Skocz do pozycji: 1.

Tytuł:

Principal Component Analysis versus Factor Analysis

Autorzy:

Gniazdowski, Zenon

Powiązania:

https://bibliotekanauki.pl/articles/1790041.pdf

Data publikacji:

2021-09

Wydawca:

Warszawska Wyższa Szkoła Informatyki

Tematy:

principal component analysis
factor analysis
number of principal components
number of factors
determining number of principal components
determining number of factors

Opis:

The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors in FA was discussed in detail. A new criterion for determining the number of factors and principal components is discussed, which will allow to present most of the variance of each of the analyzed primary variables. An efficient algorithm for determining the number of factors in FA, which complies with this criterion, was also proposed. This algorithm was adapted to find the number of principal components in PCA. It was also proposed to modify the PCA algorithm using a new method of determining the number of principal components. The obtained results were discussed.

Źródło:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2021, 15, 24; 35--88
1896-396X
2082-8349

Pojawia się w:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 2.

Tytuł:

Quantum image classification using principal component analysis

Autorzy:

Ostaszewski, M.
Sadowski, P.
Gawron, P.

Powiązania:

https://bibliotekanauki.pl/articles/375706.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

some quantum algorithms
quantum image processing
principal component analysis

Opis:

We present a novel quantum algorithm for the classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.

Źródło:

Theoretical and Applied Informatics; 2015, 27, 1; 1-12
1896-5334

Pojawia się w:

Theoretical and Applied Informatics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

Tytuł:

Nonlinear principal component analysis
Nieliniowe składowe główne

Autorzy:

Deręgowski, K.
Krzyśko, M.

Powiązania:

https://bibliotekanauki.pl/articles/9665.pdf

Data publikacji:

2010

Wydawca:

Uniwersytet Przyrodniczy w Lublinie. Katedra Zastosowań Matematyki i Informatyki

Źródło:

Colloquium Biometricum; 2010, 40
1896-7701

Pojawia się w:

Colloquium Biometricum

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 4.

Tytuł:

A principal component analysis in concrete design

Autorzy:

Kobaka, Janusz
Katzer, Jacek

Powiązania:

https://bibliotekanauki.pl/articles/31342619.pdf

Data publikacji:

2022

Wydawca:

Politechnika Częstochowska

Tematy:

principal component analysis
PCA
concrete designing
concrete mix
analiza głównych składowych
projektowanie betonu
mieszanka betonowa

Opis:

Over the last 200 years, ordinary concrete has evolved from four basic ingredient materials (gravel, sand, cement, and water) to multicomponent complex composites. The number and variety of the additives, admixtures, non-conventional aggregates, fillers, and fibres currently used for concrete production have continued to grow rapidly. Regrettably, the methods for de-signing concrete mixes have not evolved at a similarly fast pace. Keeping the above facts in mind, the authors utilised a principal component analysis (PCA) to design modern concrete mixes. As an initial approach, 550 cast and tested concrete mixes were analysed. The main aim of the presented study was to prove the usefulness of the PCA methodology for the fast classification of concrete mix compositions. The acquired knowledge should be useful for the effective design of multicomponent modern concrete mixes.

Źródło:

Budownictwo o Zoptymalizowanym Potencjale Energetycznym; 2022, 11; 203-219
2299-8535
2544-963X

Pojawia się w:

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 5.

Tytuł:

Processing of thermographic sequence using Principal Component Analysis

Autorzy:

Świta, R.
Suszyński, Z.

Powiązania:

https://bibliotekanauki.pl/articles/114604.pdf

Data publikacji:

2015

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

filtering
correlation
PCA
segmentation
thermal sequences

Opis:

This paper describes application of the principal component analysis in relation to the thermal imaging contrast sequences, recorded with pulse excitation for three different objects. The aim of the study was to demonstrate that thermographic sequences contain an excessive number of data-distorting information about the characteristics of an object and that it is possible to reduce them. It has been shown that PCA can improve SNR, simplifies separation of areas with distinct features and allows determining their count, which is important, inter alia, with infrared image segmentation. The study shows examples of the results for a sequence of infrared registered for thin-layer-chromatography plate (SiO2 on glass) with separated analytes, the high power Si-eutectic-Mo thyristor structure with defects in eutectic, and the Al disk with cavities of different diameter and depth.

Źródło:

Measurement Automation Monitoring; 2015, 61, 6; 215-218
2450-2855

Pojawia się w:

Measurement Automation Monitoring

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 6.

Tytuł:

A Kernel Version of Functional Principal Component Analysis

Autorzy:

Górecki, Tomasz
Krzyśko, Mirosław

Powiązania:

https://bibliotekanauki.pl/articles/465977.pdf

Data publikacji:

2012

Wydawca:

Główny Urząd Statystyczny

Tematy:

PCA
FPCA
kernel version of FPCA

Opis:

In this paper a new construction of functional principal components (FPCA) is proposed, based on principal components for vector data. A kernel version of FPCA is also presented. The quality of the two described methods was tested on 20 different data sets.

