

    2016-04-01

    Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

  13. Analysis Method for Integrating Components of Product

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Jun Ho [Inzest Co. Ltd, Seoul (Korea, Republic of); Lee, Kun Sang [Kookmin Univ., Seoul (Korea, Republic of)

    2017-04-15

    This paper presents some of the methods used to incorporate the parts constituting a product. A new relation function concept and its structure are introduced to analyze the relationships of component parts. This relation function has three types of information, which can be used to establish a relation function structure. The relation function structure of the analysis criteria was established to analyze and present the data. The priority components determined by the analysis criteria can be integrated. The analysis criteria were divided based on their number and orientation, as well as their direct or indirect characteristic feature. This paper presents a design algorithm for component integration. This algorithm was applied to actual products, and the components inside the product were integrated. Therefore, the proposed algorithm was used to conduct research to improve the brake discs for bicycles. As a result, an improved product similar to the related function structure was actually created.

  14. Analysis Method for Integrating Components of Product

    International Nuclear Information System (INIS)

    Choi, Jun Ho; Lee, Kun Sang

    2017-01-01

    This paper presents some of the methods used to incorporate the parts constituting a product. A new relation function concept and its structure are introduced to analyze the relationships of component parts. This relation function has three types of information, which can be used to establish a relation function structure. The relation function structure of the analysis criteria was established to analyze and present the data. The priority components determined by the analysis criteria can be integrated. The analysis criteria were divided based on their number and orientation, as well as their direct or indirect characteristic feature. This paper presents a design algorithm for component integration. This algorithm was applied to actual products, and the components inside the product were integrated. Therefore, the proposed algorithm was used to conduct research to improve the brake discs for bicycles. As a result, an improved product similar to the related function structure was actually created.

  15. An Investigation of Potential Fraud in Commercial Orange Juice Products in Malaysian Market by Cluster Analysis and Principal Component Analysis

    International Nuclear Information System (INIS)

    Keng, S.E.; Abbas Fadhl Mubarek Al-Karkhi; Mohd Khairuddin Mohd Talib; Azhar Mat Easa; Hoong, C.L.

    2015-01-01

    This study was triggered by Malaysia Ministry of Health to monitor quality of commercial orange juice products sold in Malaysia market. A total of 19 orange juice samples from 14 different brands of packed orange juice products and 5 different brands of fresh orange fruit juices were analyzed for total soluble solids content, total titratable acidity, sugar composition and amino acid profiles. Hierarchical Cluster analysis (HCA) and Principal component analysis (PCA) on amino acid composition alone allowed visual discrimination between fresh squeezed orange juices and commercial packed orange juices. Suspicion of mislabel was raised in cases of miss-classification. (author)

  16. Redundancy or heterogeneity in the electric activity of the biceps brachii muscle? Added value of PCA-processed multi-channel EMG muscle activation estimates in a parallel-fibered muscle

    NARCIS (Netherlands)

    Staudenmann, D.; Stegeman, D.F.; van Dieen, J.H.

    2013-01-01

    Conventional bipolar EMG provides imprecise muscle activation estimates due to possibly heterogeneous activity within muscles and due to improper alignment of the electrodes with the muscle fibers. Principal component analysis (PCA), applied on multi-channel monopolar EMG yielded substantial

  17. PCaPAC 2006 Proceedings

    Energy Technology Data Exchange (ETDEWEB)

    Pavel Chevtsov; Matthew Bickley (Eds.)

    2007-03-30

    The 6-th international PCaPAC (Personal Computers and Particle Accelerator Controls) workshop was held at Jefferson Lab, Newport News, Virginia, from October 24-27, 2006. The main objectives of the conference were to discuss the most important issues of the use of PCs and modern IT technologies for controls of accelerators and to give scientists, engineers, and technicians a forum to exchange the ideas on control problems and their solutions. The workshop consisted of plenary sessions and poster sessions. No parallel sessions were held.Totally, more than seventy oral and poster presentations as well as tutorials were made during the conference, on the basis of which about fifty papers were submitted by the authors and included in this publication. This printed version of the PCaPAC 2006 Proceedings is published at Jefferson Lab according to the decision of the PCaPAC International Program Committee of October 26, 2006.

  18. Preliminary PCA/TT Results on MRO CRISM Multispectral Images

    Science.gov (United States)

    Klassen, David R.; Smith, M. D.

    2010-10-01

    Mars Reconnaissance Orbiter arrived at Mars in March 2006 and by September had achieved its science-phase orbit with the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) beginning its visible to near-infrared (VIS/NIR) spectral imaging shortly thereafter. One goal of CRISM is to fill in the spatial gaps between the various targeted observations, eventually mapping the entire surface. Due to the large volume of data this would create, the instrument works in a reduced spectral sampling mode creating "multispectral” images. From these data we can create image cubes using 64 wavelengths from 0.410 to 3.923 µm. We present here our analysis of these multispectral mode data products using Principal Components Analysis (PCA) and Target Transformation (TT) [1]. Previous work with ground-based images [2-5] has shown that over an entire visible hemisphere, there are only three to four meaningful components using 32-105 wavelengths over 1.5-4.1 µm the first two are consistent over all temporal scales. The TT retrieved spectral endmembers show nearly the same level of consistency [5]. The preliminary work on the CRISM images cubes implies similar results; three to four significant principal components that are fairly consistent over time. These components are then used in TT to find spectral endmembers which can be used to characterize the surface reflectance for future use in radiative transfer cloud optical depth retrievals. We present here the PCA/TT results comparing the principal components and recovered endmembers from six reconstructed CRISM multi-spectral image cubes. References: [1] Bandfield, J. L., et al. (2000) JGR, 105, 9573. [2] Klassen, D. R. and Bell III, J. F. (2001) BAAS 33, 1069. [3] Klassen, D. R. and Bell III, J. F. (2003) BAAS, 35, 936. [4] Klassen, D. R., Wark, T. J., Cugliotta, C. G. (2005) BAAS, 37, 693. [5] Klassen, D. R. (2009) Icarus, 204, 32.

  19. Principal component analysis acceleration of rovibrational coarse-grain models for internal energy excitation and dissociation

    Science.gov (United States)

    Bellemans, Aurélie; Parente, Alessandro; Magin, Thierry

    2018-04-01

    The present work introduces a novel approach for obtaining reduced chemistry representations of large kinetic mechanisms in strong non-equilibrium conditions. The need for accurate reduced-order models arises from compression of large ab initio quantum chemistry databases for their use in fluid codes. The method presented in this paper builds on existing physics-based strategies and proposes a new approach based on the combination of a simple coarse grain model with Principal Component Analysis (PCA). The internal energy levels of the chemical species are regrouped in distinct energy groups with a uniform lumping technique. Following the philosophy of machine learning, PCA is applied on the training data provided by the coarse grain model to find an optimally reduced representation of the full kinetic mechanism. Compared to recently published complex lumping strategies, no expert judgment is required before the application of PCA. In this work, we will demonstrate the benefits of the combined approach, stressing its simplicity, reliability, and accuracy. The technique is demonstrated by reducing the complex quantum N2(g+1Σ) -N(S4u ) database for studying molecular dissociation and excitation in strong non-equilibrium. Starting from detailed kinetics, an accurate reduced model is developed and used to study non-equilibrium properties of the N2(g+1Σ) -N(S4u ) system in shock relaxation simulations.

  20. Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems

    Directory of Open Access Journals (Sweden)

    Dongwoo Jang

    2018-03-01

    Full Text Available Leaks in a water distribution network (WDS constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA and artificial neural network (ANN were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

  1. Improved algorithms for the classification of rough rice using a bionic electronic nose based on PCA and the Wilks distribution.

    Science.gov (United States)

    Xu, Sai; Zhou, Zhiyan; Lu, Huazhong; Luo, Xiwen; Lan, Yubin

    2014-03-19

    Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the classification performance of regular PCA based on the existing Wilks Λ-statistic (i.e., combined PCA with the Wilks distribution). The improved algorithms, which combine regular PCA with the Wilks Λ-statistic, were developed after analysing the functionality and defects of PCA. Verification tests were conducted using a PEN3 electronic nose. The collected samples consisted of the volatiles of six varieties of rough rice (Zhongxiang1, Xiangwan13, Yaopingxiang, WufengyouT025, Pin 36, and Youyou122), grown in same area and season. The first two principal components used as analysis vectors cannot perform the rough rice varieties classification task based on a regular PCA. Using the improved algorithms, which combine the regular PCA with the Wilks Λ-statistic, many different principal components were selected as analysis vectors. The set of data points of the Mahalanobis distance between each of the varieties of rough rice was selected to estimate the performance of the classification. The result illustrates that the rough rice varieties classification task is achieved well using the improved algorithm. A Probabilistic Neural Networks (PNN) was also established to test the effectiveness of the improved algorithms. The first two principal components (namely PC1 and PC2) and the first and fifth principal component (namely PC1 and PC5) were selected as the inputs of PNN for the classification of the six rough rice varieties. The results indicate that the classification accuracy based on the improved algorithm was improved by 6.67% compared to the results of the regular method. These results prove the effectiveness of using the Wilks Λ-statistic to improve the classification accuracy of the regular PCA approach. The results

  2. Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

    Directory of Open Access Journals (Sweden)

    Oliveira-Esquerre K.P.

    2002-01-01

    Full Text Available This work presents a way to predict the biochemical oxygen demand (BOD of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.

  3. An analytical approach based on ESI-MS, LC-MS and PCA for the quali-quantitative analysis of cycloartane derivatives in Astragalus spp.

    Science.gov (United States)

    Napolitano, Assunta; Akay, Seref; Mari, Angela; Bedir, Erdal; Pizza, Cosimo; Piacente, Sonia

    2013-11-01

    Astragalus species are widely used as health foods and dietary supplements, as well as drugs in traditional medicine. To rapidly evaluate metabolite similarities and differences among the EtOH extracts of the roots of eight commercial Astragalus spp., an approach based on direct analyses by ESI-MS followed by PCA of ESI-MS data, was carried out. Successively, quali-quantitative analyses of cycloartane derivatives in the eight Astragalus spp. by LC-ESI-MS(n) and PCA of LC-ESI-MS data were performed. This approach allowed to promptly highlighting metabolite similarities and differences among the various Astragalus spp. PCA results from LC-ESI-MS data of Astragalus samples were in reasonable agreement with both PCA results of ESI-MS data and quantitative results. This study affords an analytical method for the quali-quantitative determination of cycloartane derivatives in herbal preparations used as health and food supplements. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults.

    Science.gov (United States)

    Batis, Carolina; Mendez, Michelle A; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry

    2016-02-01

    We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose. We measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Adults (n 4316) from the China Health and Nutrition Survey. The adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood). Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes.

  5. Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

    Science.gov (United States)

    Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

    2012-11-13

    We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular

  6. Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach

    Directory of Open Access Journals (Sweden)

    Panazzolo Diogo G

    2012-11-01

    Full Text Available Abstract Background We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Methods Data from 189 female subjects (34.0±15.5 years, 30.5±7.1 kg/m2, who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA. PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI, waist circumference, systolic and diastolic blood pressure (BP, fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c, low-density lipoprotein cholesterol (LDL-c, triglycerides (TG, insulin, C-reactive protein (CRP, and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV at rest and peak after 1 min of arterial occlusion (RBCVmax, and the time taken to reach RBCVmax (TRBCVmax. Results A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCVmax varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCVmax, but in the opposite way. Principal component 3 was

  7. Component evaluation testing and analysis algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    Hart, Darren M.; Merchant, Bion John

    2011-10-01

    The Ground-Based Monitoring R&E Component Evaluation project performs testing on the hardware components that make up Seismic and Infrasound monitoring systems. The majority of the testing is focused on the Digital Waveform Recorder (DWR), Seismic Sensor, and Infrasound Sensor. In order to guarantee consistency, traceability, and visibility into the results of the testing process, it is necessary to document the test and analysis procedures that are in place. Other reports document the testing procedures that are in place (Kromer, 2007). This document serves to provide a comprehensive overview of the analysis and the algorithms that are applied to the Component Evaluation testing. A brief summary of each test is included to provide the context for the analysis that is to be performed.

  8. Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis

    Science.gov (United States)

    Richard Tran Mills; Jitendra Kumar; Forrest M. Hoffman; William W. Hargrove; Joseph P. Spruce; Steven P. Norman

    2013-01-01

    We investigated the use of principal components analysis (PCA) to visualize dominant patterns and identify anomalies in a multi-year land surface phenology data set (231 m × 231 m normalized difference vegetation index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)) used for detecting threats to forest health in the conterminous...

  9. Portable XRF and principal component analysis for bill characterization in forensic science

    International Nuclear Information System (INIS)

    Appoloni, C.R.; Melquiades, F.L.

    2014-01-01

    Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. - Highlights: • The paper is about a direct method for bills discrimination by EDXRF and principal component analysis. • The bills are analyzed directly, without sample preparation and non destructively. • The results demonstrates that the methodology is feasible and could be applied in forensic science for identification of origin and false banknotes. • The novelty is that portable EDXRF is very fast and efficient for bills characterization

  10. Principal component analysis for verifying 1H NMR spectral assignments. The case of 3-aryl (1,2,4)-oxadiazole-5-carbohydrazide benzylidene

    International Nuclear Information System (INIS)

    Silva, Joao Bosco P. da; Malvestiti, Ivani; Hallwass, Fernando; Ramos, Mozart N.; Leite, Lucia F.C. da Costa; Barreiro, Eliezer J.

    2005-01-01

    The 1 H NMR data set of a series of 3-aryl (1,2,4)-oxadiazole-5-carbohydrazide benzylidene derivatives synthesized in our group was analyzed using the chemometric technique of principal component analysis (PCA). Using the original 1H NMR data PCA allowed identifying some misassignments of the proton aromatic chemical shifts. As a consequence of this multivariate analysis, nuclear Overhauser difference experiments were performed to investigate the ambiguity of other assignments of the ortho and meta aromatic hydrogens for the compound with the bromine substituent. The effect of the 1,2,4-oxadiazole group as an electron acceptor, mainly for the hydrogens 12,13, has been highlighted. (author)

  11. Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

    Science.gov (United States)

    Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

    2016-02-01

    Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability.

  12. Independent principal component analysis for simulation of soil water content and bulk density in a Canadian Watershed

    Directory of Open Access Journals (Sweden)

    Alaba Boluwade

    2016-09-01

    Full Text Available Accurate characterization of soil properties such as soil water content (SWC and bulk density (BD is vital for hydrologic processes and thus, it is importance to estimate θ (water content and ρ (soil bulk density among other soil surface parameters involved in water retention and infiltration, runoff generation and water erosion, etc. The spatial estimation of these soil properties are important in guiding agricultural management decisions. These soil properties vary both in space and time and are correlated. Therefore, it is important to find an efficient and robust technique to simulate spatially correlated variables. Methods such as principal component analysis (PCA and independent component analysis (ICA can be used for the joint simulations of spatially correlated variables, but they are not without their flaws. This study applied a variant of PCA called independent principal component analysis (IPCA that combines the strengths of both PCA and ICA for spatial simulation of SWC and BD using the soil data set from an 11 km2 Castor watershed in southern Quebec, Canada. Diagnostic checks using the histograms and cumulative distribution function (cdf both raw and back transformed simulations show good agreement. Therefore, the results from this study has potential in characterization of water content variability and bulk density variation for precision agriculture.

  13. Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection

    DEFF Research Database (Denmark)

    Karlsen, Brian; Larsen, Jan; Sørensen, Helge Bjarup Dissing

    2001-01-01

    This paper presents statistical signal processing approaches for clutter reduction in stepped-frequency ground penetrating radar (SF-GPR) data. In particular, we suggest clutter/signal separation techniques based on principal and independent component analysis (PCA/ICA). The approaches...

  14. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures

    Directory of Open Access Journals (Sweden)

    Anders E. Berglund

    2017-01-01

    Full Text Available Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed to measure a single biological effect, then this signal should be sufficiently strong and distinct compared to other signals within the signature. Transferability: the derived PCA gene signature score should describe the same biology in the target dataset as it does in the training dataset. Conclusions. The proposed validation procedure ensures that PCA-based gene signatures perform as expected when applied to datasets other than those that the signatures were trained upon. Complex signatures, describing multiple independent biological components, are also easily identified.

  15. PCA-based bootstrap confidence interval tests for gene-disease association involving multiple SNPs

    Directory of Open Access Journals (Sweden)

    Xue Fuzhong

    2010-01-01

    Full Text Available Abstract Background Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s which usually involves multiple single-nucleotide polymorphisms (SNPs available. However, SNP-wise association tests raise concerns over multiple testing. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE and potentially large number degrees of freedom can harm its statistical power and robustness. Approaches based on principal component analysis (PCA are preferable in this regard but their performance varies with methods of extracting principal components (PCs. Results PCA-based bootstrap confidence interval test (PCA-BCIT, which directly uses the PC scores to assess gene-disease association, was developed and evaluated for three ways of extracting PCs, i.e., cases only(CAES, controls only(COES and cases and controls combined(CES. Extraction of PCs with COES is preferred to that with CAES and CES. Performance of the test was examined via simulations as well as analyses on data of rheumatoid arthritis and heroin addiction, which maintains nominal level under null hypothesis and showed comparable performance with permutation test. Conclusions PCA-BCIT is a valid and powerful method for assessing gene-disease association involving multiple SNPs.

  16. Short-term PV/T module temperature prediction based on PCA-RBF neural network

    Science.gov (United States)

    Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

    2018-02-01

    Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

  17. Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis

    Directory of Open Access Journals (Sweden)

    Matrone Giulia C

    2012-06-01

    Full Text Available Abstract Background In spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA, specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs of the prosthesis. Methods A clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days. Results Subjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric

  18. Experimental and principal component analysis of waste ...

    African Journals Online (AJOL)

    The present study is aimed at determining through principal component analysis the most important variables affecting bacterial degradation in ponds. Data were collected from literature. In addition, samples were also collected from the waste stabilization ponds at the University of Nigeria, Nsukka and analyzed to ...

  19. Independent component analysis for understanding multimedia content

    DEFF Research Database (Denmark)

    Kolenda, Thomas; Hansen, Lars Kai; Larsen, Jan

    2002-01-01

    Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval...

  20. A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements.

    Science.gov (United States)

    Nejadgholi, Isar; Bolic, Miodrag

    2015-08-01

    Due to safety and low cost of bioimpedance spectroscopy (BIS), classification of BIS can be potentially a preferred way of detecting changes in living tissues. However, for longitudinal datasets linear classifiers fail to classify conventional Cole parameters extracted from BIS measurements because of their high variability. In some applications, linear classification based on Principal Component Analysis (PCA) has shown more accurate results. Yet, these methods have not been established for BIS classification, since PCA features have neither been investigated in combination with other classifiers nor have been compared to conventional Cole features in benchmark classification tasks. In this work, PCA and Cole features are compared in three synthesized benchmark classification tasks which are expected to be detected by BIS. These three tasks are classification of before and after geometry change, relative composition change and blood perfusion in a cylindrical organ. Our results show that in all tasks the features extracted by PCA are more discriminant than Cole parameters. Moreover, a pilot study was done on a longitudinal arm BIS dataset including eight subjects and three arm positions. The goal of the study was to compare different methods in arm position classification which includes all three synthesized changes mentioned above. Our comparative study on various classification methods shows that the best classification accuracy is obtained when PCA features are classified by a K-Nearest Neighbors (KNN) classifier. The results of this work suggest that PCA+KNN is a promising method to be considered for classification of BIS datasets that deal with subject and time variability. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Wavelet Compressed PCA Models for Real-Time Image Registration in Augmented Reality Applications

    OpenAIRE

    Christopher Cooper; Kent Wise; John Cooper; Makarand Deo

    2015-01-01

    The use of augmented reality (AR) has shown great promise in enhancing medical training and diagnostics via interactive simulations. This paper presents a novel method to perform accurate and inexpensive image registration (IR) utilizing a pre-constructed database of reference objects in conjunction with a principal component analysis (PCA) model. In addition, a wavelet compression algorithm is utilized to enhance the speed of the registration process. The proposed method is used to perform r...

  2. The application of principal component analysis to quantify technique in sports.

    Science.gov (United States)

    Federolf, P; Reid, R; Gilgien, M; Haugen, P; Smith, G

    2014-06-01

    Analyzing an athlete's "technique," sport scientists often focus on preselected variables that quantify important aspects of movement. In contrast, coaches and practitioners typically describe movements in terms of basic postures and movement components using subjective and qualitative features. A challenge for sport scientists is finding an appropriate quantitative methodology that incorporates the holistic perspective of human observers. Using alpine ski racing as an example, this study explores principal component analysis (PCA) as a mathematical method to decompose a complex movement pattern into its main movement components. Ski racing movements were recorded by determining the three-dimensional coordinates of 26 points on each skier which were subsequently interpreted as a 78-dimensional posture vector at each time point. PCA was then used to determine the mean posture and principal movements (PMk ) carried out by the athletes. The first four PMk contained 95.5 ± 0.5% of the variance in the posture vectors which quantified changes in body inclination, vertical or fore-aft movement of the trunk, and distance between skis. In summary, calculating PMk offered a data-driven, quantitative, and objective method of analyzing human movement that is similar to how human observers such as coaches or ski instructors would describe the movement. © 2012 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  3. Principal component analysis to assess the composition and fate of impurities in a large river-embedded reservoir: Qingcaosha Reservoir.

    Science.gov (United States)

    Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun

    2013-08-01

    Qingcaosha Reservoir (QR) is the largest river-embedded reservoir in east China, which receives its source water from the Yangtze River (YR). The temporal and spatial variations in dissolved organic matter (DOM), chromophoric DOM (CDOM), nitrogen, phosphorus and phytoplankton biomass were investigated from June to September in 2012 and were integrated by principal component analysis (PCA). Three PCA factors were identified: (1) phytoplankton related factor 1, (2) total DOM related factor 2, and (3) eutrophication related factor 3. Factor 1 was a lake-type parameter which correlated with chlorophyll-a and protein-like CDOM (r = 0.793 and r = 0.831, respectively). Factor 2 was a river-type parameter which correlated with total DOC and humic-like CDOM (r = 0.668 and r = 0.726, respectively). Factor 3 correlated with total nitrogen and phosphorus (r = 0.864 and r = 0.621, respectively). The low flow speed, self-sedimentation and nutrient accumulation in QR resulted in increases in PCA factor 1 scores (phytoplankton biomass and derived CDOM) in the spatial scale, indicating a change of river-type water (YR) to lake-type water (QR). In summer, the water temperature variation induced a growth-bloom-decay process of phytoplankton combined with the increase of PCA factor 2 (humic-like CDOM) in the QR, which was absent in the YR.

  4. Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis.

    Science.gov (United States)

    Plazas-Nossa, Leonardo; Hofer, Thomas; Gruber, Günter; Torres, Andres

    2017-02-01

    This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.

  5. SU-F-R-41: Regularized PCA Can Model Treatment-Related Changes in Head and Neck Patients Using Daily CBCTs

    International Nuclear Information System (INIS)

    Chetvertkov, M; Siddiqui, F; Chetty, I; Kumarasiri, A; Liu, C; Gordon, J

    2016-01-01

    Purpose: To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART). Methods: Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction-to-fraction changes were superimposed on the DVFs. Standard non-regularized and regularized patient-specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course. Conclusion: The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are

  6. SU-F-R-41: Regularized PCA Can Model Treatment-Related Changes in Head and Neck Patients Using Daily CBCTs

    Energy Technology Data Exchange (ETDEWEB)

    Chetvertkov, M [Wayne State University, Detroit, MI (United States); Henry Ford Health System, Detroit, MI (United States); Siddiqui, F; Chetty, I; Kumarasiri, A; Liu, C; Gordon, J [Henry Ford Health System, Detroit, MI (United States)

    2016-06-15

    Purpose: To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART). Methods: Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction-to-fraction changes were superimposed on the DVFs. Standard non-regularized and regularized patient-specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course. Conclusion: The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are

  7. Registration of dynamic dopamine D{sub 2}receptor images using principal component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Acton, P.D.; Ell, P.J. [Institute of Nuclear Medicine, University College London Medical School, London (United Kingdom); Pilowsky, L.S.; Brammer, M.J. [Institute of Psychiatry, De Crespigny Park, London (United Kingdom); Suckling, J. [Clinical Age Research Unit, Kings College School of Medicine and Dentistry, London (United Kingdom)

    1997-11-01

    This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D{sub 2}receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D {sub 2}receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P <0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly {sup 123}I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the {chi}{sup 2}of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.) With 4 figs., 2 tabs., 26 refs.

  8. Decision tree and PCA-based fault diagnosis of rotating machinery

    Science.gov (United States)

    Sun, Weixiang; Chen, Jin; Li, Jiaqing

    2007-04-01

    After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

  9. Global Clustering Quality Coefficient Assessing the Efficiency of PCA Class Assignment

    Directory of Open Access Journals (Sweden)

    Mirela Praisler

    2014-01-01

    Full Text Available An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their dispersion. We are proposing a set of indicators inspired from analytical geometry that may be used for an objective quantitative assessment of the data clustering quality as well as a global clustering quality coefficient (GCQC that is a measure of the overall predictive power of the PCA models. The use of these indicators for evaluating the efficiency of the PCA class assignment is illustrated by a comparative study performed for the identification of the preprocessing function that is generating the most efficient PCA system screening for amphetamines based on their GC-FTIR spectra. The GCQC ranking of the tested feature weights is explained based on estimated density distributions and validated by using quadratic discriminant analysis (QDA.

  10. Circle of Willis Variants: Fetal PCA

    OpenAIRE

    Amir Shaban; Karen C. Albright; Amelia K. Boehme; Sheryl Martin-Schild

    2013-01-01

    We sought to determine the prevalence of fetal posterior cerebral artery (fPCA) and if fPCA was associated with specific stroke etiology and vessel territory affected. This paper is a retrospective review of prospectively identified patients with acute ischemic stroke from July 2008 to December 2010. We defined complete fPCA as absence of a P1 segment linking the basilar with the PCA and partial fPCA as small segment linking the basilar with the PCA. Patients without intracranial vascular ima...

  11. BUSINESS PROCESS MANAGEMENT SYSTEMS TECHNOLOGY COMPONENTS ANALYSIS

    Directory of Open Access Journals (Sweden)

    Andrea Giovanni Spelta

    2007-05-01

    Full Text Available The information technology that supports the implementation of the business process management appproach is called Business Process Management System (BPMS. The main components of the BPMS solution framework are process definition repository, process instances repository, transaction manager, conectors framework, process engine and middleware. In this paper we define and characterize the role and importance of the components of BPMS's framework. The research method adopted was the case study, through the analysis of the implementation of the BPMS solution in an insurance company called Chubb do Brasil. In the case study, the process "Manage Coinsured Events"" is described and characterized, as well as the components of the BPMS solution adopted and implemented by Chubb do Brasil for managing this process.

  12. Clustering of metabolic and cardiovascular risk factors in the polycystic ovary syndrome: a principal component analysis.

    Science.gov (United States)

    Stuckey, Bronwyn G A; Opie, Nicole; Cussons, Andrea J; Watts, Gerald F; Burke, Valerie

    2014-08-01

    Polycystic ovary syndrome (PCOS) is a prevalent condition with heterogeneity of clinical features and cardiovascular risk factors that implies multiple aetiological factors and possible outcomes. To reduce a set of correlated variables to a smaller number of uncorrelated and interpretable factors that may delineate subgroups within PCOS or suggest pathogenetic mechanisms. We used principal component analysis (PCA) to examine the endocrine and cardiometabolic variables associated with PCOS defined by the National Institutes of Health (NIH) criteria. Data were retrieved from the database of a single clinical endocrinologist. We included women with PCOS (N = 378) who were not taking the oral contraceptive pill or other sex hormones, lipid lowering medication, metformin or other medication that could influence the variables of interest. PCA was performed retaining those factors with eigenvalues of at least 1.0. Varimax rotation was used to produce interpretable factors. We identified three principal components. In component 1, the dominant variables were homeostatic model assessment (HOMA) index, body mass index (BMI), high density lipoprotein (HDL) cholesterol and sex hormone binding globulin (SHBG); in component 2, systolic blood pressure, low density lipoprotein (LDL) cholesterol and triglycerides; in component 3, total testosterone and LH/FSH ratio. These components explained 37%, 13% and 11% of the variance in the PCOS cohort respectively. Multiple correlated variables from patients with PCOS can be reduced to three uncorrelated components characterised by insulin resistance, dyslipidaemia/hypertension or hyperandrogenaemia. Clustering of risk factors is consistent with different pathogenetic pathways within PCOS and/or differing cardiometabolic outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Sparse PCA with Oracle Property.

    Science.gov (United States)

    Gu, Quanquan; Wang, Zhaoran; Liu, Han

    In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

  14. Fault detection of flywheel system based on clustering and principal component analysis

    Directory of Open Access Journals (Sweden)

    Wang Rixin

    2015-12-01

    Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  15. Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer

    Science.gov (United States)

    Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Wang, Deli; Song, Youtao; Zhang, Su

    2016-03-01

    This paper attempts to investigate the feasibility of using Raman spectroscopy for the diagnosis of colon cancer. Serum taken from 75 healthy volunteers, 65 colon cancer patients and 60 post-operation colon cancer patients was measured in this experiment. In the Raman spectra of all three groups, the Raman peaks at 750, 1083, 1165, 1321, 1629 and 1779 cm-1 assigned to nucleic acids, amino acids and chromophores were consistently observed. All of these six Raman peaks were observed to have statistically significant differences between groups. For quantitative analysis, the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbour analysis (KNN) were utilized to develop diagnostic algorithms for classification. In PCA, several peaks in the principal component (PC) loadings spectra were identified as the major contributors to the PC scores. Some of the peaks in the PC loadings spectra were also reported as characteristic peaks for colon tissues, which implies correlation between peaks in PC loadings spectra and those in the original Raman spectra. KNN was also performed on the obtained PCs, and a diagnostic accuracy of 91.0% and a specificity of 92.6% were achieved.

  16. Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer

    International Nuclear Information System (INIS)

    Li, Xiaozhou; Yang, Tianyue; Wang, Deli; Li, Siqi; Song, Youtao; Zhang, Su

    2016-01-01

    This paper attempts to investigate the feasibility of using Raman spectroscopy for the diagnosis of colon cancer. Serum taken from 75 healthy volunteers, 65 colon cancer patients and 60 post-operation colon cancer patients was measured in this experiment. In the Raman spectra of all three groups, the Raman peaks at 750, 1083, 1165, 1321, 1629 and 1779 cm −1 assigned to nucleic acids, amino acids and chromophores were consistently observed. All of these six Raman peaks were observed to have statistically significant differences between groups. For quantitative analysis, the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbour analysis (KNN) were utilized to develop diagnostic algorithms for classification. In PCA, several peaks in the principal component (PC) loadings spectra were identified as the major contributors to the PC scores. Some of the peaks in the PC loadings spectra were also reported as characteristic peaks for colon tissues, which implies correlation between peaks in PC loadings spectra and those in the original Raman spectra. KNN was also performed on the obtained PCs, and a diagnostic accuracy of 91.0% and a specificity of 92.6% were achieved. (paper)

  17. Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Benoit Parmentier

    2014-12-01

    Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

  18. Thermogravimetry/mass spectrometry study of woody residues and an herbaceous biomass crop using PCA techniques

    Energy Technology Data Exchange (ETDEWEB)

    Gomez, C.J.; Velo, E.; Puigjaner, L. [Department of Chemical Engineering, ETSEIB, Universitat Politecnica de Catalunya, Avinguda Diagonal 647, G2, E-08028 Barcelona (Spain); Meszaros, E.; Jakab, E. [Institute of Materials and Environmental Chemistry, Chemical Research Center, Hungarian Academy of Sciences, P.O. Box 17, Budapest 1525 (Hungary)

    2007-10-15

    The devolatilization behaviour of pine and beech wood from carpentry residuals and an herbaceous product from an energy plantation (artichoke thistle) was investigated by thermogravimetry/mass spectrometry (TG/MS). The effect of three pre-treatments, hot-water washing, ethanol extraction and their combination, was also studied. Principal component analysis (PCA) was employed to help in the evaluation of the large data set of results. The characteristics of the thermal decomposition of the herbaceous crop are considerably different from that of the woody biomass samples. The evolution profiles of some characteristic pyrolysis products revealed that the thermal behaviour of wood and thistle is still considerably different after the elimination of some of the inorganic ions and extractive compounds, although the macromolecular components of the samples decompose at similar temperatures. With the help of the PCA calculations, the effect of the different pre-treatments on the production of the main pyrolysis products was evidenced. (author)

  19. Characterization of deep aquifer dynamics using principal component analysis of sequential multilevel data

    Directory of Open Access Journals (Sweden)

    D. Kurtzman

    2012-03-01

    Full Text Available Two sequential multilevel profiles were obtained in an observation well opened to a 130-m thick, unconfined, contaminated aquifer in Tel Aviv, Israel. While the general profile characteristics of major ions, trace elements, and volatile organic compounds were maintained in the two sampling campaigns conducted 295 days apart, the vertical locations of high concentration gradients were shifted between the two profiles. Principal component analysis (PCA of the chemical variables resulted in a first principal component which was responsible for ∼60% of the variability, and was highly correlated with depth. PCA revealed three distinct depth-dependent water bodies in both multilevel profiles, which were found to have shifted vertically between the sampling events. This shift cut across a clayey bed which separated the top and intermediate water bodies in the first profile, and was located entirely within the intermediate water body in the second profile. Continuous electrical conductivity monitoring in a packed-off section of the observation well revealed an event in which a distinct water body flowed through the monitored section (v ∼ 150 m yr−1. It was concluded that the observed changes in the profiles result from dominantly lateral flow of water bodies in the aquifer rather than vertical flow. The significance of this study is twofold: (a it demonstrates the utility of sequential multilevel observations from deep wells and the efficacy of PCA for evaluating the data; (b the fact that distinct water bodies of 10 to 100 m vertical and horizontal dimensions flow under contaminated sites, which has implications for monitoring and remediation.

  20. Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci

    2012-07-01

    Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

  1. Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

    Science.gov (United States)

    Hearty, Aine P; Gibney, Michael J

    2009-02-01

    The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18-64 years. Cluster analysis was performed using the k-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: 'Traditional Irish'; 'Continental'; 'Unhealthy foods'; 'Light-meal foods & low-fat milk'; 'Healthy foods'; 'Wholemeal bread & desserts'. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: 'Unhealthy foods & high alcohol'; 'Traditional Irish'; 'Healthy foods'; 'Sweet convenience foods & low alcohol'. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

  2. Masked volume wise principal component analysis of small adrenocortical tumours in dynamic [11C]-metomidate positron emission tomography

    International Nuclear Information System (INIS)

    Razifar, Pasha; Hennings, Joakim; Monazzam, Azita; Hellman, Per; Långström, Bengt; Sundin, Anders

    2009-01-01

    In previous clinical Positron Emission Tomography (PET) studies novel approaches for application of Principal Component Analysis (PCA) on dynamic PET images such as Masked Volume Wise PCA (MVW-PCA) have been introduced. MVW-PCA was shown to be a feasible multivariate analysis technique, which, without modeling assumptions, could extract and separate organs and tissues with different kinetic behaviors into different principal components (MVW-PCs) and improve the image quality. In this study, MVW-PCA was applied to 14 dynamic 11C-metomidate-PET (MTO-PET) examinations of 7 patients with small adrenocortical tumours. MTO-PET was performed before and 3 days after starting per oral cortisone treatment. The whole dataset, reconstructed by filtered back projection (FBP) 0–45 minutes after the tracer injection, was used to study the tracer pharmacokinetics. Early, intermediate and late pharmacokinetic phases could be isolated in this manner. The MVW-PC1 images correlated well to the conventionally summed image data (15–45 minutes) but the image noise in the former was considerably lower. PET measurements performed by defining 'hot spot' regions of interest (ROIs) comprising 4 contiguous pixels with the highest radioactivity concentration showed a trend towards higher SUVs when the ROIs were outlined in the MVW-PC1 component than in the summed images. Time activity curves derived from '50% cut-off' ROIs based on an isocontour function whereby the pixels with SUVs between 50 to 100% of the highest radioactivity concentration were delineated, showed a significant decrease of the SUVs in normal adrenal glands and in adrenocortical adenomas after cortisone treatment. In addition to the clear decrease in image noise and the improved contrast between different structures with MVW-PCA, the results indicate that the definition of ROIs may be more accurate and precise in MVW-PC1 images than in conventional summed images. This might improve the precision of PET

  3. ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison.

    NARCIS (Netherlands)

    Zwanenburg, G.; Hoefsloot, H.C.J.; Westerhuis, J.A.; Jansen, J.J.; Smilde, A.K.

    2011-01-01

    ANOVA-simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements.

  4. Improvement of Binary Analysis Components in Automated Malware Analysis Framework

    Science.gov (United States)

    2017-02-21

    AFRL-AFOSR-JP-TR-2017-0018 Improvement of Binary Analysis Components in Automated Malware Analysis Framework Keiji Takeda KEIO UNIVERSITY Final...TYPE Final 3. DATES COVERED (From - To) 26 May 2015 to 25 Nov 2016 4. TITLE AND SUBTITLE Improvement of Binary Analysis Components in Automated Malware ...analyze malicious software ( malware ) with minimum human interaction. The system autonomously analyze malware samples by analyzing malware binary program

  5. Modeling the variability of solar radiation data among weather stations by means of principal components analysis

    International Nuclear Information System (INIS)

    Zarzo, Manuel; Marti, Pau

    2011-01-01

    Research highlights: →Principal components analysis was applied to R s data recorded at 30 stations. → Four principal components explain 97% of the data variability. → The latent variables can be fitted according to latitude, longitude and altitude. → The PCA approach is more effective for gap infilling than conventional approaches. → The proposed method allows daily R s estimations at locations in the area of study. - Abstract: Measurements of global terrestrial solar radiation (R s ) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches

  6. Elevated YKL40 is associated with advanced prostate cancer (PCa) and positively regulates invasion and migration of PCa cells.

    Science.gov (United States)

    Jeet, Varinder; Tevz, Gregor; Lehman, Melanie; Hollier, Brett; Nelson, Colleen

    2014-10-01

    Chitinase 3-like 1 (CHI3L1 or YKL40) is a secreted glycoprotein highly expressed in tumours from patients with advanced stage cancers, including prostate cancer (PCa). The exact function of YKL40 is poorly understood, but it has been shown to play an important role in promoting tumour angiogenesis and metastasis. The therapeutic value and biological function of YKL40 are unknown in PCa. The objective of this study was to examine the expression and function of YKL40 in PCa. Gene expression analysis demonstrated that YKL40 was highly expressed in metastatic PCa cells when compared with less invasive and normal prostate epithelial cell lines. In addition, the expression was primarily limited to androgen receptor-positive cell lines. Evaluation of YKL40 tissue expression in PCa patients showed a progressive increase in patients with aggressive disease when compared with those with less aggressive cancers and normal controls. Treatment of LNCaP and C4-2B cells with androgens increased YKL40 expression, whereas treatment with an anti-androgen agent decreased the gene expression of YKL40 in androgen-sensitive LNCaP cells. Furthermore, knockdown of YKL40 significantly decreased invasion and migration of PCa cells, whereas overexpression rendered them more invasive and migratory, which was commensurate with an enhancement in the anchorage-independent growth of cells. To our knowledge, this study characterises the role of YKL40 for the first time in PCa. Together, these results suggest that YKL40 plays an important role in PCa progression and thus inhibition of YKL40 may be a potential therapeutic strategy for the treatment of PCa. © 2014 The authors.

  7. Tensile properties of unirradiated path A PCA

    International Nuclear Information System (INIS)

    Braski, D.N.; Maziasz, P.J.