Źródło:

Statistics in Transition new series; 2012, 13, 3; 559-668
1234-7655

Pojawia się w:

Statistics in Transition new series

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 7.

Tytuł:

Fault detection and isolation with robust principal component analysis

Autorzy:

Tharrault, Y.
Mourot, G.
Ragot, J.
Maquin, D.

Powiązania:

https://bibliotekanauki.pl/articles/929927.pdf

Data publikacji:

2008

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

analiza głównych składowych
odporność
detekcja uszkodzeń
lokalizacja uszkodzeń
principal component analysis
robustness
outliers
fault detection
fault isolation
structured residual vector
variable reconstruction

Opis:

Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a 'local' matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 429-442
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 8.

Tytuł:

Chatter detection using principal component analysis in cold rolling mill

Autorzy:

Usmani, N. I.
Kumar, S.
Velisatti, S.
Tiwari, P. K.
Mishra, S. K.
Patnaik, U. S.

Powiązania:

https://bibliotekanauki.pl/articles/329460.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN

Tematy:

chatter
principal component analysis
PCA
cold rolling
vibration
drgania samowzbudne
analiza składowych głównych
walcowanie na zimno
drgania

Opis:

Most cold rolling mills are prone to chatter problem. Chatter marks are often observed on the strip surface in cold rolling mill leading to downgrade and rejection of rolled material. Chatter impact product quality as well as productivity of mill. In absence of online chatter detection no corrective action can be taken immediately and whole campaign gets affected. Most conventional approach for online chatter detection is by using vibration measurement of mill stands in time & frequency domain. Present work proposes two approaches to detect chatter in cold rolling mill using a statistical technique called Principal Component Analysis (PCA). In this paper two methods are used for chatter detection. First method applies PCA on Fast Fourier Transform (FFT) to differentiate between chatter and non-chatter condition. Second method applies PCA on statistical parameters calculated from raw vibration data to detect chatter.

Źródło:

Diagnostyka; 2018, 19, 1; 73-81
1641-6414
2449-5220

Pojawia się w:

Diagnostyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 9.

Tytuł:

Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering

Autorzy:

Ślot, K.
Adamiak, K.
Duch, P.
Żurek, D.

Powiązania:

https://bibliotekanauki.pl/articles/308598.pdf

Data publikacji:

2015

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

feature selection
kernel methods
pattern classification

Opis:

The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples. Several criteria that drive feature selection process are introduced and their performance is assessed and compared against the reference approach, which is a combination of kPCA and most expressive feature reordering based on the Fisher linear discriminant criterion. It has been shown that some of the proposed modifications result in generating feature spaces with noticeably better (at the level of approximately 4%) class discrimination properties.

Źródło:

Journal of Telecommunications and Information Technology; 2015, 2; 3-10
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

Tytuł:

Analiza czynnikowa zdjęć wielospektralnych
Principal component analysis of multispectral images

Autorzy:

Czapski, P.
Kotlarz, J.
Kubiak, K.
Tkaczyk, M.

Powiązania:

https://bibliotekanauki.pl/articles/213759.pdf

Data publikacji:

2014

Wydawca:

Sieć Badawcza Łukasiewicz - Instytut Lotnictwa

Tematy:

PCA
metody statystyczne
bioróżnorodność
krzywe blasku
redukcja danych
statistical methods
biodiversity
light curves
data reduction

Opis:

Analiza zdjęć wielospektralnych sprowadza się często do modelowania matematycznego opartego o wielowymiarowe przestrzenie metryczne, w których umieszcza się pozyskane za pomocą sensorów dane. Tego typu bardzo intuicyjne, łatwe do zaaplikowania w algorytmice analizy obrazu postępowanie może skutkować zupełnie niepotrzebnym wzrostem niezbędnej do analiz zdjęć mocy obliczeniowej. Jedną z ogólnie przyjętych grup metod analizy zbiorów danych tego typu są metody analizy czynnikowej. Wpracy tej prezentujemy dwie z nich: Principal Component Analysis (PCA) oraz Simplex Shrink-Wrapping (SSW). Użyte jednocześnie obniżają znacząco wymiar zadanej przestrzeni metrycznej pozwalając odnaleźć w danych wielospektralnych charakterystyczne składowe, czyli przeprowadzić cały proces detekcji fotografowanych obiektów. W roku 2014 w Pracowni Przetwarzania Danych Instytutu Lotnictwa oraz Zakładzie Ochrony Lasu Instytutu Badawczego Leśnictwa metodykę tą równie skutecznie przyjęto dla analizy dwóch niezwykle różnych serii zdjęć wielospektralnych: detekcji głównych składowych powierzchni Marsa (na podstawie zdjęć wielospektralnych pozyskanych w ramach misji EPOXI, NASA) oraz oszacowania bioróżnorodności jednej z leśnych powierzchni badawczych projektu HESOFF.
Mostly, analysis of multispectral images employs mathematical modeling based on multidimensional metric spaces that includes collected by the sensors data. Such an intuitive approach easily applicable to image analysis applications can result in unnecessary computing power increase required by this analysis. One of the groups of generally accepted methods of analysis of data sets are factor analysis methods. Two such factor analysis methods are presented in this paper, i.e. Principal Component Analysis (PCA ) and Simplex Shrink - Wrapping (SSW). If they are used together dimensions of a metric space can be reduced significantly allowing characteristic components to be found in multispectral data, i.e. to carry out the whole detection process of investigated images. In 2014 such methodology was adopted by Data Processing Department of the Institute of Aviation and Division of Forest Protection of Forest Research Institute for the analysis of the two very different series of multispectral images: detection of major components of the Mars surface (based on multispectral images obtained from the epoxy mission, NASA) and biodiversity estimation of one of the investigated in the HESOFF project forest complexes.