    1983-01-01

    The tensile properties of PCA in the Al (solution annealed), A3 (25%-cold worked), and B2 (aged, cold worked, and reaged) conditions were determined from room temperature to 600 0 C. The tensile behavior of PCA-A1 and -A3 was generally similar to that of titanium-modified type 316 stainless steel with similar microstructures. The PCA-B2 was weaker than PCA-A3, especially above 500 0 C, but demonstrated slightly better ducility

  8. Fault tree analysis with multistate components

    International Nuclear Information System (INIS)

    Caldarola, L.

    1979-02-01

    A general analytical theory has been developed which allows one to calculate the occurence probability of the top event of a fault tree with multistate (more than states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of Boolean algebra is required. This is called 'Boolean algebra with restrictions on varibales' and its basic rules are the same as those of the traditional Boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. Important features of the method are the identification of the complete base and of the smallest irredundant base of a Boolean function which does not necessarily need to be coherent. It is shown that the identification of the complete base of a Boolean function requires the application of some algorithms which are not used in today's computer programmes for fault tree analysis. The problem of statistical dependence among primary components is discussed. The paper includes a small demonstrative example to illustrate the method. The example includes also statistical dependent components. (orig.) [de

  9. PCA-MLP SVM distinction of salivary Raman spectra of dengue fever infection.

    Science.gov (United States)

    Radzol, A R M; Lee, Khuan Y; Mansor, W; Wong, P S; Looi, I

    2017-07-01

    Dengue fever (DF) is a disease of major concern caused by flavivirus infection. Delayed diagnosis leads to severe stages, which could be deadly. Of recent, non-structural protein (NS1) has been acknowledged as a biomarker, alternative to immunoglobulins for early detection of dengue in blood. Further, non-invasive detection of NS1 in saliva makes the approach more appealing. However, since its concentration in saliva is less than blood, a sensitive and specific technique, Surface Enhanced Raman Spectroscopy (SERS), is employed. Our work here intends to define an optimal PCA-SVM (Principal Component Analysis-Support Vector Machine) with Multilayer Layer Perceptron (MLP) kernel model to distinct between positive and negative NS1 infected samples from salivary SERS spectra, which, to the best of our knowledge, has never been explored. Salivary samples of DF positive and negative subjects were collected, pre-processed and analyzed. PCA and SVM classifier were then used to differentiate the SERS analyzed spectra. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Its performance is also compared to our previous works on simulated NS1 salivary samples. It is found that the best PCA-SVM (MLP) model can be defined by 95 PCs from CPV criterion with P1 and P2 values of 0.01 and -0.2 respectively. A classification performance of [76.88%, 85.92%, 67.83%] is achieved.

  10. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  11. Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages

    DEFF Research Database (Denmark)

    Lukic, Ana S.; Wernick, Miles N.; Yang, Yongui

    2007-01-01

    this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (f......It has been previously observed that spatial independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper we seek to determine analytically the conditions under which...

  12. Multilevel sparse functional principal component analysis.

    Science.gov (United States)

    Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

    2014-01-29

    We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

  13. SU-E-I-58: Objective Models of Breast Shape Undergoing Mammography and Tomosynthesis Using Principal Component Analysis.

    Science.gov (United States)

    Feng, Ssj; Sechopoulos, I

    2012-06-01

    To develop an objective model of the shape of the compressed breast undergoing mammographic or tomosynthesis acquisition. Automated thresholding and edge detection was performed on 984 anonymized digital mammograms (492 craniocaudal (CC) view mammograms and 492 medial lateral oblique (MLO) view mammograms), to extract the edge of each breast. Principal Component Analysis (PCA) was performed on these edge vectors to identify a limited set of parameters and eigenvectors that. These parameters and eigenvectors comprise a model that can be used to describe the breast shapes present in acquired mammograms and to generate realistic models of breasts undergoing acquisition. Sample breast shapes were then generated from this model and evaluated. The mammograms in the database were previously acquired for a separate study and authorized for use in further research. The PCA successfully identified two principal components and their corresponding eigenvectors, forming the basis for the breast shape model. The simulated breast shapes generated from the model are reasonable approximations of clinically acquired mammograms. Using PCA, we have obtained models of the compressed breast undergoing mammographic or tomosynthesis acquisition based on objective analysis of a large image database. Up to now, the breast in the CC view has been approximated as a semi-circular tube, while there has been no objectively-obtained model for the MLO view breast shape. Such models can be used for various breast imaging research applications, such as x-ray scatter estimation and correction, dosimetry estimates, and computer-aided detection and diagnosis. © 2012 American Association of Physicists in Medicine.

  14. Finger crease pattern recognition using Legendre moments and principal component analysis

    Science.gov (United States)

    Luo, Rongfang; Lin, Tusheng

    2007-03-01

    The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

  15. Towards the generation of a parametric foot model using principal component analysis: A pilot study.

    Science.gov (United States)

    Scarton, Alessandra; Sawacha, Zimi; Cobelli, Claudio; Li, Xinshan

    2016-06-01

    There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) represents an appealing solution. In this study PCA has been applied to the feet of a small cohort of diabetic and healthy subjects, in order to evaluate the possibility of developing parametric foot models, and to use them to identify variations and similarities between the two populations. Both the skin and the first metatarsal bones have been examined. Besides the reduced sample of subjects considered in the analysis, results demonstrated that the method adopted herein constitutes a first step towards the realization of a parametric foot models for biomechanical analysis. Furthermore the study showed that the methodology can successfully describe features in the foot, and evaluate differences in the shape of healthy and diabetic subjects. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  16. Comparative Performance Of Using PCA With K-Means And Fuzzy C Means Clustering For Customer Segmentation

    Directory of Open Access Journals (Sweden)

    Fahmida Afrin

    2015-08-01

    Full Text Available Abstract Data mining is the process of analyzing data and discovering useful information. Sometimes it is called knowledge Discovery. Clustering refers to groups whereas data are grouped in such a way that the data in one cluster are similar data in different clusters are dissimilar. Many data mining technologies are developed for customer segmentation. PCA is working as a preprocessor of Fuzzy C means and K- means for reducing the high dimensional and noisy data. There are many clustering method apply on customer segmentation. In this paper the performance of Fuzzy C means and K-means after implementing Principal Component Analysis is analyzed. We analyze the performance on a standard dataset for these algorithms. The results indicate that PCA based fuzzy clustering produces better results than PCA based K-means and is a more stable method for customer segmentation.

  17. Independent Component Analysis in Multimedia Modeling

    DEFF Research Database (Denmark)

    Larsen, Jan

    2003-01-01

    largely refers to text, images/video, audio and combinations of such data. We review a number of applications within single and combined media with the hope that this might provide inspiration for further research in this area. Finally, we provide a detailed presentation of our own recent work on modeling......Modeling of multimedia and multimodal data becomes increasingly important with the digitalization of the world. The objective of this paper is to demonstrate the potential of independent component analysis and blind sources separation methods for modeling and understanding of multimedia data, which...

  18. Analysis of spiral components in 16 galaxies

    International Nuclear Information System (INIS)

    Considere, S.; Athanassoula, E.

    1988-01-01

    A Fourier analysis of the intensity distributions in the plane of 16 spiral galaxies of morphological types from 1 to 7 is performed. The galaxies processed are NGC 300,598,628,2403,2841,3031,3198,3344,5033,5055,5194,5247,6946,7096,7217, and 7331. The method, mathematically based upon a decomposition of a distribution into a superposition of individual logarithmic spiral components, is first used to determine for each galaxy the position angle PA and the inclination ω of the galaxy plane onto the sky plane. Our results, in good agreement with those issued from different usual methods in the literature, are discussed. The decomposition of the deprojected galaxies into individual spiral components reveals that the two-armed component is everywhere dominant. Our pitch angles are then compared to the previously published ones and their quality is checked by drawing each individual logarithmic spiral on the actual deprojected galaxy images. Finally, the surface intensities for angular periodicities of interest are calculated. A choice of a few of the most important ones is used to elaborate a composite image well representing the main spiral features observed in the deprojected galaxies

  19. Structural analysis of NPP components and structures

    International Nuclear Information System (INIS)

    Saarenheimo, A.; Keinaenen, H.; Talja, H.

    1998-01-01

    Capabilities for effective structural integrity assessment have been created and extended in several important cases. In the paper presented applications deal with pressurised thermal shock loading, PTS, and severe dynamic loading cases of containment, reinforced concrete structures and piping components. Hydrogen combustion within the containment is considered in some severe accident scenarios. Can a steel containment withstand the postulated hydrogen detonation loads and still maintain its integrity? This is the topic of Chapter 2. The following Chapter 3 deals with a reinforced concrete floor subjected to jet impingement caused by a postulated rupture of a near-by high-energy pipe and Chapter 4 deals with dynamic loading resistance of the pipe lines under postulated pressure transients due to water hammer. The reliability of the structural integrity analysing methods and capabilities which have been developed for application in NPP component assessment, shall be evaluated and verified. The resources available within the RATU2 programme alone cannot allow performing of the large scale experiments needed for that purpose. Thus, the verification of the PTS analysis capabilities has been conducted by participation in international co-operative programmes. Participation to the European Network for Evaluating Steel Components (NESC) is the topic of a parallel paper in this symposium. The results obtained in two other international programmes are summarised in Chapters 5 and 6 of this paper, where PTS tests with a model vessel and benchmark assessment of a RPV nozzle integrity are described. (author)

  20. International assessment of PCA codes

    International Nuclear Information System (INIS)

    Neymotin, L.; Lui, C.; Glynn, J.; Archarya, S.

    1993-11-01

    Over the past three years (1991-1993), an extensive international exercise for intercomparison of a group of six Probabilistic Consequence Assessment (PCA) codes was undertaken. The exercise was jointly sponsored by the Commission of European Communities (CEC) and OECD Nuclear Energy Agency. This exercise was a logical continuation of a similar effort undertaken by OECD/NEA/CSNI in 1979-1981. The PCA codes are currently used by different countries for predicting radiological health and economic consequences of severe accidents at nuclear power plants (and certain types of non-reactor nuclear facilities) resulting in releases of radioactive materials into the atmosphere. The codes participating in the exercise were: ARANO (Finland), CONDOR (UK), COSYMA (CEC), LENA (Sweden), MACCS (USA), and OSCAAR (Japan). In parallel with this inter-code comparison effort, two separate groups performed a similar set of calculations using two of the participating codes, MACCS and COSYMA. Results of the intercode and inter-MACCS comparisons are presented in this paper. The MACCS group included four participants: GREECE: Institute of Nuclear Technology and Radiation Protection, NCSR Demokritos; ITALY: ENEL, ENEA/DISP, and ENEA/NUC-RIN; SPAIN: Universidad Politecnica de Madrid (UPM) and Consejo de Seguridad Nuclear; USA: Brookhaven National Laboratory, US NRC and DOE

  1. Reformulating Component Identification as Document Analysis Problem

    NARCIS (Netherlands)

    Gross, H.G.; Lormans, M.; Zhou, J.

    2007-01-01

    One of the first steps of component procurement is the identification of required component features in large repositories of existing components. On the highest level of abstraction, component requirements as well as component descriptions are usually written in natural language. Therefore, we can

  2. Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom Cluster Research

    Directory of Open Access Journals (Sweden)

    Hee-Ju Kim, PhD, RN

    2008-03-01

    Conclusion: If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research, CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

  3. Composition-driven Cu-speciation and reducibility in Cu-CHA zeolite catalysts: a multivariate XAS/FTIR approach to complexity† †Electronic supplementary information (ESI) available: Sample description and synthesis details, experimental setup for in situ XAS and FTIR spectroscopy, details on the MCR-ALS method, details on DFT-assisted XANES simulations, details on the determination of N pure by PCA, MCR-ALS results for downsized and upsized component spaces, additional information to support the assignment of theoretical XANES curves, details on EXAFS analysis, details on IR spectral deconvolution. See DOI: 10.1039/c7sc02266b Click here for additional data file.

    Science.gov (United States)

    Martini, A.; Lomachenko, K. A.; Pankin, I. A.; Negri, C.; Berlier, G.; Beato, P.; Falsig, H.; Bordiga, S.; Lamberti, C.

    2017-01-01

    The small pore Cu-CHA zeolite is attracting increasing attention as a versatile platform to design novel single-site catalysts for deNOx applications and for the direct conversion of methane to methanol. Understanding at the atomic scale how the catalyst composition influences the Cu-species formed during thermal activation is a key step to unveil the relevant composition–activity relationships. Herein, we explore by in situ XAS the impact of Cu-CHA catalyst composition on temperature-dependent Cu-speciation and reducibility. Advanced multivariate analysis of in situ XANES in combination with DFT-assisted simulation of XANES spectra and multi-component EXAFS fits as well as in situ FTIR spectroscopy of adsorbed N2 allow us to obtain unprecedented quantitative structural information on the complex dynamics during the speciation of Cu-sites inside the framework of the CHA zeolite. PMID:29147509

  4. [Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

    Science.gov (United States)

    Wu, Gui-Fang; He, Yong

    2009-06-01

    One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

  5. Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of Space Geodetic Time Series

    Science.gov (United States)

    Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano

    2015-04-01

    A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources

  6. The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

    Science.gov (United States)

    Foch, Eric; Milner, Clare E

    2014-01-03

    Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

  7. Principle component analysis and linear discriminant analysis of multi-spectral autofluorescence imaging data for differentiating basal cell carcinoma and healthy skin

    Science.gov (United States)

    Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.

    2016-09-01

    In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

  8. SU-F-BRA-13: Knowledge-Based Treatment Planning for Prostate LDR Brachytherapy Based On Principle Component Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Roper, J; Bradshaw, B; Godette, K; Schreibmann, E [Winship Cancer Institute of Emory University, Atlanta, GA (United States); Chanyavanich, V [Rocky Mountain Cancer Centers, Denver, CO (United States)

    2015-06-15

    Purpose: To create a knowledge-based algorithm for prostate LDR brachytherapy treatment planning that standardizes plan quality using seed arrangements tailored to individual physician preferences while being fast enough for real-time planning. Methods: A dataset of 130 prior cases was compiled for a physician with an active prostate seed implant practice. Ten cases were randomly selected to test the algorithm. Contours from the 120 library cases were registered to a common reference frame. Contour variations were characterized on a point by point basis using principle component analysis (PCA). A test case was converted to PCA vectors using the same process and then compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best-matched library case. The seed arrangement was extracted from the best-matched case and used as a starting point for planning the test case. Computational time was recorded. Any subsequent modifications were recorded that required input from a treatment planner to achieve an acceptable plan. Results: The computational time required to register contours from a test case and evaluate PCA similarity across the library was approximately 10s. Five of the ten test cases did not require any seed additions, deletions, or moves to obtain an acceptable plan. The remaining five test cases required on average 4.2 seed modifications. The time to complete manual plan modifications was less than 30s in all cases. Conclusion: A knowledge-based treatment planning algorithm was developed for prostate LDR brachytherapy based on principle component analysis. Initial results suggest that this approach can be used to quickly create treatment plans that require few if any modifications by the treatment planner. In general, test case plans have seed arrangements which are very similar to prior cases, and thus are inherently tailored to physician preferences.

  9. Support vector machine and principal component analysis for microarray data classification

    Science.gov (United States)

    Astuti, Widi; Adiwijaya

    2018-03-01

    Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

  10. Using the Cluster Analysis and the Principal Component Analysis in Evaluating the Quality of a Destination

    Directory of Open Access Journals (Sweden)

    Ida Vajčnerová

    2016-01-01

    Full Text Available The objective of the paper is to explore possibilities of evaluating the quality of a tourist destination by means of the principal components analysis (PCA and the cluster analysis. In the paper both types of analysis are compared on the basis of the results they provide. The aim is to identify advantage and limits of both methods and provide methodological suggestion for their further use in the tourism research. The analyses is based on the primary data from the customers’ satisfaction survey with the key quality factors of a destination. As output of the two statistical methods is creation of groups or cluster of quality factors that are similar in terms of respondents’ evaluations, in order to facilitate the evaluation of the quality of tourist destinations. Results shows the possibility to use both tested methods. The paper is elaborated in the frame of wider research project aimed to develop a methodology for the quality evaluation of tourist destinations, especially in the context of customer satisfaction and loyalty.

  11. Development of a noise reduction program of a prompt gamma spectrum based on principal component analysis for an explosive detection

    International Nuclear Information System (INIS)

    Lee, Yun Hee; Im, Hee Jung; Song, Byung ChoI; Park, Yong Joon; Kim, Won Ho; Cho, Jung Hwan

    2005-01-01

    This work demonstrates a developed program to reduce noises of a prompt gamma-ray spectrum measured by irradiating neutrons into baggage. The noises refer to random variations mainly caused by electrical fluctuations and also by a measurement time. Especially, since the short measurement time yields such a noisy spectrum in which its special peak can not be observed, it is necessary to extract its characteristic signals from the spectrum to identify an explosive hidden in luggage. Principal component analysis(PCA) that is a multivariate statistical technique is closely related to singular value decomposition(SVD). The SVD-based PCA decreases the noise by reconstructing the spectrum after determining the number of principal components corresponding important signals based on the history data that sufficiently describe its population. In this study, we present a visualized program of the above procedure using the MATLAB 7.04 programming language. When our program is started, it requires an arbitrary measured spectrum to be reduced and history spectra as input files. If user selects the files with menu, our program automatically carries out the PCA procedure and provides its noise-reduced spectrum plot as well as the original spectrum plot into an output window. In addition, user can obtain signal-to-noise ratio of an interesting peak by defining the peak and noise ranges with menu

  12. Optimization of CNC end milling process parameters using PCA ...

    African Journals Online (AJOL)

    Optimization of CNC end milling process parameters using PCA-based Taguchi method. ... International Journal of Engineering, Science and Technology ... To meet the basic assumption of Taguchi method; in the present work, individual response correlations have been eliminated first by means of Principal Component ...

  13. PCA/HEXTE Observations of Coma and A2319

    Science.gov (United States)

    Rephaeli, Yoel

    1998-01-01

    The Coma cluster was observed in 1996 for 90 ks by the PCA and HEXTE instruments aboard the RXTE satellite, the first simultaneous, pointing measurement of Coma in the broad, 2-250 keV, energy band. The high sensitivity achieved during this long observation allows precise determination of the spectrum. Our analysis of the measurements clearly indicates that in addition to the main thermal emission from hot intracluster gas at kT=7.5 keV, a second spectral component is required to best-fit the data. If thermal, it can be described with a temperature of 4.7 keV contributing about 20% of the total flux. The additional spectral component can also be described by a power-law, possibly due to Compton scattering of relativistic electrons by the CMB. This interpretation is based on the diffuse radio synchrotron emission, which has a spectral index of 2.34, within the range allowed by fits to the RXTE spectral data. A Compton origin of the measured nonthermal component would imply that the volume-averaged magnetic field in the central region of Coma is B =0.2 micro-Gauss, a value deduced directly from the radio and X-ray measurements (and thus free of the usual assumption of energy equipartition). Barring the presence of unknown systematic errors in the RXTE source or background measurements, our spectral analysis yields considerable evidence for Compton X-ray emission in the Coma cluster.

  14. Investigation of inversion polymorphisms in the human genome using principal components analysis.

    Science.gov (United States)

    Ma, Jianzhong; Amos, Christopher I

    2012-01-01

    Despite the significant advances made over the last few years in mapping inversions with the advent of paired-end sequencing approaches, our understanding of the prevalence and spectrum of inversions in the human genome has lagged behind other types of structural variants, mainly due to the lack of a cost-efficient method applicable to large-scale samples. We propose a novel method based on principal components analysis (PCA) to characterize inversion polymorphisms using high-density SNP genotype data. Our method applies to non-recurrent inversions for which recombination between the inverted and non-inverted segments in inversion heterozygotes is suppressed due to the loss of unbalanced gametes. Inside such an inversion region, an effect similar to population substructure is thus created: two distinct "populations" of inversion homozygotes of different orientations and their 1:1 admixture, namely the inversion heterozygotes. This kind of substructure can be readily detected by performing PCA locally in the inversion regions. Using simulations, we demonstrated that the proposed method can be used to detect and genotype inversion polymorphisms using unphased genotype data. We applied our method to the phase III HapMap data and inferred the inversion genotypes of known inversion polymorphisms at 8p23.1 and 17q21.31. These inversion genotypes were validated by comparing with literature results and by checking Mendelian consistency using the family data whenever available. Based on the PCA-approach, we also performed a preliminary genome-wide scan for inversions using the HapMap data, which resulted in 2040 candidate inversions, 169 of which overlapped with previously reported inversions. Our method can be readily applied to the abundant SNP data, and is expected to play an important role in developing human genome maps of inversions and exploring associations between inversions and susceptibility of diseases.

  15. Behavior of the PCA3 gene in the urine of men with high grade prostatic intraepithelial neoplasia.

    Science.gov (United States)

    Morote, Juan; Rigau, Marina; Garcia, Marta; Mir, Carmen; Ballesteros, Carlos; Planas, Jacques; Raventós, Carles X; Placer, José; de Torres, Inés M; Reventós, Jaume; Doll, Andreas

    2010-12-01

    An ideal marker for the early detection of prostate cancer (PCa) should also differentiate between men with isolated high grade prostatic intraepithelial neoplasia (HGPIN) and those with PCa. Prostate Cancer Gene 3 (PCA3) is a highly specific PCa gene and its score, in relation to the PSA gene in post-prostate massage urine (PMU-PCA3), seems to be useful in ruling out PCa, especially after a negative prostate biopsy. Because PCA3 is also expressed in the HGPIN lesion, the aim of this study was to determine the efficacy of PMU-PCA3 scores for ruling out PCa in men with previous HGPIN. The PMU-PCA3 score was assessed by quantitative PCR (multiplex research assay) in 244 men subjected to prostate biopsy: 64 men with an isolated HGPIN (no cancer detected after two or more repeated biopsies), 83 men with PCa and 97 men with benign pathology findings (BP: no PCa, HGPIN or ASAP). The median PMU-PCA3 score was 1.56 in men with BP, 2.01 in men with HGPIN (p = 0.128) and 9.06 in men with PCa (p = 0.008). The AUC in the ROC analysis was 0.705 in the subset of men with BP and PCa, while it decreased to 0.629 when only men with isolated HGPIN and PCa were included in the analysis. Fixing the sensitivity of the PMU-PCA3 score at 90%, its specificity was 79% in men with BP and 69% in men with isolated HGPIN. The efficacy of the PMU-PCA3 score to rule out PCa in men with HGPIN is lower than in men with BP.

  16. Principal component analysis of air particulate data from the industrial area of islamabad, pakistan

    International Nuclear Information System (INIS)

    Waheed, S.; Siddique, N.; Daud, M.

    2008-01-01

    A Gent air sampler was used to collect 72 pairs of size fractionated coarse and fine (PM/sub 10/ and PM/sub 2.5/) particulate mass samples from the industrial zone (sector I-9) of Islamabad. These samples were analyzed for their elemental composition using Instrumental Neutron Activation Analysis (INAA). Principal component analysis (PCA), which can be used for source apportionment of quantified elemental data, was used to interpret the data. Graphical representations of loadings were used to explain the data through grouping of the elements from same source. The present work shows well defined elemental fingerprints of suspended soil and road dust, industry, motor vehicle exhaust and tyres, and coal and refuses combustions for the studied locality of Islamabad. (author)

  17. Principal Components Analysis on the spectral Bidirectional Reflectance Distribution Function of ceramic colour standards.

    Science.gov (United States)

    Ferrero, A; Campos, J; Rabal, A M; Pons, A; Hernanz, M L; Corróns, A

    2011-09-26

    The Bidirectional Reflectance Distribution Function (BRDF) is essential to characterize an object's reflectance properties. This function depends both on the various illumination-observation geometries as well as on the wavelength. As a result, the comprehensive interpretation of the data becomes rather complex. In this work we assess the use of the multivariable analysis technique of Principal Components Analysis (PCA) applied to the experimental BRDF data of a ceramic colour standard. It will be shown that the result may be linked to the various reflection processes occurring on the surface, assuming that the incoming spectral distribution is affected by each one of these processes in a specific manner. Moreover, this procedure facilitates the task of interpolating a series of BRDF measurements obtained for a particular sample. © 2011 Optical Society of America

  18. Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis.

    Science.gov (United States)

    Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L

    2014-01-01

    Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in Southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.

  19. A probability index for surface zonda wind occurrence at Mendoza city through vertical sounding principal components analysis

    Science.gov (United States)

    Otero, Federico; Norte, Federico; Araneo, Diego

    2018-01-01

    The aim of this work is to obtain an index for predicting the probability of occurrence of zonda event at surface level from sounding data at Mendoza city, Argentine. To accomplish this goal, surface zonda wind events were previously found with an objective classification method (OCM) only considering the surface station values. Once obtained the dates and the onset time of each event, the prior closest sounding for each event was taken to realize a principal component analysis (PCA) that is used to identify the leading patterns of the vertical structure of the atmosphere previously to a zonda wind event. These components were used to construct the index model. For the PCA an entry matrix of temperature ( T) and dew point temperature (Td) anomalies for the standard levels between 850 and 300 hPa was build. The analysis yielded six significant components with a 94 % of the variance explained and the leading patterns of favorable weather conditions for the development of the phenomenon were obtained. A zonda/non-zonda indicator c can be estimated by a logistic multiple regressions depending on the PCA component loadings, determining a zonda probability index \\widehat{c} calculable from T and Td profiles and it depends on the climatological features of the region. The index showed 74.7 % efficiency. The same analysis was performed by adding surface values of T and Td from Mendoza Aero station increasing the index efficiency to 87.8 %. The results revealed four significantly correlated PCs with a major improvement in differentiating zonda cases and a reducing of the uncertainty interval.

  20. Component fragilities - data collection, analysis and interpretation

    International Nuclear Information System (INIS)

    Bandyopadhyay, K.K.; Hofmayer, C.H.

    1986-01-01

    As part of the component fragility research program sponsored by the US Nuclear Regulatory Commission, BNL is involved in establishing seismic fragility levels for various nuclear power plant equipment with emphasis on electrical equipment, by identifying, collecting and analyzing existing test data from various sources. BNL has reviewed approximately seventy test reports to collect fragility or high level test data for switchgears, motor control centers and similar electrical cabinets, valve actuators and numerous electrical and control devices of various manufacturers and models. Through a cooperative agreement, BNL has also obtained test data from EPRI/ANCO. An analysis of the collected data reveals that fragility levels can best be described by a group of curves corresponding to various failure modes. The lower bound curve indicates the initiation of malfunctioning or structural damage, whereas the upper bound curve corresponds to overall failure of the equipment based on known failure modes occurring separately or interactively. For some components, the upper and lower bound fragility levels are observed to vary appreciably depending upon the manufacturers and models. An extensive amount of additional fragility or high level test data exists. If completely collected and properly analyzed, the entire data bank is expected to greatly reduce the need for additional testing to establish fragility levels for most equipment

  1. Component fragilities. Data collection, analysis and interpretation

    International Nuclear Information System (INIS)

    Bandyopadhyay, K.K.; Hofmayer, C.H.

    1985-01-01

    As part of the component fragility research program sponsored by the US NRC, BNL is involved in establishing seismic fragility levels for various nuclear power plant equipment with emphasis on electrical equipment. To date, BNL has reviewed approximately seventy test reports to collect fragility or high level test data for switchgears, motor control centers and similar electrical cabinets, valve actuators and numerous electrical and control devices, e.g., switches, transmitters, potentiometers, indicators, relays, etc., of various manufacturers and models. BNL has also obtained test data from EPRI/ANCO. Analysis of the collected data reveals that fragility levels can best be described by a group of curves corresponding to various failure modes. The lower bound curve indicates the initiation of malfunctioning or structural damage, whereas the upper bound curve corresponds to overall failure of the equipment based on known failure modes occurring separately or interactively. For some components, the upper and lower bound fragility levels are observed to vary appreciably depending upon the manufacturers and models. For some devices, testing even at the shake table vibration limit does not exhibit any failure. Failure of a relay is observed to be a frequent cause of failure of an electrical panel or a system. An extensive amount of additional fregility or high level test data exists

  2. MD-11 PCA - Research flight team photo

    Science.gov (United States)

    1995-01-01

    On Aug. 30, 1995, a the McDonnell Douglas MD-11 transport aircraft landed equipped with a computer-assisted engine control system that has the potential to increase flight safety. In landings at NASA Dryden Flight Research Center, Edwards, California, on August 29 and 30, the aircraft demonstrated software used in the aircraft's flight control computer that essentially landed the MD-11 without a need for the pilot to manipulate the flight controls significantly. In partnership with McDonnell Douglas Aerospace (MDA), with Pratt & Whitney and Honeywell helping to design the software, NASA developed this propulsion-controlled aircraft (PCA) system following a series of incidents in which hydraulic failures resulted in the loss of flight controls. This new system enables a pilot to operate and land the aircraft safely when its normal, hydraulically-activated control surfaces are disabled. This August 29, 1995, photo shows the MD-11 team. Back row, left to right: Tim Dingen, MDA pilot; John Miller, MD-11 Chief pilot (MDA); Wayne Anselmo, MD-11 Flight Test Engineer (MDA); Gordon Fullerton, PCA Project pilot; Bill Burcham, PCA Chief Engineer; Rudey Duran, PCA Controls Engineer (MDA); John Feather, PCA Controls Engineer (MDA); Daryl Townsend, Crew Chief; Henry Hernandez, aircraft mechanic; Bob Baron, PCA Project Manager; Don Hermann, aircraft mechanic; Jerry Cousins, aircraft mechanic; Eric Petersen, PCA Manager (Honeywell); Trindel Maine, PCA Data Engineer; Jeff Kahler, PCA Software Engineer (Honeywell); Steve Goldthorpe, PCA Controls Engineer (MDA). Front row, left to right: Teresa Hass, Senior Project Management Analyst; Hollie Allingham (Aguilera), Senior Project Management Analyst; Taher Zeglum, PCA Data Engineer (MDA); Drew Pappas, PCA Project Manager (MDA); John Burken, PCA Control Engineer.

  3. Principal component analysis for the forensic discrimination of black inkjet inks based on the Vis-NIR fibre optics reflection spectra.

    Science.gov (United States)

    Gál, Lukáš; Oravec, Michal; Gemeiner, Pavol; Čeppan, Michal

    2015-12-01

    Nineteen black inkjet inks of six different brands were examined by fibre optics reflection spectroscopy in Visible and Near Infrared Region (Vis-NIR FORS) directly on paper with a view to achieving good resolution between them. These different inks were tested on nineteen different inkjet printers from three brands. Samples were obtained from prints by reflection probe. Processed reflection spectra in the range 500-1000 nm were used as samples in principal component analysis. Variability between spectra of the same ink obtained from different prints, as well as between spectra of square areas and lines was examined. For both spectra obtained from square areas and lines reference, Principal Component Analysis (PCA) models were created. According to these models, the inkjet inks were divided into clusters. PCA method is able to separate inks containing carbon black as main colorant from the other inks using other colorants. Some spectra were recorded from another piece of printer and used as validation samples. Spectra of validation samples were projected onto reference PCA models. According to position of validation samples in score plots it can be concluded that PCA based on Vis-NIR FORS can reliably differentiate inkjet inks which are included in the reference database. The presented method appears to be a suitable tool for forensic examination of questioned documents containing inkjet inks. Inkjet inks spectra were obtained without extraction or cutting sample with possibility to measure out of the laboratory. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  4. A Study of Wind Turbine Comprehensive Operational Assessment Model Based on EM-PCA Algorithm

    Science.gov (United States)

    Zhou, Minqiang; Xu, Bin; Zhan, Yangyan; Ren, Danyuan; Liu, Dexing

    2018-01-01

    To assess wind turbine performance accurately and provide theoretical basis for wind farm management, a hybrid assessment model based on Entropy Method and Principle Component Analysis (EM-PCA) was established, which took most factors of operational performance into consideration and reach to a comprehensive result. To verify the model, six wind turbines were chosen as the research objects, the ranking obtained by the method proposed in the paper were 4#>6#>1#>5#>2#>3#, which are completely in conformity with the theoretical ranking, which indicates that the reliability and effectiveness of the EM-PCA method are high. The method could give guidance for processing unit state comparison among different units and launching wind farm operational assessment.

  5. Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2013-01-01

    Full Text Available A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized -dimensional Principal Component Analysis (GND-PCA is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.

  6. Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

    Directory of Open Access Journals (Sweden)

    Ernestina Martel

    2018-06-01

    Full Text Available Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA, suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.

  7. Posterior cerebral artery involvement in moyamoya disease: initial infarction and angle between PCA and basilar artery.

    Science.gov (United States)

    Lee, Ji Yeoun; Kim, Seung-Ki; Cheon, Jung-Eun; Choi, Jung Won; Phi, Ji Hoon; Kim, In-One; Cho, Byung-Kyu; Wang, Kyu-Chang

    2013-12-01

    Moyamoya disease (MMD) is a chronic cerebrovascular occlusive disease, and progressive involvement of the posterior cerebral artery (PCA) has been reported. However, majority of MMD articles are presenting classic anterior circulation related issues. This study investigates the preoperative factors related to the long-term outcome of posterior circulation in MMD. Retrospective review of 88 MMD patients (166 PCAs in either hemisphere) without symptomatic disease involvement of PCA at initial diagnosis was done. Data at initial diagnosis regarding age, presence of infarction, status of the PCA, type of posterior communicating artery, and the angle between PCA and basilar artery were reviewed. Progressive stenosis of PCA was evaluated by symptom or radiological imaging during follow up. During an average follow up of 8.3 years, 29 out of 166 (18 %) evaluated PCAs showed progressive disease involvement. The average time of progression from the initial operation was 4.9 years, with the latest onset at 10.8 years. The patients who showed progressive stenosis of the PCA tended to be younger, present with infarction, have smaller angle between PCA and basilar artery, and have asymptomatic stenosis of the PCA at initial presentation. However, multivariate analysis confirmed only the presence of initial infarction and a smaller angle between PCA and basilar artery to be significantly associated with progressive stenosis of PCA. Involvement of PCA in MMD may occur in a delayed fashion, years after the completion of revascularization of anterior circulation. Persistent long-term follow-up regarding the posterior circulation is recommended.

  8. Magnetic Flux Leakage and Principal Component Analysis for metal loss approximation in a pipeline

    International Nuclear Information System (INIS)

    Ruiz, M; Mujica, L E; Quintero, M; Florez, J; Quintero, S

    2015-01-01

    Safety and reliability of hydrocarbon transportation pipelines represent a critical aspect for the Oil an Gas industry. Pipeline failures caused by corrosion, external agents, among others, can develop leaks or even rupture, which can negatively impact on population, natural environment, infrastructure and economy. It is imperative to have accurate inspection tools traveling through the pipeline to diagnose the integrity. In this way, over the last few years, different techniques under the concept of structural health monitoring (SHM) have continuously been in development.This work is based on a hybrid methodology that combines the Magnetic Flux Leakage (MFL) and Principal Components Analysis (PCA) approaches. The MFL technique induces a magnetic field in the pipeline's walls. The data are recorded by sensors measuring leakage magnetic field in segments with loss of metal, such as cracking, corrosion, among others. The data provide information of a pipeline with 15 years of operation approximately, which transports gas, has a diameter of 20 inches and a total length of 110 km (with several changes in the topography). On the other hand, PCA is a well-known technique that compresses the information and extracts the most relevant information facilitating the detection of damage in several structures. At this point, the goal of this work is to detect and localize critical loss of metal of a pipeline that are currently working. (paper)

  9. Evaluation of Service Life of Polystyrene in Tropical Marine Environment by Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Dongdong Song

    2015-01-01

    Full Text Available To predict the service life of polystyrene (PS under an aggressive environment, the nondimensional expression Z was established from a data set of multiple properties of PS by principal component analysis (PCA. In this study, PS specimens were exposed to the tropical environment on Xisha Islands in China for two years. Chromatic aberration, gloss, tensile strength, elongation at break, flexural strength, and impact strength were tested to evaluate the aging behavior of PS. Based on different needs of industries, each of the multiple properties could be used to evaluate the service life of PS. However, selecting a single performance variation will inevitably hide some information about the entire aging process. Therefore, finding a comprehensive measure representing the overall aging performance of PS can be highly significant. Herein, PCA was applied to obtain a specific property (Z which can represent all properties of PS. Z of PS degradation showed a slight decrease for the initial two months of exposure after which it increased rapidly in the next eight months. Subsequently, a slower increase of Z value was observed. From the three different stages shown as Z value increases, three stages have been identified for PS service life.

  10. Magnetic Flux Leakage and Principal Component Analysis for metal loss approximation in a pipeline

    Science.gov (United States)

    Ruiz, M.; Mujica, L. E.; Quintero, M.; Florez, J.; Quintero, S.

    2015-07-01

    Safety and reliability of hydrocarbon transportation pipelines represent a critical aspect for the Oil an Gas industry. Pipeline failures caused by corrosion, external agents, among others, can develop leaks or even rupture, which can negatively impact on population, natural environment, infrastructure and economy. It is imperative to have accurate inspection tools traveling through the pipeline to diagnose the integrity. In this way, over the last few years, different techniques under the concept of structural health monitoring (SHM) have continuously been in development. This work is based on a hybrid methodology that combines the Magnetic Flux Leakage (MFL) and Principal Components Analysis (PCA) approaches. The MFL technique induces a magnetic field in the pipeline's walls. The data are recorded by sensors measuring leakage magnetic field in segments with loss of metal, such as cracking, corrosion, among others. The data provide information of a pipeline with 15 years of operation approximately, which transports gas, has a diameter of 20 inches and a total length of 110 km (with several changes in the topography). On the other hand, PCA is a well-known technique that compresses the information and extracts the most relevant information facilitating the detection of damage in several structures. At this point, the goal of this work is to detect and localize critical loss of metal of a pipeline that are currently working.

  11. Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis

    International Nuclear Information System (INIS)

    Kaistha, Nitin; Upadhyaya, Belle R.

    2001-01-01

    An integrated method for the detection and isolation of incipient faults in common field devices, such as sensors and actuators, using plant operational data is presented. The approach is based on the premise that data for normal operation lie on a surface and abnormal situations lead to deviations from the surface in a particular way. Statistically significant deviations from the surface result in the detection of faults, and the characteristic directions of deviations are used for isolation of one or more faults from the set of typical faults. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and fit a hyperplane to the data. The fault direction for each of the scenarios is obtained using the singular value decomposition on the state and control function prediction errors, and fault isolation is then accomplished from projections on the fault directions. This approach is demonstrated for a simulated pressurized water reactor steam generator system and for a laboratory process control system under single device fault conditions. Enhanced fault isolation capability is also illustrated by incorporating realistic nonlinear terms in the PCA data matrix

  12. A neuro-fuzzy inference system for sensor failure detection using wavelet denoising, PCA and SPRT

    International Nuclear Information System (INIS)

    Na, Man Gyun

    2001-01-01

    In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA(principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system. The PCA is used to reduce the dimension of an input space without losing a significant amount of information, The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors

  13. News Schemes for Activity Recognition Systems Using PCA-WSVM, ICA-WSVM, and LDA-WSVM

    Directory of Open Access Journals (Sweden)

    M’hamed Bilal Abidine

    2015-08-01

    Full Text Available Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA, Independent Component Analysis (ICA, and Linear Discriminant Analysis (LDA. The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM classifier. This classifier is used to handle the problem of imbalanced activity data from the sensor readings. The experiments were implemented on multiple real-world datasets with Conditional Random Fields (CRF, standard Support Vector Machines (SVM, Weighted SVM, and combined methods PCA+WSVM, ICA+WSVM, and LDA+WSVM showed that LDA+WSVM had a higher recognition rate than other methods for activity recognition.

  14. Principal component analysis to assess the efficiency and mechanism for enhanced coagulation of natural algae-laden water using a novel dual coagulant system.