Źródło:

Prace Instytutu Lotnictwa; 2014, 1 (234) March 2014; 143-150
0509-6669
2300-5408

Pojawia się w:

Prace Instytutu Lotnictwa

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 11.

Tytuł:

Wavelength-sensitive-function-based spectral reconstruction using segmented principal component analysis

Autorzy:

Wu, G.
Shen, X.
Liu, Z.
Zhang, J.

Powiązania:

https://bibliotekanauki.pl/articles/174199.pdf

Data publikacji:

2016

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

spectral reconstruction
wavelength-sensitive function
segmented principal component analysis

Opis:

Spectral images provide richer information than colorimetric images. A high-dimensional spectral data presents a challenge for efficient spectral reconstruction. In conventional reconstruction methods it is very difficult to obtain good spectral and colorimetric accuracy simultaneously. In this paper, a segmented principal component analysis (SPCA) method and a weighted segmented principal component analysis (wSPCA) method are proposed for efficient reconstruction of spectral color information. The methods require, firstly, partitioning the complete spectrum of wavelengths into two subgroups, considering the sensitivity of human visual system. Then the classical principal component analysis (PCA) carried out each subgroup of data separately. The results indicated that the spectral and colorimetric accuracy of the SPCA and wSPCA outperformed the PCA and weighted PCA, and wSPCA clearly retained more color visual information.

Źródło:

Optica Applicata; 2016, 46, 3; 365-374
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

Tytuł:

Principal Component Analysis and Cluster Analysis In Multivariate Assessment of Water Quality

Autorzy:

Jankowska, J.
Radzka, E.
Rymuza, K.

Powiązania:

https://bibliotekanauki.pl/articles/124319.pdf

Data publikacji:

2017

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

principal component analysis
water quality
cluster analysis

Opis:

This paper deals with the use of multivariate methods in drinking water analysis. During a five-year project, from 2008 to 2012, selected chemical parameters in 11 water supply networks of the Siedlce County were studied. Throughout that period drinking water was of satisfactory quality, with only iron and manganese ions exceeding the limits (21 times and 12 times, respectively). In accordance with the results of cluster analysis, all water networks were put into three groups of different water quality. A high concentration of chlorides, sulphates, and manganese and a low concentration of copper and sodium was found in the water of Group 1 supply networks. The water in Group 2 had a high concentration of copper and sodium, and a low concentration of iron and sulphates. The water from Group 3 had a low concentration of chlorides and manganese, but a high concentration of fluorides. Using principal component analysis and cluster analysis, multivariate correlation between the studied parameters was determined, helping to put water supply networks into groups according to similar water quality.

Źródło:

Journal of Ecological Engineering; 2017, 18, 2; 92-96
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 13.

Tytuł:

Wavelets and principal component analysis method for vibration monitoring of rotating machinery

Autorzy:

Bendjama, H.
Boucherit, M. S.

Powiązania:

https://bibliotekanauki.pl/articles/949212.pdf

Data publikacji:

2016

Wydawca:

Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej

Tematy:

vibration
fault diagnosis
wavelet analysis
principal component analysis, squared
prediction error

Opis:

Fault diagnosis is playing today a crucial role in industrial systems. To improve reliability, safety and efficiency advanced monitoring methods have become increasingly important for many systems. The vibration analysis method is essential in improving condition monitoring and fault diagnosis of rotating machinery. Effective utilization of vibration signals depends upon effectiveness of applied signal processing techniques. In this paper, fault diagnosis is performed using a combination between Wavelet Transform (WT) and Principal Component Analysis (PCA). The WT is employed to decompose the vibration signal of measurements data in different frequency bands. The obtained decomposition levels are used as the input to the PCA method for fault identification using, respectively, the Q-statistic, also called Squared Prediction Error (SPE) and the Q-contribution. Clearly, useful information about the fault can be contained in some levels of wavelet decomposition. For this purpose, the Q-contribution is used as an evaluation criterion to select the optimal level, which contains the maximum information.Associated to spectral analysis and envelope analysis, it allows clear visualization of fault frequencies. The objective of this method is to obtain the information contained in the measured data. The monitoring results using real sensor measurements from a pilot scale are presented and discussed.

Źródło:

Journal of Theoretical and Applied Mechanics; 2016, 54, 2; 659-670
1429-2955

Pojawia się w:

Journal of Theoretical and Applied Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 14.

Tytuł:

Magnitude Modelling of HRTF Using Principal Component Analysis Applied to Complex Values

Autorzy:

Ramos, O. A.
Tommasini, F. C.

Powiązania:

https://bibliotekanauki.pl/articles/177682.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

head related transfer functions
HRTF
principal components analysis
PCA
binaural audition
auditory perception

Opis:

Principal components analysis (PCA) is frequently used for modelling the magnitude of the head- related transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between −45◦ and +90◦.

Źródło:

Archives of Acoustics; 2014, 39, 4; 477-482
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 15.

Tytuł:

Parametric estimation of water retention using mGMDH method and principal component analysis

Autorzy:

Neyshaburi, M.R.
Bayat, H.
Rastgou, M.
Mohammadi, K.
Gregory, A.S.
Nariman-Zadeh, N.

Powiązania:

https://bibliotekanauki.pl/articles/905465.pdf

Data publikacji:

2016

Wydawca:

Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej

Opis:

Performing a primary analysis, such as principal component analysis (PCA) may increase accuracy and reliability of developed pedotransfer functions (PTFs). This study focuses on the usefulness of the soil penetration resistance (PR) and principal components (PCs) as new inputs along with the others to develop the PTFs for estimating the soil water retention curve (SWRC) using a multi-objective group method of data handling (mGMDH). The Brooks and Corey (1964) SWRC model was used to give a description of the water retention curves and its parameters were determined from experimental SWRC data. To select eight PCs, PCA was applied to all measured or calculated variables. Penetration resistance, organic matter (OM), aggregates mean weight diameter (MWD), saturated hydraulic conductivity (Ks), macro porosity (Mp), micro porosity (Mip) and eight selected PCs were used as predictors to estimate the Brooks and Corey model parameters by mGMDH. Using PR or OM, Ks and MWD, improved the estimation of SWRC in some cases. Using the predicted PR can be useful in the estimation of SWRC. Using either the MP and Mip or the eight PCs significantly improved the PTFs accuracy and reliability. It would be very useful to apply PCA on the original variables as a primary analysis to develop parametric PTFs.

Źródło:

Polish Journal of Soil Science; 2016, 49, 1
0079-2985

Pojawia się w:

Polish Journal of Soil Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 16.

Tytuł:

Using principal component analysis and canonical discriminant analysis for multibeam seafloor characterisation data

Autorzy:

Łubniewski, Z.
Stepnowski, A.

Powiązania:

https://bibliotekanauki.pl/articles/331872.pdf

Data publikacji:

2012

Wydawca:

Polskie Towarzystwo Akustyczne

Opis:

The paper presents the seafloor characterisation based on multibeam sonar data. It relies on using the integrated model and description of three types of multibeam data obtained during seafloor sensing: 1) the grey-level sonar images (echograms) of seabed, 2) the 3D model of the seabed surface which consists of bathymetric data, 3) the set of time domain bottom echo envelopes received in the consecutive sonar beams. The classification is performed by utilisation of several statistical methods applied for analysis of a set of seafloor descriptors derived from multibeam data. In the paper, the use of Principal Component Analysis (PCA), as well as Canonical Discriminant Analysis (CDA) for reduction of the seafloor parameter space dimension is presented along with the obtained results. In addition, the use of the open source World Wind Java SDK tool for implementation of imaging and mapping of seafloor multibeam data, integrated with other elements of a scene and overlaid on rich background data, is also shown.