    Science.gov (United States)

    Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun; Ren, Yuan; Hu, Yun

    2014-02-01

    A novel dual coagulant system of polyaluminum chloride sulfate (PACS) and polydiallyldimethylammonium chloride (PDADMAC) was used to treat natural algae-laden water from Meiliang Gulf, Lake Taihu. PACS (Aln(OH)mCl3n-m-2k(SO4)k) has a mass ratio of 10 %, a SO4 (2-)/Al3 (+) mole ratio of 0.0664, and an OH/Al mole ratio of 2. The PDADMAC ([C8H16NCl]m) has a MW which ranges from 5 × 10(5) to 20 × 10(5) Da. The variations of contaminants in water samples during treatments were estimated in the form of principal component analysis (PCA) factor scores and conventional variables (turbidity, DOC, etc.). Parallel factor analysis determined four chromophoric dissolved organic matters (CDOM) components, and PCA identified four integrated principle factors. PCA factor 1 had significant correlations with chlorophyll-a (r=0.718), protein-like CDOM C1 (0.689), and C2 (0.756). Factor 2 correlated with UV254 (0.672), humic-like CDOM component C3 (0.716), and C4 (0.758). Factors 3 and 4 had correlations with NH3-N (0.748) and T-P (0.769), respectively. The variations of PCA factors scores revealed that PACS contributed less aluminum dissolution than PAC to obtain equivalent removal efficiency of contaminants. This might be due to the high cationic charge and pre-hydrolyzation of PACS. Compared with PACS coagulation (20 mg L(-1)), the removal of PCA factors 1, 2, and 4 increased 45, 33, and 12 %, respectively, in combined PACS-PDADMAC treatment (0.8 mg L(-1) +20 mg L(-1)). Since PAC contained more Al (0.053 g/1 g) than PACS (0.028 g/1 g), the results indicated that PACS contributed less Al dissolution into the water to obtain equivalent removal efficiency.

  15. Shallow-Land Buriable PCA-type austenitic stainless steel for fusion application

    International Nuclear Information System (INIS)

    Zucchetti, M.

    1991-01-01

    Neutron-induced activity in the PCA (Primary Candidate Alloy) austenitic stainless steel is examined, when used for first-wall components in a DEMO fusion reactor. Some low-activity definitions, based on different waste management and disposal concepts, are introduced. Activity in the PCA is so high that any recycling of the irradiated material can be excluded. Disposal of PCA radioactive wastes in Shallow-Land Buriable (SLB) is prevented as well. Mo, Nb and some impurity elements have to be removed or limited, in order to reduce the radioactivity of the PCA. Possible low-activity versions of the PCA are introduced (PCA-la); they meet the requirements for SLB and may also be recycled under certain conditions. (author)

  16. Predicting timing of foot strike during running, independent of striking technique, using principal component analysis of joint angles.

    Science.gov (United States)

    Osis, Sean T; Hettinga, Blayne A; Leitch, Jessica; Ferber, Reed

    2014-08-22

    As 3-dimensional (3D) motion-capture for clinical gait analysis continues to evolve, new methods must be developed to improve the detection of gait cycle events based on kinematic data. Recently, the application of principal component analysis (PCA) to gait data has shown promise in detecting important biomechanical features. Therefore, the purpose of this study was to define a new foot strike detection method for a continuum of striking techniques, by applying PCA to joint angle waveforms. In accordance with Newtonian mechanics, it was hypothesized that transient features in the sagittal-plane accelerations of the lower extremity would be linked with the impulsive application of force to the foot at foot strike. Kinematic and kinetic data from treadmill running were selected for 154 subjects, from a database of gait biomechanics. Ankle, knee and hip sagittal plane angular acceleration kinematic curves were chained together to form a row input to a PCA matrix. A linear polynomial was calculated based on PCA scores, and a 10-fold cross-validation was performed to evaluate prediction accuracy against gold-standard foot strike as determined by a 10 N rise in the vertical ground reaction force. Results show 89-94% of all predicted foot strikes were within 4 frames (20 ms) of the gold standard with the largest error being 28 ms. It is concluded that this new foot strike detection is an improvement on existing methods and can be applied regardless of whether the runner exhibits a rearfoot, midfoot, or forefoot strike pattern. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Assessment of genetic divergence in tomato through agglomerative hierarchical clustering and principal component analysis

    International Nuclear Information System (INIS)

    Iqbal, Q.; Saleem, M.Y.; Hameed, A.; Asghar, M.

    2014-01-01

    For the improvement of qualitative and quantitative traits, existence of variability has prime importance in plant breeding. Data on different morphological and reproductive traits of 47 tomato genotypes were analyzed for correlation,agglomerative hierarchical clustering and principal component analysis (PCA) to select genotypes and traits for future breeding program. Correlation analysis revealed significant positive association between yield and yield components like fruit diameter, single fruit weight and number of fruits plant-1. Principal component (PC) analysis depicted first three PCs with Eigen-value higher than 1 contributing 81.72% of total variability for different traits. The PC-I showed positive factor loadings for all the traits except number of fruits plant-1. The contribution of single fruit weight and fruit diameter was highest in PC-1. Cluster analysis grouped all genotypes into five divergent clusters. The genotypes in cluster-II and cluster-V exhibited uniform maturity and higher yield. The D2 statistics confirmed highest distance between cluster- III and cluster-V while maximum similarity was observed in cluster-II and cluster-III. It is therefore suggested that crosses between genotypes of cluster-II and cluster-V with those of cluster-I and cluster-III may exhibit heterosis in F1 for hybrid breeding and for selection of superior genotypes in succeeding generations for cross breeding programme. (author)

  18. Application of Principal Component Analysis in Prompt Gamma Spectra for Material Sorting

    Energy Technology Data Exchange (ETDEWEB)

    Im, Hee Jung; Lee, Yun Hee; Song, Byoung Chul; Park, Yong Joon; Kim, Won Ho

    2006-11-15

    For the detection of illicit materials in a very short time by comparing unknown samples' gamma spectra to pre-programmed material signatures, we at first, selected a method to reduce the noise of the obtained gamma spectra. After a noise reduction, a pattern recognition technique was applied to discriminate the illicit materials from the innocuous materials in the noise reduced data. Principal component analysis was applied for a noise reduction and pattern recognition in prompt gamma spectra. A computer program for the detection of illicit materials based on PCA method was developed in our lab and can be applied to the PGNAA system for the baggage checking at all ports of entry at a very short time.

  19. Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations

    International Nuclear Information System (INIS)

    Ahmed, M; Gu, F; Ball, A D

    2012-01-01

    Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T 2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

  20. Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

    Science.gov (United States)

    Xu, Shaoping; Zeng, Xiaoxia; Jiang, Yinnan; Tang, Yiling

    2018-01-01

    We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

  1. A novel normalization method based on principal component analysis to reduce the effect of peak overlaps in two-dimensional correlation spectroscopy

    Science.gov (United States)

    Wang, Yanwei; Gao, Wenying; Wang, Xiaogong; Yu, Zhiwu

    2008-07-01

    Two-dimensional correlation spectroscopy (2D-COS) has been widely used to separate overlapped spectroscopic bands. However, band overlap may sometimes cause misleading results in the 2D-COS spectra, especially if one peak is embedded within another peak by the overlap. In this work, we propose a new normalization method, based on principal component analysis (PCA). For each spectrum under discussion, the first principal component of PCA is simply taken as the normalization factor of the spectrum. It is demonstrated that the method works well with simulated dynamic spectra. Successful result has also been obtained from the analysis of an overlapped band in the wavenumber range 1440-1486 cm -1 for the evaporation process of a solution containing behenic acid, methanol, and chloroform.

  2. Gene set analysis using variance component tests.

    Science.gov (United States)

    Huang, Yen-Tsung; Lin, Xihong

    2013-06-28

    Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

  3. Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream.

    Science.gov (United States)

    Byrne, Patrick; Runkel, Robert L; Walton-Day, Katherine

    2017-07-01

    Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.

  4. Validation of the academic management evaluation instru-ment based on principal component analysis for engineering and technological courses

    Directory of Open Access Journals (Sweden)

    Albano Oliveira Nunes

    2015-05-01

    Full Text Available In recent years, the expansion of higher education in Brazil has led to a series of demands related to aspects concerning training at the college level. These processes relate to: academics, professionals, entering in the labor market, among others. In this context, an important aspect is the quality of the courses. Thus, the evaluation becomes a critical diagnostic process of reality and starting point for possible interventions to be put in practice by the coordinators of the programs. This article presents the results of a questionnaire administered at the Federal University of Ceará (UFC, especially to  Systems & Digital Media and Engineering Programs professors. This research aims to identify how the professors from each department see the administrative procedures developed by the departments and also investigate the possibility of using Principal Components Analysis (PCA as a support for management of the higher education training. The methodology included the implementation of Likert scale questionnaire and subsequent mathematical treatment with PCA. The results indicate the potential application of PCA to support the management of higher education; it was possible to extract preliminaries inferences related to management methods and their characteristics. This suggests the possibility of developing the Educametrics field.

  5. Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

    Science.gov (United States)

    Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

    2009-07-01

    A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

  6. Thermogravimetric analysis of combustible waste components

    DEFF Research Database (Denmark)

    Munther, Anette; Wu, Hao; Glarborg, Peter

    In order to gain fundamental knowledge about the co-combustion of coal and waste derived fuels, the pyrolytic behaviors of coal, four typical waste components and their mixtures have been studied by a simultaneous thermal analyzer (STA). The investigated waste components were wood, paper, polypro......In order to gain fundamental knowledge about the co-combustion of coal and waste derived fuels, the pyrolytic behaviors of coal, four typical waste components and their mixtures have been studied by a simultaneous thermal analyzer (STA). The investigated waste components were wood, paper...

  7. SU-C-BRF-03: PCA Modeling of Anatomical Changes During Head and Neck Radiation Therapy

    International Nuclear Information System (INIS)

    Chetvertkov, M; Kim, J; Siddiqui, F; Kumarasiri, A; Chetty, I; Gordon, J

    2014-01-01

    Purpose: To develop principal component analysis (PCA) models from daily cone beam CTs (CBCTs) of head and neck (H and N) patients that could be used prospectively in adaptive radiation therapy (ART). Methods: : For 7 H and N patients, Pinnacle Treatment Planning System (Philips Healthcare) was used to retrospectively deformably register daily CBCTs to the planning CT. The number N of CBCTs per treatment course ranged from 14 to 22. For each patient a PCA model was built from the deformation vector fields (DVFs), after first subtracting the mean DVF, producing N eigen-DVFs (EDVFs). It was hypothesized that EDVFs with large eigenvalues represent the major anatomical deformations during the course of treatment, and that it is feasible to relate each EDVF to a clinically meaningful systematic or random change in anatomy, such as weight loss, neck flexion, etc. Results: DVFs contained on the order of 3×87×87×58=1.3 million scalar values (3 times the number of voxels in the registered volume). The top 3 eigenvalues accounted for ∼90% of variance. Anatomical changes corresponding to an EDVF were evaluated by generating a synthetic DVF, and applying that DVF to the CT to produce a synthetic CBCT. For all patients, the EDVF for the largest eigenvalue was interpreted to model weight loss. The EDVF for other eigenvalues appeared to represented quasi-random fraction-to-fraction changes. Conclusion: The leading EDVFs from single-patient PCA models have tentatively been identified with weight loss changes during treatment. Other EDVFs are tentatively identified as quasi-random inter-fraction changes. Clean separation of systematic and random components may require further work. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant anatomical changes, such as weight loss, early in treatment, triggering replanning where beneficial

  8. Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Alia Colniță

    2017-09-01

    Full Text Available Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS, are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei and Listeria monocytogenes (L. monocytogenes were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA to their specific spectral data.

  9. Empirical evaluation of grouping of lower urinary tract symptoms: principal component analysis of Tampere Ageing Male Urological Study data.

    Science.gov (United States)

    Pöyhönen, Antti; Häkkinen, Jukka T; Koskimäki, Juha; Hakama, Matti; Tammela, Teuvo L J; Auvinen, Anssi

    2013-03-01

    WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: The ICS has divided LUTS into three groups: storage, voiding and post-micturition symptoms. The classification is based on anatomical, physiological and urodynamic considerations of a theoretical nature. We used principal component analysis (PCA) to determine the inter-correlations of various LUTS, which is a novel approach to research and can strengthen existing knowledge of the phenomenology of LUTS. After we had completed our analyses, another study was published that used a similar approach and results were very similar to those of the present study. We evaluated the constellation of LUTS using PCA of the data from a population-based study that included >4000 men. In our analysis, three components emerged from the 12 LUTS: voiding, storage and incontinence components. Our results indicated that incontinence may be separate from the other storage symptoms and post-micturition symptoms should perhaps be regarded as voiding symptoms. To determine how lower urinary tract symptoms (LUTS) relate to each other and assess if the classification proposed by the International Continence Society (ICS) is consistent with empirical findings. The information on urinary symptoms for this population-based study was collected using a self-administered postal questionnaire in 2004. The questionnaire was sent to 7470 men, aged 30-80 years, from Pirkanmaa County (Finland), of whom 4384 (58.7%) returned the questionnaire. The Danish Prostatic Symptom Score-1 questionnaire was used to evaluate urinary symptoms. Principal component analysis (PCA) was used to evaluate the inter-correlations among various urinary symptoms. The PCA produced a grouping of 12 LUTS into three categories consisting of voiding, storage and incontinence symptoms. Post-micturition symptoms were related to voiding symptoms, but incontinence symptoms were separate from storage symptoms. In the analyses by age group, similar categorization was found at

  10. Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2007-01-01

    Full Text Available This paper presents the results of an experimental application of artificial neural network as a classifier of the degree of cracking of a tooth root in a gear wheel. The neural classifier was based on the artificial neural network of Probabilistic Neural Network type (PNN. The input data for the classifier was in a form of matrix composedof statistical measures, obtained from fast Fourier transform (FFT and principal component analysis (PCA. The identified model of toothed gear transmission, operating in a circulating power system, served for generation of the teaching and testing set applied for the experiment.

  11. Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: Part III. Using Combined PCA to Compare Spatiotemporal Variability of MODIS, MISR and OMI Aerosol Optical Depth

    Science.gov (United States)

    Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

    2014-01-01

    Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

  12. Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

    Science.gov (United States)

    Ueki, Kenta; Iwamori, Hikaru

    2017-10-01

    In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

  13. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

    Directory of Open Access Journals (Sweden)

    Balbir Singh

    2017-01-01

    Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

  14. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

    Science.gov (United States)

    Wagatsuma, Hiroaki

    2017-01-01

    EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

  15. Classification and structural analysis of live and dead salmonella cells using fourier transform infrared (FT-IR) spectroscopy and principle component analysis (PCA)

    Science.gov (United States)

    Fourier Transform Infrared Spectroscopy (FT-IR) was used to detect Salmonella typhimurium and Salmonella enteritidis foodborne bacteria and distinguish between live and dead cells of both serotypes. Bacteria were loaded individually on the ZnSe Attenuated Total Reflection (ATR) crystal surface and s...

  16. Finding the limit of diverging components in three-way Candecomp/Parafac : A demonstration of its practical merits

    NARCIS (Netherlands)

    Stegeman, Alwin

    Three-way Candecomp/Parafac (CP) is a three-way generalization of principal component analysis (PCA) for matrices. Contrary to PCA, a CP decomposition is rotationally unique under mild conditions. However, a CP analysis may be hampered by the non-existence of a best-fitting CP decomposition with R≤2

  17. Limits of principal components analysis for producing a common trait space: implications for inferring selection, contingency, and chance in evolution.

    Directory of Open Access Journals (Sweden)

    Kevin J Parsons

    2009-11-01

    Full Text Available Comparing patterns of divergence among separate lineages or groups has posed an especially difficult challenge for biologists. Recently a new, conceptually simple methodology called the "ordered-axis plot" approach was introduced for the purpose of comparing patterns of diversity in a common morphospace. This technique involves a combination of principal components analysis (PCA and linear regression. Given the common use of these statistics the potential for the widespread use of the ordered axis approach is high. However, there are a number of drawbacks to this approach, most notably that lineages with the greatest amount of variance will largely bias interpretations from analyses involving a common morphospace. Therefore, without meeting a set of a priori requirements regarding data structure the ordered-axis plot approach will likely produce misleading results.Morphological data sets from cichlid fishes endemic to Lakes Tanganyika, Malawi, and Victoria were used to statistically demonstrate how separate groups can have differing contributions to a common morphospace produced by a PCA. Through a matrix superimposition of eigenvectors (scale-free trajectories of variation identified by PCA we show that some groups contribute more to the trajectories of variation identified in a common morphospace. Furthermore, through a set of randomization tests we show that a common morphospace model partitions variation differently than group-specific models. Finally, we demonstrate how these limitations may influence an ordered-axis plot approach by performing a comparison on data sets with known alterations in covariance structure. Using these results we provide a set of criteria that must be met before a common morphospace can be reliably used.Our results suggest that a common morphospace produced by PCA would not be useful for producing biologically meaningful results unless a restrictive set of criteria are met. We therefore suggest biologists be aware

  18. Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream

    Science.gov (United States)

    Byrne, Patrick; Runkel, Robert L.; Walton-Day, Katie

    2017-01-01

    Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.

  19. Detection and Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of GPS Time Series

    Science.gov (United States)

    Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

    2014-12-01

    A critical point in the analysis of ground displacements time series is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies. Indeed, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we present the application of the vbICA technique to GPS position time series. First, we use vbICA on synthetic data that simulate a seismic cycle

  20. Synchrotron-Based Microspectroscopic Analysis of Molecular and Biopolymer Structures Using Multivariate Techniques and Advanced Multi-Components Modeling

    International Nuclear Information System (INIS)

    Yu, P.

    2008-01-01

    More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.

  1. Analysis of failed nuclear plant components

    Science.gov (United States)

    Diercks, D. R.

    1993-12-01

    Argonne National Laboratory has conducted analyses of failed components from nuclear power- gener-ating stations since 1974. The considerations involved in working with and analyzing radioactive compo-nents are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in serv-ice. The failures discussed are (1) intergranular stress- corrosion cracking of core spray injection piping in a boiling water reactor, (2) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressurized water reactor, (3) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (4) failure of pump seal wear rings by nickel leaching in a boiling water reactor.

  2. Analysis of failed nuclear plant components

    International Nuclear Information System (INIS)

    Diercks, D.R.

    1993-01-01

    Argonne National Laboratory has conducted analyses of failed components from nuclear power-generating stations since 1974. The considerations involved in working with an analyzing radioactive components are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in service. The failures discussed are (1) intergranular stress-corrosion cracking of core spray injection piping in a boiling water reactor, (2) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressurized water reactor, (3) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (4) failure of pump seal wear rings by nickel leaching in a boiling water reactor

  3. Analysis of failed nuclear plant components

    International Nuclear Information System (INIS)

    Diercks, D.R.

    1992-07-01

    Argonne National Laboratory has conducted analyses of failed components from nuclear power generating stations since 1974. The considerations involved in working with and analyzing radioactive components are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in service. The failures discussed are (a) intergranular stress corrosion cracking of core spray injection piping in a boiling water reactor, (b) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressure water reactor, (c) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (d) failure of pump seal wear rings by nickel leaching in a boiling water reactor

  4. A radiographic analysis of implant component misfit.

    LENUS (Irish Health Repository)

    Sharkey, Seamus

    2011-07-01

    Radiographs are commonly used to assess the fit of implant components, but there is no clear agreement on the amount of misfit that can be detected by this method. This study investigated the effect of gap size and the relative angle at which a radiograph was taken on the detection of component misfit. Different types of implant connections (internal or external) and radiographic modalities (film or digital) were assessed.

  5. Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

    Science.gov (United States)

    Reena Benjamin, J; Jayasree, T

    2018-02-01

    In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

  6. Principal components analysis based control of a multi-dof underactuated prosthetic hand

    Directory of Open Access Journals (Sweden)

    Magenes Giovanni

    2010-04-01

    Full Text Available Abstract Background Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG. Driving a multi degrees of freedom (DoF hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. Methods A Principal Components Analysis (PCA based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs. Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Results Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture may be achieved. Conclusions This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

  7. Automatic detection of optic disc based on PCA and mathematical morphology.

    Science.gov (United States)

    Morales, Sandra; Naranjo, Valery; Angulo, Us; Alcaniz, Mariano

    2013-04-01

    The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.

  8. EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection

    Science.gov (United States)

    Fitriah, N.; Wijaya, S. K.; Fanany, M. I.; Badri, C.; Rezal, M.