Źródło:

Hydroacoustics; 2012, 15; 123-130
1642-1817

Pojawia się w:

Hydroacoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

On a generalization of the principal component analysis
O uogólnieniu analizy głównych składowych

Autorzy:

Jajuga, Krzysztof

Powiązania:

https://bibliotekanauki.pl/articles/906716.pdf

Data publikacji:

1987

Wydawca:

Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego

Źródło:

Acta Universitatis Lodziensis. Folia Oeconomica; 1987, 68
0208-6018
2353-7663

Pojawia się w:

Acta Universitatis Lodziensis. Folia Oeconomica

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

Land clayey deposits compressibility investigation using principal component analysis and multiple regression tools

Autorzy:

Berrah, Yacine
Chegrouche, Aymen
Brahmi, Serhane
Boumezbeur, Abderrahmane

Powiązania:

https://bibliotekanauki.pl/articles/2201674.pdf

Data publikacji:

2022

Wydawca:

Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie

Tematy:

compressibility index
geotechnical parameters
principal component analysis
PCA
multiple regression models
indeks ściśliwości
parametry geotechniczne
analiza głównych składowych
regresja wielokrotna

Opis:

The settlement and compressibility magnitude of the major clayey and marly sediments in Tebessa area (N-E of Algeria) depends on several geotechnical parameters such as compression Cc and recompression Cs indices. The aim of this study was to investigate the parameters related to soil compressibility through tools of statistical analysis, which save time in comparison to multiply repeated laboratory tests. The study also adopted the principal component analysis (PCA) method to eliminate a number of uncorrelated variables that have no influence on the compressibility magnitude, or their impact is insignificant. The highest mean correlation coefficients were obtained for different contributing parameters. Multiple regression analysis has been performed to obtain the best fit model of the output Cc parameter taking into account the best correlation by adding parameters as regressors to reach the highest coefficient of regression R2 . The final obtained model of the present case study gives the best fit model with R2 of 0.92 which is a better value compared to different published models in the literature (R2 of 0.7 as maximum). The chosen input parameters using PCA combined with multiple regression analysis allow identifying the most important input parameters that noticeably affect the soil compression index, and provide with the best model for estimating the Cc index.

Źródło:

Geomatics, Landmanagement and Landscape; 2022, 4; 95--107
2300-1496

Pojawia się w:

Geomatics, Landmanagement and Landscape

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

Tytuł:

Classification of the indoor environment of a mobile robot using principal component analysis

Autorzy:

Yaqub, T.
Katupitya, J.

Powiązania:

https://bibliotekanauki.pl/articles/384275.pdf

Data publikacji:

2008

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

mobile robot environment
PCA
classification
feature extraction
training data
bootstrap method

Opis:

Large indoor environments of a mobile robot usually consist of different types of areas connected together. The structure of a corridor differs from a room, a main hall or laboratory. A method for online classification of these areas using a laser scanner is presented in this paper. This classification can reduce the search space of localization module to a great extent making the navigation system efficient. The intention was to make the classification of a sensor observation in a fast and real-time fashion and immediately on its arrival in the sensor frame. Our approach combines both the feature based and statistical approaches. We extract some vital features of lines and corners with attributes such as average length of lines and distance between corners from the raw laser data and classify the observation based on these features. Bootstrap method is used to get a robust correlation of features from training data and finally Principal Component Analysis (PCA) is used to model the environment. In PCA, the underlying assumption is that data is coming from a multivariate normal distribution. The use of bootstrap method makes it possible to use the observations data set which set, which is not necessarily normally distributed. This technique lifts up the normality assumption and reduces the computational cost further as compared to the PCA techniques based on raw sensor data and can be easily implemented in moderately complex indoor environment. The knowledge of the environment can also be up-dated in an adaptive fashion. Results of experimentation in a simulated hospital building under varying environmental conditions using a real-time robotic software Player/Stage are shown.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 2; 44-53
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 20.

Tytuł:

Principal Component Analysis of Egg Quality Characteristics of Isa Brown Layer Chickens in Nigeria

Autorzy:

Ukwu, H. O.
Abari, P. O.
Kuusu, D. J.

Powiązania:

https://bibliotekanauki.pl/articles/1178566.pdf

Data publikacji:

2017

Wydawca:

Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN

Tematy:

Correlation
Egg
Isa brown
Principal component
Quality

Opis:

This study was designed to provide an objective description of egg quality of Isa brown layer chickens in Nigeria. 104 eggs were used for the study. The eggs were initially weighed individually using a sensitive electronic weighing balance with accuracy of 0.001g. Data were collected on egg weight, egg length, egg width, oblong circumference, egg shell weight, yolk height, albumen height, albumen length, Haugh unit, albumen index and egg shell thickness. Data were subjected to principal component analysis. Egg quality traits had three principal components (factors) that contributed 85.805% of the total variability of the original eleven egg characteristics tested. The three principal components had Eigen values of 4.73 (PC1), 3.656 (PC2) and 1.069 (PC3). The first factor (PC1) accounted for 42.84% of the total variance, the second factor (PC2) accounted for 33.24% of the total variance, while the third factor (PC3) accounted for 9.72% of the total variance. The moderate to large communalities (0.583 – 0.944) observed indicate that a large number of variance has been accounted for by the factor solution. The present principal component analysis provided a means for objective description of the interdependence in the original eleven egg quality characteristics of Isa Brown layer chickens.