    2017-07-01

    In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

  9. Post-hoc principal component analysis on a largely illiterate elderly population from North-west India to identify important elements of mini-mental state examination

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Raina

    2016-01-01

    Full Text Available Background: Mini-mental state examination (MMSE scale measures cognition using specific elements that can be isolated, defined, and subsequently measured. This study was conducted with the aim to analyze the factorial structure of MMSE in a largely, illiterate, elderly population in India and to reduce the number of variables to a few meaningful and interpretable combinations. Methodology: Principal component analysis (PCA was performed post-hoc on the data generated by a research project conducted to estimate the prevalence of dementia in four geographically defined habitations in Himachal Pradesh state of India. Results: Questions on orientation and registration account for high percentage of cumulative variance in comparison to other questions. Discussion: The PCA conducted on the data derived from a largely, illiterate population reveals that the most important components to consider for the estimation of cognitive impairment in illiterate Indian population are temporal orientation, spatial orientation, and immediate memory.

  10. Post-hoc principal component analysis on a largely illiterate elderly population from North-west India to identify important elements of mini-mental state examination.

    Science.gov (United States)

    Raina, Sunil Kumar; Chander, Vishav; Raina, Sujeet; Grover, Ashoo

    2016-01-01

    Mini-mental state examination (MMSE) scale measures cognition using specific elements that can be isolated, defined, and subsequently measured. This study was conducted with the aim to analyze the factorial structure of MMSE in a largely, illiterate, elderly population in India and to reduce the number of variables to a few meaningful and interpretable combinations. Principal component analysis (PCA) was performed post-hoc on the data generated by a research project conducted to estimate the prevalence of dementia in four geographically defined habitations in Himachal Pradesh state of India. Questions on orientation and registration account for high percentage of cumulative variance in comparison to other questions. The PCA conducted on the data derived from a largely, illiterate population reveals that the most important components to consider for the estimation of cognitive impairment in illiterate Indian population are temporal orientation, spatial orientation, and immediate memory.

  11. PCA-based algorithm for calibration of spectrophotometric analysers of food

    International Nuclear Information System (INIS)

    Morawski, Roman Z; Miekina, Andrzej

    2013-01-01

    Spectrophotometric analysers of food, being instruments for determination of the composition of food products and ingredients, are today of growing importance for food industry, as well as for food distributors and consumers. Their metrological performance significantly depends of the numerical performance of available means for spectrophotometric data processing; in particular – the means for calibration of analysers. In this paper, a new algorithm for this purpose is proposed, viz. the algorithm using principal components analysis (PCA). It is almost as efficient as PLS-based algorithms of calibration, but much simpler

  12. Lifetime analysis of fusion-reactor components

    International Nuclear Information System (INIS)

    Mattas, R.F.

    1983-01-01

    A one-dimensional computer code has been developed to examine the lifetime of first-wall and impurity-control components. The code incorporates the operating and design parameters, the material characteristics, and the appropriate failure criteria for the individual components. The major emphasis of the modelling effort has been to calculate the temperature-stress-strain-radiation effects history of a component so that the synergystic effects between sputtering erosion, swelling, creep, fatigue, and crack growth can be examined. The general forms of the property equations are the same for all materials in order to provide the greatest flexibility for materials selection in the code. The code is capable of determining the behavior of a plate, composed of either a single or dual material structure, that is either totally constrained or constrained from bending but not from expansion. The code has been utilized to analyze the first walls for FED/INTOR and DEMO

  13. Generalized structured component analysis a component-based approach to structural equation modeling

    CERN Document Server

    Hwang, Heungsun

    2014-01-01

    Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new a...

  14. Principal component analysis of psoriasis lesions images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A set of RGB images of psoriasis lesions is used. By visual examination of these images, there seem to be no common pattern that could be used to find and align the lesions within and between sessions. It is expected that the principal components of the original images could be useful during future...

  15. Prostate health index (phi) and prostate cancer antigen 3 (PCA3) significantly improve diagnostic accuracy in patients undergoing prostate biopsy.

    Science.gov (United States)

    Perdonà, Sisto; Bruzzese, Dario; Ferro, Matteo; Autorino, Riccardo; Marino, Ada; Mazzarella, Claudia; Perruolo, Giuseppe; Longo, Michele; Spinelli, Rosa; Di Lorenzo, Giuseppe; Oliva, Andrea; De Sio, Marco; Damiano, Rocco; Altieri, Vincenzo; Terracciano, Daniela

    2013-02-15

    Prostate health index (phi) and prostate cancer antigen 3 (PCA3) have been recently proposed as novel biomarkers for prostate cancer (PCa). We assessed the diagnostic performance of these biomarkers, alone or in combination, in men undergoing first prostate biopsy for suspicion of PCa. One hundred sixty male subjects were enrolled in this prospective observational study. PSA molecular forms, phi index (Beckman coulter immunoassay), PCA3 score (Progensa PCA3 assay), and other established biomarkers (tPSA, fPSA, and %fPSA) were assessed before patients underwent a 18-core first prostate biopsy. The discriminating ability between PCa-negative and PCa-positive biopsies of Beckman coulter phi and PCA3 score and other used biomarkers were determined. One hundred sixty patients met inclusion criteria. %p2PSA (p2PSA/fPSA × 100), phi and PCA3 were significantly higher in patients with PCa compared to PCa-negative group (median values: 1.92 vs. 1.55, 49.97 vs. 36.84, and 50 vs. 32, respectively, P ≤ 0.001). ROC curve analysis showed that %p2PSA, phi, and PCA3 are good indicator of malignancy (AUCs = 0.68, 0.71, and 0.66, respectively). A multivariable logistic regression model consisting of both the phi index and PCA3 score allowed to reach an overall diagnostic accuracy of 0.77. Decision curve analysis revealed that this "combined" marker achieved the highest net benefit over the examined range of the threshold probability. phi and PCA3 showed no significant difference in the ability to predict PCa diagnosis in men undergoing first prostate biopsy. However, diagnostic performance is significantly improved by combining phi and PCA3. Copyright © 2012 Wiley Periodicals, Inc.

  16. Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region, Ghana.

    Science.gov (United States)

    Krefis, Anne Caroline; Schwarz, Norbert Georg; Nkrumah, Bernard; Acquah, Samuel; Loag, Wibke; Sarpong, Nimako; Adu-Sarkodie, Yaw; Ranft, Ulrich; May, Jürgen

    2010-07-13

    The socioeconomic and sociodemographic situation are important components for the design and assessment of malaria control measures. In malaria endemic areas, however, valid classification of socioeconomic factors is difficult due to the lack of standardized tax and income data. The objective of this study was to quantify household socioeconomic levels using principal component analyses (PCA) to a set of indicator variables and to use a classification scheme for the multivariate analysis of children<15 years of age presented with and without malaria to an outpatient department of a rural hospital. In total, 1,496 children presenting to the hospital were examined for malaria parasites and interviewed with a standardized questionnaire. The information of eleven indicators of the family's housing situation was reduced by PCA to a socioeconomic score, which was then classified into three socioeconomic status (poor, average and rich). Their influence on the malaria occurrence was analysed together with malaria risk co-factors, such as sex, parent's educational and ethnic background, number of children living in a household, applied malaria protection measures, place of residence and age of the child and the mother. The multivariate regression analysis demonstrated that the proportion of children with malaria decreased with increasing socioeconomic status as classified by PCA (p<0.05). Other independent factors for malaria risk were the use of malaria protection measures (p<0.05), the place of residence (p<0.05), and the age of the child (p<0.05). The socioeconomic situation is significantly associated with malaria even in holoendemic rural areas where economic differences are not much pronounced. Valid classification of the socioeconomic level is crucial to be considered as confounder in intervention trials and in the planning of malaria control measures.

  17. EXAFS and principal component analysis : a new shell game

    International Nuclear Information System (INIS)

    Wasserman, S.

    1998-01-01

    The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions

  18. Identification of an IL-1-induced gene expression pattern in AR+ PCa cells that mimics the molecular phenotype of AR- PCa cells.

    Science.gov (United States)

    Thomas-Jardin, Shayna E; Kanchwala, Mohammed S; Jacob, Joan; Merchant, Sana; Meade, Rachel K; Gahnim, Nagham M; Nawas, Afshan F; Xing, Chao; Delk, Nikki A

    2018-06-01

    In immunosurveillance, bone-derived immune cells infiltrate the tumor and secrete inflammatory cytokines to destroy cancer cells. However, cancer cells have evolved mechanisms to usurp inflammatory cytokines to promote tumor progression. In particular, the inflammatory cytokine, interleukin-1 (IL-1), is elevated in prostate cancer (PCa) patient tissue and serum, and promotes PCa bone metastasis. IL-1 also represses androgen receptor (AR) accumulation and activity in PCa cells, yet the cells remain viable and tumorigenic; suggesting that IL-1 may also contribute to AR-targeted therapy resistance. Furthermore, IL-1 and AR protein levels negatively correlate in PCa tumor cells. Taken together, we hypothesize that IL-1 reprograms AR positive (AR + ) PCa cells into AR negative (AR - ) PCa cells that co-opt IL-1 signaling to ensure AR-independent survival and tumor progression in the inflammatory tumor microenvironment. LNCaP and PC3 PCa cells were treated with IL-1β or HS-5 bone marrow stromal cell (BMSC) conditioned medium and analyzed by RNA sequencing and RT-QPCR. To verify genes identified by RNA sequencing, LNCaP, MDA-PCa-2b, PC3, and DU145 PCa cell lines were treated with the IL-1 family members, IL-1α or IL-1β, or exposed to HS-5 BMSC in the presence or absence of Interleukin-1 Receptor Antagonist (IL-1RA). Treated cells were analyzed by western blot and/or RT-QPCR. Comparative analysis of sequencing data from the AR + LNCaP PCa cell line versus the AR - PC3 PCa cell line reveals an IL-1-conferred gene suite in LNCaP cells that is constitutive in PC3 cells. Bioinformatics analysis of the IL-1 regulated gene suite revealed that inflammatory and immune response pathways are primarily elicited; likely facilitating PCa cell survival and tumorigenicity in an inflammatory tumor microenvironment. Our data supports that IL-1 reprograms AR + PCa cells to mimic AR - PCa gene expression patterns that favor AR-targeted treatment resistance and cell survival. © 2018 Wiley

  19. Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC multivariate method

    Directory of Open Access Journals (Sweden)

    Grahić Jasmin

    2013-01-01

    Full Text Available In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H, thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC created 3 discriminant functions (DF, whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn’t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina’s common been landraces could be useful in future breeding programs.

  20. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    Science.gov (United States)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  1. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yihang Yin

    2015-08-01

    Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

  2. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

    Science.gov (United States)

    Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

    2015-08-07

    Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

  3. A principal components analysis of the factors effecting personal exposure to air pollution in urban commuters in Dublin, Ireland.

    Science.gov (United States)

    McNabola, Aonghus; Broderick, Brian M; Gill, Laurence W

    2009-10-01

    Principal component analysis was used to examine air pollution personal exposure data of four urban commuter transport modes for their interrelationships between pollutants and relationships with traffic and meteorological data. Air quality samples of PM2.5 and VOCs were recorded during peak traffic congestion for the car, bus, cyclist and pedestrian between January 2005 and June 2006 on a busy route in Dublin, Ireland. In total, 200 personal exposure samples were recorded each comprising 17 variables describing the personal exposure concentrations, meteorological conditions and traffic conditions. The data reduction technique, principal component analysis (PCA), was used to create weighted linear combinations of the data and these were subsequently examined for interrelationships between the many variables recorded. The results of the PCA found that personal exposure concentrations in non-motorised forms of transport were influenced to a higher degree by wind speed, whereas personal exposure concentrations in motorised forms of transport were influenced to a higher degree by traffic congestion. The findings of the investigation show that the most effective mechanisms of personal exposure reduction differ between motorised and non-motorised modes of commuter transport.

  4. Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI.

    Science.gov (United States)

    Soltysik, David A; Thomasson, David; Rajan, Sunder; Biassou, Nadia

    2015-02-15

    Functional magnetic resonance imaging (fMRI) time series are subject to corruption by many noise sources, especially physiological noise and motion. Researchers have developed many methods to reduce physiological noise, including RETROICOR, which retroactively removes cardiac and respiratory waveforms collected during the scan, and CompCor, which applies principal components analysis (PCA) to remove physiological noise components without any physiological monitoring during the scan. We developed four variants of the CompCor method. The optimized CompCor method applies PCA to time series in a noise mask, but orthogonalizes each component to the BOLD response waveform and uses an algorithm to determine a favorable number of components to use as "nuisance regressors." Whole brain component correction (WCompCor) is similar, except that it applies PCA to time-series throughout the whole brain. Low-pass component correction (LCompCor) identifies low-pass filtered components throughout the brain, while high-pass component correction (HCompCor) identifies high-pass filtered components. We compared the new methods with the original CompCor method by examining the resulting functional contrast-to-noise ratio (CNR), sensitivity, and specificity. (1) The optimized CompCor method increased the CNR and sensitivity compared to the original CompCor method and (2) the application of WCompCor yielded the best improvement in the CNR and sensitivity. The sensitivity of the optimized CompCor, WCompCor, and LCompCor methods exceeded that of the original CompCor method. However, regressing noise signals showed a paradoxical consequence of reducing specificity for all noise reduction methods attempted. Published by Elsevier B.V.

  5. ToF-SIMS and principal component analysis of lipids and amino acids from inflamed and dysplastic human colonic mucosa.

    Science.gov (United States)

    Urbini, Marco; Petito, Valentina; de Notaristefani, Francesco; Scaldaferri, Franco; Gasbarrini, Antonio; Tortora, Luca

    2017-10-01

    Here, time of flight secondary ion mass spectrometry (ToF-SIMS) and multivariate analysis were combined to study the role of ulcerative colitis (UC), a type of inflammatory bowel disease (IBD), in the colon cancer progression. ToF-SIMS was used to obtain mass spectra and chemical maps from the mucosal surface of human normal (NC), inflamed (IC), and dysplastic (DC) colon tissues. Chemical mapping with a lateral resolution of ≈ 1 μm allowed to evaluate zonation of fatty acids and amino acids as well as the morphological condition of the intestinal glands. High mass resolution ToF-SIMS spectra showed chemical differences in lipid and amino acid composition as a function of pathological state. In positive ion mode, mono- (MAG), di- (DAG), and triacylglycerol (TAG) signals were detected in NC tissues, while in IC and DC tissues, the only cholesterol was present as lipid class representative. Signals from fatty acids, collected in negative ion mode, were subjected to principal component analysis (PCA). PCA showed a strict correlation between IC and DC samples, due to an increase of stearic, arachidonic, and linoleic acid. In the same way, differences in the amino acid composition were highlighted through multivariate analysis. PCA revealed that glutamic acid, leucine/isoleucine, and valine fragments are related to IC tissues. On the other hand, tyrosine, methionine, and tryptophan peaks contributed highly to the separation of DC tissues. Finally, a classification of NC, IC, and DC patients was also achieved through hierarchical cluster analysis of amino acid fragments. In this case, human colonic inflammation showed a stronger relationship with normal than dysplastic condition. Graphical Abstract ᅟ.

  6. Classificação periódica: um exemplo didático para ensinar análise de componentes principais Periodic classification: a didactic example to teach principal component analysis

    Directory of Open Access Journals (Sweden)

    Wellington da Silva Lyra

    2010-01-01

    Full Text Available A dataset of chemical properties of the elements is used herein to introduce principal components analysis (PCA. The focus in this article is to verify the classification of the elements within the periodic table. The reclassification of the semimetals as metals or nonmetals emerges naturally from PCA and agrees with the current SBQ/IUPAC periodic table. Dataset construction, basic preprocessing, loading and score plots, and interpretation have been emphasized. This activity can be carried out even when students with distinct levels of formation are together in the same learning environment.

  7. PcaO Positively Regulates pcaHG of the β-Ketoadipate Pathway in Corynebacterium glutamicum▿

    OpenAIRE

    Zhao, Ke-Xin; Huang, Yan; Chen, Xi; Wang, Nan-Xi; Liu, Shuang-Jiang

    2010-01-01

    We identified a new regulator, PcaO, which is involved in regulation of the protocatechuate (PCA) branch of the β-ketoadipate pathway in Corynebacterium glutamicum. PcaO is an atypical large ATP-binding LuxR family (LAL)-type regulator and does not have a Walker A motif. A mutant of C. glutamicum in which pcaO was disrupted (RES167ΔpcaO) was unable to grow on PCA, and growth on PCA was restored by complementation with pcaO. Both an enzymatic assay of PCA 3,4-dioxygenase activity (encoded by p...

  8. The Application of Principal Component Analysis Using Fixed Eigenvectors to the Infrared Thermographic Inspection of the Space Shuttle Thermal Protection System

    Science.gov (United States)

    Cramer, K. Elliott; Winfree, William P.

    2006-01-01

    The Nondestructive Evaluation Sciences Branch at NASA s Langley Research Center has been actively involved in the development of thermographic inspection techniques for more than 15 years. Since the Space Shuttle Columbia accident, NASA has focused on the improvement of advanced NDE techniques for the Reinforced Carbon-Carbon (RCC) panels that comprise the orbiter s wing leading edge. Various nondestructive inspection techniques have been used in the examination of the RCC, but thermography has emerged as an effective inspection alternative to more traditional methods. Thermography is a non-contact inspection method as compared to ultrasonic techniques which typically require the use of a coupling medium between the transducer and material. Like radiographic techniques, thermography can be used to inspect large areas, but has the advantage of minimal safety concerns and the ability for single-sided measurements. Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. A typical implementation of PCA is when the eigenvectors are generated from the data set being analyzed. Although it is a powerful tool for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the good material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued when a fixed set of eigenvectors is used to process the thermal data from the RCC materials. These eigen vectors can be generated either from an analytic model of the thermal response of the material under examination, or from a large cross section of experimental data. This paper will provide the

  9. Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images.

    Science.gov (United States)

    Sanz-Requena, Roberto; Prats-Montalbán, José Manuel; Martí-Bonmatí, Luis; Alberich-Bayarri, Ángel; García-Martí, Gracián; Pérez, Rosario; Ferrer, Alberto

    2015-08-01

    To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate. © 2014 Wiley Periodicals, Inc.

  10. PCA-1/ALKBH3 contributes to pancreatic cancer by supporting apoptotic resistance and angiogenesis.

    Science.gov (United States)

    Yamato, Ichiro; Sho, Masayuki; Shimada, Keiji; Hotta, Kiyohiko; Ueda, Yuko; Yasuda, Satoshi; Shigi, Naoko; Konishi, Noboru; Tsujikawa, Kazutake; Nakajima, Yoshiyuki

    2012-09-15

    The PCA-1/ALKBH3 gene implicated in DNA repair is expressed in several human malignancies but its precise contributions to cancer remain mainly unknown. In this study, we have determined its functions and clinical importance in pancreatic cancer. PCA-1/ALKBH3 functions in proliferation, apoptosis and angiogenesis were evaluated in human pancreatic cancer cells in vitro and in vivo. Further, PCA-1/ALKBH3 expression in 116 patients with pancreatic cancer was evaluated by immunohistochemistry. siRNA-mediated silencing of PCA-1/ALKBH3 expression induced apoptosis and suppressed cell proliferation. Conversely, overexpression of PCA-1/ALKBH3 increased anchorage-independent growth and invasiveness. In addition, PCA-1/ALKBH3 silencing downregulated VEGF expression and inhibited angiogenesis in vivo. Furthermore, immunohistochemical analysis showed that PCA-1/ALKBH3 expression was abundant in pancreatic cancer tissues, where it correlated with advanced tumor status, pathological stage and VEGF intensity. Importantly, patients with low positivity of PCA-1/ALKBH3 expression had improved postoperative prognosis compared with those with high positivity. Our results establish PCA-1/ALKBH3 as important gene in pancreatic cancer with potential utility as a therapeutic target in this fatal disease.

  11. Contribution to the understanding of how principal component analysis-derived dietary patterns emerge from habitual data on food consumption.