Źródło:

World Scientific News; 2017, 70, 2; 304-311
2392-2192

Pojawia się w:

World Scientific News

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

Application of principal component analysis for optimization of a system operation assessment criteria set

Autorzy:

Muślewski, Ł.
Knopik, L.

Powiązania:

https://bibliotekanauki.pl/articles/247903.pdf

Data publikacji:

2013

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

quality
criteria
transport system
analysis
canonical correlations
optimization

Opis:

The analysis of results of experimental tests and a literature survey of the issue reveal that the subject matter connected with determination of the optimal number of a given transportation system operation assessment criteria has a direct influence on the result of the considered assessment. The analysis was made within the research on operation quality of a selected transportation system. In order to optimize the analysed system assessment criteria, a theoretical description has been made and an example of canonical correlations application has been presented. Analysis of canonical correlations involves determination of linear combination parameters of the studied sets so that the obtained correlation coefficient will have maximal value. In the successive step, the next pair of linear combination are not correlated with the combinations determined in the first step. The determined correlations can measure the power of relation between two sets of variables and are useful in the process of choosing significant criteria for a given research object operation quality assessment.

Źródło:

Journal of KONES; 2013, 20, 4; 307-312
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

Application of principal component analysis for optimization of a system operation assessment criteria set

Autorzy:

Muślewski, Ł.
Knopik, L.

Powiązania:

https://bibliotekanauki.pl/articles/242963.pdf

Data publikacji:

2012

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

quality
criteria
transport system
analysis of main components
optimization

Opis:

The analysis of results of experimental tests and a literature survey on the issue reveal that the subject matter connected with determination of the optimal number of a given transportation system operation assessment criteria has a direct influence on the result of the considered assessment. Whole study has been made on the basis of the analysis of data obtained from tests performed in a real municipal transportation system, providing transport tasks in a 400 thousand urban agglomeration. On the basis of the tests, there was determined a set of sixteen criteria for the system operation assessment, depending on preferences of drivers, passengers, workers of the system providing vehicles with serviceability and the last group of results is a final assessment. The research involved tests of significance for correlation coefficients between the assessment criteria. The method of main factors involving analysis of a linear transformation of existing vector of variables X into Y has been used to study the task dimensioning. Proper values, providing basis for determination of a new space correspond to coordinates of the newly created vector Y. On this basis, the dimension of the criteria and their significance space has been concluded. The determined criteria have been accepted for the development of a resultant model for assessment of a transportation system operation quality.

Źródło:

Journal of KONES; 2012, 19, 4; 481-486
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

Tytuł:

The application of principal component analysis (pca) for the study of the spanish tourist demand

Autorzy:

González, María Jesús González
Pascual, María-Eva Vallejo

Powiązania:

https://bibliotekanauki.pl/articles/1051397.pdf

Data publikacji:

2018-12-30

Wydawca:

Uniwersytet im. Adama Mickiewicza w Poznaniu

Tematy:

Spain
autonomous communities
tourist demand
principal component analysis

Opis:

The objective of this study is the characterisation of the Spanish autonomous communities as tourist destinations for Spanish trips, based on the activities carried out, using the principal component method. The Spanish tourist is not only motivated by the sun and beach. This paper aims to clarify how Spanish people consider other tourist destinations. We contrast how frequently other types of tourism are valued when choosing their destination within the Spanish geography. Inland tourism, sports tourism, entertainment as well as gastronomy are becoming increasingly important.

Źródło:

Quaestiones Geographicae; 2018, 37, 4; 43-52
0137-477X
2081-6383

Pojawia się w:

Quaestiones Geographicae

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 24.

Tytuł:

Forcing variables in simulation of transpiration of water stressed plants determined by principal component analysis

Autorzy:

Durigon, A.
de Jong van Lier, Q.
Metselaar, K.

Powiązania:

https://bibliotekanauki.pl/articles/24101.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Instytut Agrofizyki PAN

Opis:

To date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Ags model was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.

Źródło:

International Agrophysics; 2016, 30, 4
0236-8722

Pojawia się w:

International Agrophysics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 25.

Tytuł:

Data compression by Principal Component Analysis (PCA) in modelling of soil density parameters based on soil granulation

Autorzy:

Sulewska, M. J.
Zabielska-Adamska, K.

Powiązania:

https://bibliotekanauki.pl/articles/2060294.pdf

Data publikacji:

2015

Wydawca:

Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy

Tematy:

Artificial Neural Networks
principal component analysis
compaction parameters
minimum and maximum dry density of solid particles
graining parameters

Opis:

The parameter for the density specification of naturally compacted non-cohesive soils and soils in embankments of hydraulic structures is the density index (ID). The parameter used to control the quality of compaction of cohesive and non-cohesive soils artificially thickened, embedded in a variety of embankments is the degree of compaction (IS). In order to determine the parameters of density (ID or IS), compaction parameters ( or should be examined in a laboratory, which often is a long and difficult procedure to carry out. Therefore, there is a need for methods of improving and shortening the test of compaction parameters based on the development and application of useful correlations. Since compaction parameters are dependent on the soil granulation, a method based on regression and artificial neural networks was applied to develop required correlations. Due to the large number of input variables of neural networks in relation to the number of case studies, a PCA method was used to reduce the number of input variables, which resulted in reduction in the size of neural networks.

Źródło:

Geological Quarterly; 2015, 59, 2; 400--407
1641-7291

Pojawia się w:

Geological Quarterly

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

Tytuł:

An application of Hilbert-Huang transform and principal component analysis for diagnostics of cylindrical plunge grinding process

Autorzy:

Lajmert, P.

Powiązania:

https://bibliotekanauki.pl/articles/100119.pdf

Data publikacji:

2010

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

diagnostics
plunge grinding
Hilbert-Huang transform

Opis:

This paper presents a sensor based diagnostic system for a cylindrical plunge grinding process which ensures a reliable process state and tool wear identification. A new signal processing technique, i.e. Hilbert-Huang transform (HHT) was evaluated for this purpose based on the vibration and acoustic emission signal measurements. Numerical and experimental studies have demonstrated that the process state and tool wear may be effectivel detected through a statistical analysis of the time-dependent amplitudes and instantaneous frequencies resulting from the HHT. A principal component analysis was used to diagnose different grinding process states.

Źródło:

Journal of Machine Engineering; 2010, 10, 1; 39-49
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 27.

Tytuł:

Porównanie liniowych metod PCA (Principal Component Analysis) i LDA (Linear Discriminant Analysis) zastosowanych do klasyfikacji matryc wzbudzeniowo-emisyjnych wybranych grup substancji biologicznych
Comparison of Principal Component Analysis and Linear Discriminant Analysis applied to classification of excitation-emission matrices of the selected biological material

Autorzy:

Leśkiewicz, M.
Kaliszewski, M.
Mierczyk, Z.
Włodarski, M.

Powiązania:

https://bibliotekanauki.pl/articles/211300.pdf

Data publikacji:

2016

Wydawca:

Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego

Tematy:

analiza cech
spektroskopia fluorescencyjna
klasyfikacja substancji biologicznych
Feature Analysis
Fluorescence Spectroscopy
Biological Material Classification

Opis:

W pracy porównano właściwości dwóch liniowych metod (PCA i LDA) pozwalających na redukcję wymiarów w trakcie analizy cech oraz zbadano wydajność tych dwóch algorytmów w procesie klasyfikacji wybranego materiału biologicznego na podstawie jego wzbudzeniowo-emisyjnych matryc fluorescencyjnych. Stwierdzono, że metoda LDA redukuje liczbę wymiarów (znaczących zmiennych) bardziej efektywnie niż metoda PCA. Za pomocą algorytmu LDA udało się uzyskać względnie dobre rozróżnienie badanego materiału biologicznego.
Quality of two linear methods (PCA and LDA) applied to reduce dimensionality of feature analysis is compared and efficiency of their algorithms in classification of the selected biological materials according to their excitation-emission fluorescence matrices is examined. It has been found that LDA method reduces the dimensions (or a number of significant variables) more effectively than PCA method. Arelatively good discrimination within the examined biological material has been obtained with the use of LDA algorithm.

Źródło:

Biuletyn Wojskowej Akademii Technicznej; 2016, 65, 1; 15-31
1234-5865

Pojawia się w:

Biuletyn Wojskowej Akademii Technicznej

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

Multi-parameter data visualization by means of principal component analysis (PCA) in qualitative evaluation of various coal types

Autorzy:

Niedoba, T.

Powiązania:

https://bibliotekanauki.pl/articles/109595.pdf

Data publikacji:

2014

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

principal component analysis
PCA
multi-parameter data visualization
coal
identification of data
covariance matrix
pattern recognition

Opis:

Multi-parameter data visualization methods are a modern tool allowing to classify some analyzed objects. When it comes to grained materials, e.g. coal, many characteristics have an influence on the material quality. Besides the most obvious features like particle size, particle density or ash contents, coal has many other qualities which show significant differences between the studied types of material. The paper presents the possibility of applying visualization techniques for coal type identification and determination of significant differences between various types of coal. The Principal Component Analysis was applied to achieve this purpose. Three types of coal 31, 34.2 and 35 (according to Polish classification of coal types) were investigated, which were initially screened on sieves and subsequently divided into density fractions. Next, each size-density fraction was analyzed chemically to obtain other characteristics. It was pointed out that the applied methodology allowed to identify certain coal types efficiently, which makes it useful as a qualitative criterion for grained materials. However, it was impossible to provide such identification based on contrastive comparisons of all three types of coal. The presented methodology is a new way of analyzing data concerning widely understood mineral processing.