    Science.gov (United States)

    Schwedhelm, Carolina; Iqbal, Khalid; Knüppel, Sven; Schwingshackl, Lukas; Boeing, Heiner

    2018-02-01

    Principal component analysis (PCA) is a widely used exploratory method in epidemiology to derive dietary patterns from habitual diet. Such dietary patterns seem to originate from intakes on multiple days and eating occasions. Therefore, analyzing food intake of study populations with different levels of food consumption can provide additional insights as to how habitual dietary patterns are formed. We analyzed the food intake data of German adults in terms of the relations among food groups from three 24-h dietary recalls (24hDRs) on the habitual, single-day, and main-meal levels, and investigated the contribution of each level to the formation of PCA-derived habitual dietary patterns. Three 24hDRs were collected in 2010-2012 from 816 adults for an European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam subcohort study. We identified PCA-derived habitual dietary patterns and compared cross-sectional food consumption data in terms of correlation (Spearman), consistency (intraclass correlation coefficient), and frequency of consumption across all days and main meals. Contribution to the formation of the dietary patterns was obtained through Spearman correlation of the dietary pattern scores. Among the meals, breakfast appeared to be the most consistent eating occasion within individuals. Dinner showed the strongest correlations with "Prudent" (Spearman correlation = 0.60), "Western" (Spearman correlation = 0.59), and "Traditional" (Spearman correlation = 0.60) dietary patterns identified on the habitual level, and lunch showed the strongest correlations with the "Cereals and legumes" (Spearman correlation = 0.60) habitual dietary pattern. Higher meal consistency was related to lower contributions to the formation of PCA-derived habitual dietary patterns. Absolute amounts of food consumption did not strongly conform to the habitual dietary patterns by meals, suggesting that these patterns are formed by complex combinations of variable food

  12. Identifying the Component Structure of Satisfaction Scales by Nonlinear Principal Components Analysis

    NARCIS (Netherlands)

    Manisera, M.; Kooij, A.J. van der; Dusseldorp, E.

    2010-01-01

    The component structure of 14 Likert-type items measuring different aspects of job satisfaction was investigated using nonlinear Principal Components Analysis (NLPCA). NLPCA allows for analyzing these items at an ordinal or interval level. The participants were 2066 workers from five types of social

  13. Columbia River Component Data Gap Analysis

    Energy Technology Data Exchange (ETDEWEB)

    L. C. Hulstrom

    2007-10-23

    This Data Gap Analysis report documents the results of a study conducted by Washington Closure Hanford (WCH) to compile and reivew the currently available surface water and sediment data for the Columbia River near and downstream of the Hanford Site. This Data Gap Analysis study was conducted to review the adequacy of the existing surface water and sediment data set from the Columbia River, with specific reference to the use of the data in future site characterization and screening level risk assessments.

  14. Using principal component analysis to understand the variability of PDS 456

    Science.gov (United States)

    Parker, M. L.; Reeves, J. N.; Matzeu, G. A.; Buisson, D. J. K.; Fabian, A. C.

    2018-02-01

    We present a spectral-variability analysis of the low-redshift quasar PDS 456 using principal component analysis. In the XMM-Newton data, we find a strong peak in the first principal component at the energy of the Fe absorption line from the highly blueshifted outflow. This indicates that the absorption feature is more variable than the continuum, and that it is responding to the continuum. We find qualitatively different behaviour in the Suzaku data, which is dominated by changes in the column density of neutral absorption. In this case, we find no evidence of the absorption produced by the highly ionized gas being correlated with this variability. Additionally, we perform simulations of the source variability, and demonstrate that PCA can trivially distinguish between outflow variability correlated, anticorrelated and un-correlated with the continuum flux. Here, the observed anticorrelation between the absorption line equivalent width and the continuum flux may be due to the ionization of the wind responding to the continuum. Finally, we compare our results with those found in the narrow-line Seyfert 1 IRAS 13224-3809. We find that the Fe K UFO feature is sharper and more prominent in PDS 456, but that it lacks the lower energy features from lighter elements found in IRAS 13224-3809, presumably due to differences in ionization.

  15. Introduction of an index for drought evaluation using principle components analysis

    Directory of Open Access Journals (Sweden)

    S. Farhangfar

    2016-05-01

    Full Text Available Isfahan province is located in the center of Iran and has arid and semi-arid climate. In recent years, water shortage has increased in this region and has affected crop production. Wheat is one of the most important crops of the province. In the present research, an index (DEI has been developed for drought evaluation using long term climatic data through application of principle components analysis (PCA. The counties of the province were classified and evaluated according to drought intensity. In addition to DEI for quantifying drought, Aridity index (AI was also calculated at different time scales in each county. The climatic and grain yield data were collected from the Iranian Meteorological Organization and Isfahan Agricultural Organization, respectively. In order to remove the positive effects of genetic improvement and progress in agronomic management on long-term wheat grain yield, double exponential smoothing technique was used. According to DEI, Isfahan, Shahreza, Golpaygan and Natanz had semi-arid climate and Ardestan, Khoorobiabanak, Kashan and Naein could be classified as arid, while according to AI studied counties had arid climate. AI had the greatest amount only in Golpaygan while DEI had the greatest value in Isfahan, Shahreza, Golpaygan, Kashan and Natanz. PCA results showed that maximum temperature (coefficient of 3.51 followed by mean wind speed (coefficient of 2.27 were the main climatic variable influencing counties weather. Calculated drought indices showed poor correlation with wheat yield, indicating that other meteorological indices should still be examined to capture wheat yield variability in this province.

  16. Principal component analysis of the Norwegian version of the quality of life in late-stage dementia scale.

    Science.gov (United States)

    Mjørud, Marit; Kirkevold, Marit; Røsvik, Janne; Engedal, Knut

    2014-01-01

    To investigate which factors the Quality of Life in Late-Stage Dementia (QUALID) scale holds when used among people with dementia (pwd) in nursing homes and to find out how the symptom load varies across the different severity levels of dementia. We included 661 pwd [mean age ± SD, 85.3 ± 8.6 years; 71.4% women]. The QUALID and the Clinical Dementia Rating (CDR) scale were applied. A principal component analysis (PCA) with varimax rotation and Kaiser normalization was applied to test the factor structure. Nonparametric analyses were applied to examine differences of symptom load across the three CDR groups. The mean QUALID score was 21.5 (±7.1), and the CDR scores of the three groups were 1 in 22.5%, 2 in 33.6% and 3 in 43.9%. The results of the statistical measures employed were the following: Crohnbach's α of QUALID, 0.74; Bartlett's test of sphericity, p Kaiser-Meyer-Olkin measure, 0.77. The PCA analysis resulted in three components accounting for 53% of the variance. The first component was 'tension' ('facial expression of discomfort', 'appears physically uncomfortable', 'verbalization suggests discomfort', 'being irritable and aggressive', 'appears calm', Crohnbach's α = 0.69), the second was 'well-being' ('smiles', 'enjoys eating', 'enjoys touching/being touched', 'enjoys social interaction', Crohnbach's α = 0.62) and the third was 'sadness' ('appears sad', 'cries', 'facial expression of discomfort', Crohnbach's α 0.65). The mean score on the components 'tension' and 'well-being' increased significantly with increasing severity levels of dementia. Three components of quality of life (qol) were identified. Qol decreased with increasing severity of dementia. © 2013 S. Karger AG, Basel.

  17. PCA3 and PCA3-Based Nomograms Improve Diagnostic Accuracy in Patients Undergoing First Prostate Biopsy

    Directory of Open Access Journals (Sweden)

    Virginie Vlaeminck-Guillem

    2013-08-01

    Full Text Available While now recognized as an aid to predict repeat prostate biopsy outcome, the urinary PCA3 (prostate cancer gene 3 test has also been recently advocated to predict initial biopsy results. The objective is to evaluate the performance of the PCA3 test in predicting results of initial prostate biopsies and to determine whether its incorporation into specific nomograms reinforces its diagnostic value. A prospective study included 601 consecutive patients addressed for initial prostate biopsy. The PCA3 test was performed before ≥12-core initial prostate biopsy, along with standard risk factor assessment. Diagnostic performance of the PCA3 test was evaluated. The three available nomograms (Hansen’s and Chun’s nomograms, as well as the updated Prostate Cancer Prevention Trial risk calculator; PCPT were applied to the cohort, and their predictive accuracies were assessed in terms of biopsy outcome: the presence of any prostate cancer (PCa and high-grade prostate cancer (HGPCa. The PCA3 score provided significant predictive accuracy. While the PCPT risk calculator appeared less accurate; both Chun’s and Hansen’s nomograms provided good calibration and high net benefit on decision curve analyses. When applying nomogram-derived PCa probability thresholds ≤30%, ≤6% of HGPCa would have been missed, while avoiding up to 48% of unnecessary biopsies. The urinary PCA3 test and PCA3-incorporating nomograms can be considered as reliable tools to aid in the initial biopsy decision.

  18. Projection and analysis of nuclear components

    International Nuclear Information System (INIS)

    Heeschen, U.

    1980-01-01

    The classification and the types of analysis carried out in pipings for quality control and safety of nuclear power plants, are presented. The operation and emergency conditions with emphasis of possible simplifications of calculations are described. (author/M.C.K.) [pt

  19. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    International Nuclear Information System (INIS)

    Na, Man Gyun; Oh, Seungrohk

    2002-01-01

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors

  20. Nonparametric inference in nonlinear principal components analysis : exploration and beyond

    NARCIS (Netherlands)

    Linting, Mariëlle

    2007-01-01

    In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),

  1. Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

    Science.gov (United States)

    Deepaisarn, S; Tar, P D; Thacker, N A; Seepujak, A; McMahon, A W

    2018-01-01

    Abstract Motivation Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. Results We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Availability and implementation Software is available via the open source image analysis system TINA Vision, www.tina-vision.net. Contact paul.tar@manchester.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29091994

  2. [Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

    Science.gov (United States)

    Li, Wu; Hu, Bing; Wang, Ming-wei

    2014-12-01

    In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species

  3. Blind source separation dependent component analysis

    CERN Document Server

    Xiang, Yong; Yang, Zuyuan

    2015-01-01

    This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.

  4. The biological knowledge discovery by PCCF measure and PCA-F projection.

    Science.gov (United States)

    Jia, Xingang; Zhu, Guanqun; Han, Qiuhong; Lu, Zuhong

    2017-01-01

    In the process of biological knowledge discovery, PCA is commonly used to complement the clustering analysis, but PCA typically gives the poor visualizations for most gene expression data sets. Here, we propose a PCCF measure, and use PCA-F to display clusters of PCCF, where PCCF and PCA-F are modeled from the modified cumulative probabilities of genes. From the analysis of simulated and experimental data sets, we demonstrate that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of PCCF, where we aim at such data sets that the expression values of genes are collected at different time points.

  5. Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.

    Science.gov (United States)

    Ginsburg, Shoshana B; Viswanath, Satish E; Bloch, B Nicolas; Rofsky, Neil M; Genega, Elizabeth M; Lenkinski, Robert E; Madabhushi, Anant

    2015-05-01

    To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization. Classifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively. PCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI. © 2014 Wiley Periodicals, Inc.

  6. Real Time Engineering Analysis Based on a Generative Component Implementation

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Klitgaard, Jens

    2007-01-01

    The present paper outlines the idea of a conceptual design tool with real time engineering analysis which can be used in the early conceptual design phase. The tool is based on a parametric approach using Generative Components with embedded structural analysis. Each of these components uses the g...

  7. Principal Component Analysis: Most Favourite Tool in Chemometrics

    Indian Academy of Sciences (India)

    GENERAL ARTICLE. Principal ... Chemometrics is a discipline that combines mathematics, statis- ... workers have used PCA for air quality monitoring [8]. ..... J S Verbeke, Handbook of Chemometrics and Qualimetrics, Elsevier, New York,.

  8. Evaluation and Comparison of the Principal Component Analysis ...

    African Journals Online (AJOL)

    2012-08-01

    Aug 1, 2012 ... Comparison evaluation of performance indicates that, with PCA,. 80% of good GTE ... power generation Systems, Trains, Marine ... distributed around the median. Before ... GTE data is nonlinear and the distribution of the data ...

  9. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho; Huang, Jianhua Z.; Hu, Jianhua

    2010-01-01

    with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated

  10. Brewing and volatiles analysis of three tea beers indicate a potential interaction between tea components and lager yeast.

    Science.gov (United States)

    Rong, Lei; Peng, Li-Juan; Ho, Chi-Tang; Yan, Shou-He; Meurens, Marc; Zhang, Zheng-Zhu; Li, Da-Xiang; Wan, Xiao-Chun; Bao, Guan-Hu; Gao, Xue-Ling; Ling, Tie-Jun

    2016-04-15

    Green tea, oolong tea and black tea were separately introduced to brew three kinds of tea beers. A model was designed to investigate the tea beer flavour character. Comparison of the volatiles between the sample of tea beer plus water mixture (TBW) and the sample of combination of tea infusion and normal beer (CTB) was accomplished by triangular sensory test and HS-SPME GC-MS analysis. The PCA of GC-MS data not only showed a significant difference between volatile features of each TBW and CTB group, but also suggested some key compounds to distinguish TBW from CTB. The results of GC-MS showed that the relative concentrations of many typical tea volatiles were significantly changed after the brewing process. More interestingly, the behaviour of yeast fermentation was influenced by tea components. A potential interaction between tea components and lager yeast could be suggested. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Liquid chromatography tandem mass spectrometry determination of chemical markers and principal component analysis of Vitex agnus-castus L. fruits (Verbenaceae) and derived food supplements.

    Science.gov (United States)

    Mari, Angela; Montoro, Paola; Pizza, Cosimo; Piacente, Sonia

    2012-11-01

    A validated analytical method for the quantitative determination of seven chemical markers occurring in a hydroalcoholic extract of Vitex agnus-castus fruits by liquid chromatography electrospray triple quadrupole tandem mass spectrometry (LC/ESI/(QqQ)MSMS) is reported. To carry out a comparative study, five commercial food supplements corresponding to hydroalcoholic extracts of V. agnus-castus fruits were analysed under the same chromatographic conditions of the crude extract. Principal component analysis (PCA), based only on the variation of the amount of the seven chemical markers, was applied in order to find similarities between the hydroalcoholic extract and the food supplements. A second PCA analysis was carried out considering the whole spectroscopic data deriving from liquid chromatography electrospray linear ion trap mass spectrometry (LC/ESI/(LIT)MS) analysis. High similarity between the two PCA was observed, showing the possibility to select one of these two approaches for future applications in the field of comparative analysis of food supplements and quality control procedures. Copyright © 2012 Elsevier B.V. All rights reserved.

  12. Application of EOF/PCA-based methods in the post-processing of GRACE derived water variations

    Science.gov (United States)

    Forootan, Ehsan; Kusche, Jürgen

    2010-05-01

    Two problems that users of monthly GRACE gravity field solutions face are 1) the presence of correlated noise in the Stokes coefficients that increases with harmonic degree and causes ‘striping', and 2) the fact that different physical signals are overlaid and difficult to separate from each other in the data. These problems are termed the signal-noise separation problem and the signal-signal separation problem. Methods that are based on principal component analysis and empirical orthogonal functions (PCA/EOF) have been frequently proposed to deal with these problems for GRACE. However, different strategies have been applied to different (spatial: global/regional, spectral: global/order-wise, geoid/equivalent water height) representations of the GRACE level 2 data products, leading to differing results and a general feeling that PCA/EOF-based methods are to be applied ‘with care'. In addition, it is known that conventional EOF/PCA methods force separated modes to be orthogonal, and that, on the other hand, to either EOFs or PCs an arbitrary orthogonal rotation can be applied. The aim of this paper is to provide a common theoretical framework and to study the application of PCA/EOF-based methods as a signal separation tool due to post-process GRACE data products. In order to investigate and illustrate the applicability of PCA/EOF-based methods, we have employed them on GRACE level 2 monthly solutions based on the Center for Space Research, University of Texas (CSR/UT) RL04 products and on the ITG-GRACE03 solutions from the University of Bonn, and on various representations of them. Our results show that EOF modes do reveal the dominating annual, semiannual and also long-periodic signals in the global water storage variations, but they also show how choosing different strategies changes the outcome and may lead to unexpected results.

  13. Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

    Directory of Open Access Journals (Sweden)

    Pinghua Huang

    2018-01-01

    Full Text Available Source discrimination of mine water plays an important role in guiding mine water prevention in mine water management. To accurately determine water inrush source from a mine in the Jiaozuo mining area, a Piper trilinear diagram based on hydrochemical experimental data of stratified underground water in the area was utilized to determine typical water samples. Additionally, principal component analysis (PCA was used for dimensionality reduction of conventional hydrochemical variables, after which mutually independent variables were extracted. The Piper-PCA-Fisher water inrush source recognition model was established by combining the Piper trilinear diagram and Fisher discrimination theory. Screened typical samples were used to conduct back-discriminate verification of the model. Results showed that 28 typical water samples in different aquifers were determined through the Piper trilinear diagram as a water sample set for training. Before PCA was carried out, the first five factors covered 98.92% of the information quantity of the original data and could effectively represent the data information of the original samples. During the one-by-one rediscrimination process of 28 groups of training samples using the Piper-PCA-Fisher water inrush source model, 100% correct discrimination rate was achieved. During the prediction and discrimination process of 13 samples, one water sample was misdiscriminated; hence, the correct prediscrimination rate was 92.3%. Compared with the traditional Fisher water source recognition model, the Piper-PCA-Fisher water source recognition model established in this study had higher accuracy in both rediscrimination and prediscrimination processes. Thus it had a strong ability to discriminate water inrush sources.

  14. Design of experiments and principal component analysis as approaches for enhancing performance of gas-diffusional air-breathing bilirubin oxidase cathode

    Science.gov (United States)

    Babanova, Sofia; Artyushkova, Kateryna; Ulyanova, Yevgenia; Singhal, Sameer; Atanassov, Plamen

    2014-01-01

    Two statistical methods, design of experiments (DOE) and principal component analysis (PCA) are employed to investigate and improve performance of air-breathing gas-diffusional enzymatic electrodes. DOE is utilized as a tool for systematic organization and evaluation of various factors affecting the performance of the composite system. Based on the results from the DOE, an improved cathode is constructed. The current density generated utilizing the improved cathode (755 ± 39 μA cm-2 at 0.3 V vs. Ag/AgCl) is 2-5 times higher than the highest current density previously achieved. Three major factors contributing to the cathode performance are identified: the amount of enzyme, the volume of phosphate buffer used to immobilize the enzyme, and the thickness of the gas-diffusion layer (GDL). PCA is applied as an independent confirmation tool to support conclusions made by DOE and to visualize the contribution of factors in individual cathode configurations.

  15. Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure

    Directory of Open Access Journals (Sweden)

    Shaukat S. Shahid

    2016-06-01

    Full Text Available In this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50 on the eigenvalues and eigenvectors resulting from principal component analysis (PCA. For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22 of a small data set comprising of 55 samples (stations from where water samples were collected. Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.

  16. A multivariate analysis of intrinsic soil components influencing the mean-weight diameter of water-stable aggregates

    International Nuclear Information System (INIS)

    Mbagwu, J.S.C.; Chukwu, W.I.E.

    1994-06-01

    A knowledge of the soil properties influencing the water-stability of soil aggregates is needed for selecting those more easily-determined properties that would be useful in areas where lack of facilities makes its direct determination impossible. In this laboratory study we evaluated the main soil physical, chemical and mineralogical properties influencing the stability of macro aggregates of some Italian surface soils in water. The objective is to select a subset of soil properties which predict optimally, soil aggregate stability. The index of stability used is the mean weight diameter of water-stable aggregates whereas the method of evaluation is the principal component analysis (PCA). The range in coefficients of variation (CV) among the properties was least in the physical (12.0-61.0%), medium in the mineralogical (28.0-116.2%) and highest in the chemical (8.2-110.8%) properties. The wider the range in CV in each subset of properties, the greater the number of components extracted by the PCA. The component defining variables, i.e. those with the highest loadings on each component and therefore, provide the best relationship between the variables and aggregate stability, revealed the ratio of total sand/clay and plastic limit as the significant physical properties. The significant chemical properties are Al 2 O 3 , FeO, MgO and MnO which contribute positively to aggregate stability. Feldspar, quartz and muscovite are the significant mineralogical properties each of which is negatively related to aggregate stability. These soil components are useful for developing empirical models for estimating the stability of aggregates of these soils in water. (author). 38 refs, 7 tabs

  17. Chemical fingerprinting of terpanes and steranes by chromatographic alignment and principal component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Christensen, J.H. [Royal Veterinary and Agricultural Univ., Thorvaldsensvej (Denmark). Dept. of Natural Sciences; Hansen, A.B. [National Environmental Research Inst., Roskilde (Denmark). Dept. of Environmental Chemistry and Microbiology; Andersen, O. [Roskilde Univ., Roskilde (Denmark). Dept. of Life Sciences and Chemistry

    2005-07-01

    Biomarkers such as steranes and terpanes are abundant in crude oils, particularly in heavy distillate petroleum products. They are useful for matching highly weathered oil samples when other groups of petroleum hydrocarbons fail to distinguish oil samples. In this study, time warping and principal component analysis (PCA) were applied for oil hydrocarbon fingerprinting based on relative amounts of terpane and sterane isomers analyzed by gas chromatography and mass spectrometry. The 4 principal components were boiling point range, clay content, marine or organic terrestrial matter, and maturity based on differences in the terpane and sterane isomer patterns. This study is an extension of a previous fingerprinting study for identifying the sources of oil spill samples based only on the profiles of sterane isomers. Spill samples from the Baltic Carrier oil spill were correctly identified by inspection of score plots. The interpretation of the loading and score plots offered further chemical information about correlations between changes in the amounts of sterane and terpane isomers. It was concluded that this method is an objective procedure for analyzing chromatograms with more comprehensive data usage compared to other fingerprinting methods. 20 refs., 4 figs.

  18. Chemical fingerprinting of terpanes and steranes by chromatographic alignment and principal component analysis

    International Nuclear Information System (INIS)

    Christensen, J.H.; Hansen, A.B.; Andersen, O.

    2005-01-01

    Biomarkers such as steranes and terpanes are abundant in crude oils, particularly in heavy distillate petroleum products. They are useful for matching highly weathered oil samples when other groups of petroleum hydrocarbons fail to distinguish oil samples. In this study, time warping and principal component analysis (PCA) were applied for oil hydrocarbon fingerprinting based on relative amounts of terpane and sterane isomers analyzed by gas chromatography and mass spectrometry. The 4 principal components were boiling point range, clay content, marine or organic terrestrial matter, and maturity based on differences in the terpane and sterane isomer patterns. This study is an extension of a previous fingerprinting study for identifying the sources of oil spill samples based only on the profiles of sterane isomers. Spill samples from the Baltic Carrier oil spill were correctly identified by inspection of score plots. The interpretation of the loading and score plots offered further chemical information about correlations between changes in the amounts of sterane and terpane isomers. It was concluded that this method is an objective procedure for analyzing chromatograms with more comprehensive data usage compared to other fingerprinting methods. 20 refs., 4 figs

  19. Problems of stress analysis of fuelling machine head components

    International Nuclear Information System (INIS)

    Mathur, D.D.

    1975-01-01

    The problem of stress analysis of fuelling machine head components are discussed. To fulfil the functional requirements, the components are required to have certain shapes where stress problems cannot be matched to a catalogue of pre-determined solutions. The areas where complex systems of loading due to hydrostatic pressure, weight, moments and temperature gradients coupled with the intricate shapes of the components make it difficult to arrive at satisfactory solutions. Particularly, the analysis requirements of the magazine housing, end cover, gravloc clamps and centre support are highlighted. An experimental stress analysis programme together with a theoretical finite element analysis is perhaps the answer. (author)

  20. Testing a Modified PCA-Based Sharpening Approach for Image Fusion

    Directory of Open Access Journals (Sweden)

    Jan Jelének

    2016-09-01

    Full Text Available Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER and high spatial-resolution panchromatic data (WorldView-2 for image fusion. A modified Principal Component Analysis (PCA-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1–PC4 can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used—PCA sharpening and Gram–Schmidt sharpening (GS, both available in ENVI software (Version 5.2 and lower as well as to the standard approach—sharpening Landsat 8 multispectral bands (MUL using its own panchromatic (PAN band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2 while keeping the proper albedo

  1. Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

    Science.gov (United States)

    Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

    2018-02-01

    Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

  2. A functional monomer is not enough: principal component analysis of the influence of template complexation in pre-polymerization mixtures on imprinted polymer recognition and morphology.

    Science.gov (United States)

    Golker, Kerstin; Karlsson, Björn C G; Rosengren, Annika M; Nicholls, Ian A

    2014-11-10

    In this report, principal component analysis (PCA) has been used to explore the influence of template complexation in the pre-polymerization phase on template molecularly imprinted polymer (MIP) recognition and polymer morphology. A series of 16 bupivacaine MIPs were studied. The ethylene glycol dimethacrylate (EGDMA)-crosslinked polymers had either methacrylic acid (MAA) or methyl methacrylate (MMA) as the functional monomer, and the stoichiometry between template, functional monomer and crosslinker was varied. The polymers were characterized using radioligand equilibrium binding experiments, gas sorption measurements, swelling studies and data extracted from molecular dynamics (MD) simulations of all-component pre-polymerization mixtures. The molar fraction of the functional monomer in the MAA-polymers contributed to describing both the binding, surface area and pore volume. Interestingly, weak positive correlations between the swelling behavior and the rebinding characteristics of the MAA-MIPs were exposed. Polymers prepared with MMA as a functional monomer and a polymer prepared with only EGDMA were found to share the same characteristics, such as poor rebinding capacities, as well as similar surface area and pore volume, independent of the molar fraction MMA used in synthesis. The use of PCA for interpreting relationships between MD-derived descriptions of events in the pre-polymerization mixture, recognition properties and morphologies of the corresponding polymers illustrates the potential of PCA as a tool for better understanding these complex materials and for their rational design.

  3. A Functional Monomer Is Not Enough: Principal Component Analysis of the Influence of Template Complexation in Pre-Polymerization Mixtures on Imprinted Polymer Recognition and Morphology

    Directory of Open Access Journals (Sweden)

    Kerstin Golker

    2014-11-01

    Full Text Available In this report, principal component analysis (PCA has been used to explore the influence of template complexation in the pre-polymerization phase on template molecularly imprinted polymer (MIP recognition and polymer morphology. A series of 16 bupivacaine MIPs were studied. The ethylene glycol dimethacrylate (EGDMA-crosslinked polymers had either methacrylic acid (MAA or methyl methacrylate (MMA as the functional monomer, and the stoichiometry between template, functional monomer and crosslinker was varied. The polymers were characterized using radioligand equilibrium binding experiments, gas sorption measurements, swelling studies and data extracted from molecular dynamics (MD simulations of all-component pre-polymerization mixtures. The molar fraction of the functional monomer in the MAA-polymers contributed to describing both the binding, surface area and pore volume. Interestingly, weak positive correlations between the swelling behavior and the rebinding characteristics of the MAA-MIPs were exposed. Polymers prepared with MMA as a functional monomer and a polymer prepared with only EGDMA were found to share the same characteristics, such as poor rebinding capacities, as well as similar surface area and pore volume, independent of the molar fraction MMA used in synthesis. The use of PCA for interpreting relationships between MD-derived descriptions of events in the pre-polymerization mixture, recognition properties and morphologies of the corresponding polymers illustrates the potential of PCA as a tool for better understanding these complex materials and for their rational design.

  4. Delusions in first-episode psychosis: Principal component analysis of twelve types of delusions and demographic and clinical correlates of resulting domains.

    Science.gov (United States)

    Paolini, Enrico; Moretti, Patrizia; Compton, Michael T

    2016-09-30

    Although delusions represent one of the core symptoms of psychotic disorders, it is remarkable that few studies have investigated distinct delusional themes. We analyzed data from a large sample of first-episode psychosis patients (n=245) to understand relations between delusion types and demographic and clinical correlates. First, we conducted a principal component analysis (PCA) of the 12 delusion items within the Scale for the Assessment of Positive Symptoms (SAPS). Then, using the domains derived via PCA, we tested a priori hypotheses and answered exploratory research questions related to delusional content. PCA revealed five distinct components: Delusions of Influence, Grandiose/Religious Delusions, Paranoid Delusions, Negative Affect Delusions (jealousy, and sin or guilt), and Somatic Delusions. The most prevalent type of delusion was Paranoid Delusions, and such delusions were more common at older ages at onset of psychosis. The level of Delusions of Influence was correlated with the severity of hallucinations and negative symptoms. We ascertained a general relationship between different childhood adversities and delusional themes, and a specific relationship between Somatic Delusions and childhood neglect. Moreover, we found higher scores on Delusions of Influence and Negative Affect Delusions among cannabis and stimulant users. Our results support considering delusions as varied experiences with varying prevalences and correlates. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Component reliability analysis for development of component reliability DB of Korean standard NPPs

    International Nuclear Information System (INIS)

    Choi, S. Y.; Han, S. H.; Kim, S. H.

    2002-01-01

    The reliability data of Korean NPP that reflects the plant specific characteristics is necessary for PSA and Risk Informed Application. We have performed a project to develop the component reliability DB and calculate the component reliability such as failure rate and unavailability. We have collected the component operation data and failure/repair data of Korean standard NPPs. We have analyzed failure data by developing a data analysis method which incorporates the domestic data situation. And then we have compared the reliability results with the generic data for the foreign NPPs

  6. Application of Principal Component Analysis in Assessment of Relation Between the Parameters of Technological Quality of Wheat Grains Treated with Inert Dusts Against Rice Weevil (Sitophilus oryzae L.

    Directory of Open Access Journals (Sweden)

    Marija Bodroža-Solarov

    2011-01-01

    Full Text Available Quality parameters of several wheat grain lots (low vitreous and high vitreous grains,non-infested and infested with rice weevils, (Sitophilus oryzae L. treated with inert dusts(natural zeolite, two diatomaceous earths originating from Serbia and a commercial productProtect-It® were investigated. Principal component analysis (PCA was used to investigatethe classification of treated grain lots and to assess how attributes of technological qualitycontribute to this classification. This research showed that vitreousness (0.95 and test weight(0.93 contributed most to the first principal component whereas extensigraph area (-0.76contributed to the second component. The determined accountability of the total variabilityby the first component was around 55%, while with the second it was 18%, which meansthat those two dimensions together account for around 70% of total variability of the observedset of variables. Principal component analysis (PCA of data set was able to distinguishamong the various treatments of wheat lots. It was revealed that inert dust treatments producedifferent effects depending on the degree of endosperm vitreousness.

  7. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  8. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Alessandra Caggiano

    2018-03-01

    Full Text Available Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA is proposed. PCA allowed to identify a smaller number of features (k = 2 features, the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax was achieved, with predicted values very close to the measured tool wear values.

  9. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

    Science.gov (United States)

    Caggiano, Alessandra

    2018-03-09

    Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

  10. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

    Science.gov (United States)

    2018-01-01

    Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

  11. A novel principal component analysis for spatially misaligned multivariate air pollution data.

    Science.gov (United States)

    Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

    2017-01-01

    We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

  12. Using principal component analysis for selecting network behavioral anomaly metrics

    Science.gov (United States)

    Gregorio-de Souza, Ian; Berk, Vincent; Barsamian, Alex

    2010-04-01

    This work addresses new approaches to behavioral analysis of networks and hosts for the purposes of security monitoring and anomaly detection. Most commonly used approaches simply implement anomaly detectors for one, or a few, simple metrics and those metrics can exhibit unacceptable false alarm rates. For instance, the anomaly score of network communication is defined as the reciprocal of the likelihood that a given host uses a particular protocol (or destination);this definition may result in an unrealistically high threshold for alerting to avoid being flooded by false positives. We demonstrate that selecting and adapting the metrics and thresholds, on a host-by-host or protocol-by-protocol basis can be done by established multivariate analyses such as PCA. We will show how to determine one or more metrics, for each network host, that records the highest available amount of information regarding the baseline behavior, and shows relevant deviances reliably. We describe the methodology used to pick from a large selection of available metrics, and illustrate a method for comparing the resulting classifiers. Using our approach we are able to reduce the resources required to properly identify misbehaving hosts, protocols, or networks, by dedicating system resources to only those metrics that actually matter in detecting network deviations.

  13. An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

    International Nuclear Information System (INIS)

    Azadeh, A.; Amalnick, M.S.; Ghaderi, S.F.; Asadzadeh, S.M.

    2007-01-01

    This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors

  14. PRINCIPAL COMPONENT ANALYSIS OF FACTORS DETERMINING PHOSPHATE ROCK DISSOLUTION ON ACID SOILS

    Directory of Open Access Journals (Sweden)

    Yusdar Hilman

    2016-10-01

    Full Text Available Many of the agricultural soils in Indonesia are acidic and low in both total and available phosphorus which severely limits their potential for crops production. These problems can be corrected by application of chemical fertilizers. However, these fertilizers are expensive, and cheaper alternatives such as phosphate rock (PR have been considered. Several soil factors may influence the dissolution of PR in soils, including both chemical and physical properties. The study aimed to identify PR dissolution factors and evaluate their relative magnitude. The experiment was conducted in Soil Chemical Laboratory, Universiti Putra Malaysia and Indonesian Center for Agricultural Land Resources Research and Development from January to April 2002. The principal component analysis (PCA was used to characterize acid soils in an incubation system into a number of factors that may affect PR dissolution. Three major factors selected were soil texture, soil acidity, and fertilization. Using the scores of individual factors as independent variables, stepwise regression analysis was performed to derive a PR dissolution function. The factors influencing PR dissolution in order of importance were soil texture, soil acidity, then fertilization. Soil texture factors including clay content and organic C, and soil acidity factor such as P retention capacity interacted positively with P dissolution and promoted PR dissolution effectively. Soil texture factors, such as sand and silt content, soil acidity factors such as pH, and exchangeable Ca decreased PR dissolution.

  15. A Fault Prognosis Strategy Based on Time-Delayed Digraph Model and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Ningyun Lu

    2012-01-01

    Full Text Available Because of the interlinking of process equipments in process industry, event information may propagate through the plant and affect a lot of downstream process variables. Specifying the causality and estimating the time delays among process variables are critically important for data-driven fault prognosis. They are not only helpful to find the root cause when a plant-wide disturbance occurs, but to reveal the evolution of an abnormal event propagating through the plant. This paper concerns with the information flow directionality and time-delay estimation problems in process industry and presents an information synchronization technique to assist fault prognosis. Time-delayed mutual information (TDMI is used for both causality analysis and time-delay estimation. To represent causality structure of high-dimensional process variables, a time-delayed signed digraph (TD-SDG model is developed. Then, a general fault prognosis strategy is developed based on the TD-SDG model and principle component analysis (PCA. The proposed method is applied to an air separation unit and has achieved satisfying results in predicting the frequently occurred “nitrogen-block” fault.

  16. Principal Component Analysis of Body Measurements In Three ...

    African Journals Online (AJOL)

    This study was conducted to explore the relationship among body measurements in 3 strains of broilers chicken (Arbor Acre, Marshal and Ross) using principal component analysis with the view of identifying those components that define body conformation in broilers. A total of 180 birds were used, 60 per strain.

  17. Tomato sorting using independent component analysis on spectral images

    NARCIS (Netherlands)

    Polder, G.; Heijden, van der G.W.A.M.; Young, I.T.

    2003-01-01

    Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components

  18. The Northern Norway Mother-and-Child Contaminant Cohort (MISA) Study: PCA analyses of environmental contaminants in maternal sera and dietary intake in early pregnancy.

    Science.gov (United States)

    Veyhe, Anna Sofía; Hofoss, Dag; Hansen, Solrunn; Thomassen, Yngvar; Sandanger, Torkjel M; Odland, Jon Øyvind; Nieboer, Evert

    2015-03-01

    Although predictors of contaminants in serum or whole blood are usually examined by chemical groups (e.g., POPs, toxic and/or essential elements; dietary sources), principal component analysis (PCA) permits consideration of both individual substances and combined variables. Our study had two primary objectives: (i) Characterize the sources and predictors of a suite of eight PCBs, four organochlorine (OC) pesticides, five essential and five toxic elements in serum and/or whole blood of pregnant women recruited as part of the Mother-and-Child Contaminant Cohort Study conducted in Northern Norway (The MISA study); and (ii) determine the influence of personal and social characteristics on both dietary and contaminant factors. Recruitment and sampling started in May 2007 and continued for the next 31 months until December 2009. Blood/serum samples were collected during the 2nd trimester (mean: 18.2 weeks, range 9.0-36.0). A validated questionnaire was administered to obtain personal information. The samples were analysed by established laboratories employing verified methods and reference standards. PCA involved Varimax rotation, and significant predictors (p≤0.05) in linear regression models were included in the multivariable linear regression analysis. When considering all the contaminants, three prominent PCA axes stood out with prominent loadings of: all POPs; arsenic, selenium and mercury; and cadmium and lead. Respectively, in the multivariate models the following were predictors: maternal age, parity and consumption of freshwater fish and land-based wild animals; marine fish; cigarette smoking, dietary PCA axes reflecting consumption of grains and cereals, and food items involving hunting. PCA of only the POPs separated them into two axes that, in terms of recently published findings, could be understood to reflect longitudinal trends and their relative contributions to summed POPs. The linear combinations of variables generated by PCA identified prominent

  19. Key components of financial-analysis education for clinical nurses.

    Science.gov (United States)

    Lim, Ji Young; Noh, Wonjung

    2015-09-01

    In this study, we identified key components of financial-analysis education for clinical nurses. We used a literature review, focus group discussions, and a content validity index survey to develop key components of financial-analysis education. First, a wide range of references were reviewed, and 55 financial-analysis education components were gathered. Second, two focus group discussions were performed; the participants were 11 nurses who had worked for more than 3 years in a hospital, and nine components were agreed upon. Third, 12 professionals, including professors, nurse executive, nurse managers, and an accountant, participated in the content validity index. Finally, six key components of financial-analysis education were selected. These key components were as follows: understanding the need for financial analysis, introduction to financial analysis, reading and implementing balance sheets, reading and implementing income statements, understanding the concepts of financial ratios, and interpretation and practice of financial ratio analysis. The results of this study will be used to develop an education program to increase financial-management competency among clinical nurses. © 2015 Wiley Publishing Asia Pty Ltd.

  20. Combination of 1H nuclear magnetic resonance spectroscopy and principal component analysis to evaluate the lipid fluidity of flutamide-encapsulated lipid nanoemulsions.

    Science.gov (United States)

    Takegami, Shigehiko; Ueyama, Keita; Konishi, Atsuko; Kitade, Tatsuya

    2018-06-06

    The lipid fluidity of various lipid nanoemulsions (LNEs) without and with flutamide (FT) and containing one of two neutral lipids, one of four phosphatidylcholines as a surfactant, and sodium palmitate as a cosurfactant was investigated by the combination of 1 H nuclear magnetic resonance (NMR) spectroscopy and principal component analysis (PCA). In the 1 H NMR spectra, the peaks from the methylene groups of the neutral lipids and surfactants for all LNE preparations showed downfield shifts with increasing temperature from 20 to 60 °C. PCA was applied to the 1 H NMR spectral data obtained for the LNEs. The PCA resulted in a model in which the first two principal components (PCs) extracted 88% of the total spectral variation; the first PC (PC-1) axis and second PC (PC-2) axis accounted for 73 and 15%, respectively, of the total spectral variation. The Score-1 values for PC-1 plotted against temperature revealed the existence of two clusters, which were defined by the neutral lipid of the LNE preparations. Meanwhile, the Score-2 values decreased with rising temperature and reflected the increase in lipid fluidity of each LNE preparation, consistent with fluorescence anisotropy measurements. In addition, the changes of Score-2 values with temperature for LNE preparations with FT were smaller than those for LNE preparations without FT. This indicates that FT encapsulated in LNE particles markedly suppressed the increase in lipid fluidity of LNE particles with rising temperature. Thus, PCA of 1 H NMR spectra will become a powerful tool to analyze the lipid fluidity of lipid nanoparticles. Graphical abstract ᅟ.