Źródło:

Physicochemical Problems of Mineral Processing; 2014, 50, 2; 575-589
1643-1049
2084-4735

Pojawia się w:

Physicochemical Problems of Mineral Processing

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Principal Component Analysis in the Study of Structure of the Best Polish Decathlon Competitors from the Period between 1985–2015

Autorzy:

Dziadek, Bartosz
Iskra, Janusz
Przednowek, Krzysztof

Powiązania:

https://bibliotekanauki.pl/articles/1030587.pdf

Data publikacji:

2018

Wydawca:

Uniwersytet Szczeciński. Wydawnictwo Naukowe Uniwersytetu Szczecińskiego

Tematy:

decathlon
principal component analysis
sport career

Opis:

The modern decathlon is a sport consisting of ten different events held over two days, played by men. Depending on the complexity of combined events, variety of events (runs, throws, jumps), the multi-stage, time-consuming and difficult training process the sport is considered as one of the most difficult. The analysis of careers of the best decathlon participants and applying advanced data-mining methods can help define the patterns occurring between each decathlon event and the final result. The research material encompasses career data of the 25 top competitors from Poland in years 1985–2015. Principal component analysis (PCA) was used in the research in order to designate new uncorrelated variables (components), representing input data across a new plane. Data analysis involved appointment of correlations between the events, determining the number of main components taken into account in further studies, analysis of the weight of each variable in formation of main components as well as visualisation and interpretation of results in the new plane described by the determined main components. Through the implementation of PCA method in the process of analysis it was possible to designate over 69% of compound data volatility with the use of the first three components. The first component, comprised of seven variables, displays the largest share in the total variability. The study of the relationship between variables in the new plane displayed strong correlations between sprint events (100 m, 110 m hurdles) and long jump and pole vault. No correlations between the 1500 m run and other events were found.

Źródło:

Central European Journal of Sport Sciences and Medicine; 2018, 23, 3; 77-87
2300-9705
2353-2807

Pojawia się w:

Central European Journal of Sport Sciences and Medicine

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

Specyficzna identyfikacja emiterów radarowych bazująca na analizie składowych głównych
Specific radar emitter identification based on principal component analysis

Autorzy:

Kawalec, A.
Owczarek, R.
Rapacki, T.
Wnuczek, S.

Powiązania:

https://bibliotekanauki.pl/articles/210693.pdf

Data publikacji:

2006

Wydawca:

Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego

Tematy:

walka elektroniczna
klasyfikacja cech sygnałów radarowych
specyficzna identyfikacja emiterów
przekształcenie Karhunena-Loeve'a
warfare electronic
radar signal feature classification
specific emitter identification
Karhunen-Loeve expansion

Opis:

W artykule została przedstawiona problematyka związana z identyfikacją emiterów radarowych należących do tego samego typu i klasy. Jest to specyficzny rodzaj identyfikacji (SEI, ang. Specific Emitter Identification), polegający na rozróżnianiu poszczególnych egzemplarzy tego samego typu radaru. Klasyczna identyfikacja sygnałów bazująca na analizie statystycznej podstawowych parametrów mierzalnych sygnału nie spełnia wymagań stawianych przed SEI. Przedstawiona w artykule metoda identyfikacji opiera się na przekształceniu Karhunena-Loeve'a (KL), która należy do metod analizy składowych głównych (PCA, ang. Principal Component Analysis).
One of the most difficult tasks in the radar signal processing is optimal features extraction and classification. The multifunction radar systems cannot be classified and precisely recognized by most of new and modern Electronic Support Measure and Electronic Intelligence Devices in the real time. In most cases, the modern ESM/ELINT systems cannot recognize the different devices of the same type or class. New method of the radar identification with a high quality of recognizing is the Specific Emitter Identification (SEI). The main task is to find non-intentional modulations in the receiving signals. This paper provides an overview of the new methods of measurement emitter signal features parameters and their transformation. This paper presents some aspects of radar signal features processing using Karhunen-Loeve's expansion as a feature selection and classification transform.

Źródło:

Biuletyn Wojskowej Akademii Technicznej; 2006, 55, 1; 41-54
1234-5865

Pojawia się w:

Biuletyn Wojskowej Akademii Technicznej

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Machining Investigations of Squeeze Cast TiB2/Al 7075 Composites through EDM: Regression Modelling and Weighted Principal Component Analysis

Autorzy:

Pradhan, Rahul Chandra
Das, Diptikanta
Sahoo, Barada Prasanna
Rout, Chiranjeeb
Panda, Akash
Barla, Evangelin

Powiązania:

https://bibliotekanauki.pl/articles/27324484.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

Al 7075
TiB2
metal matrix composite
electrical discharge machining
weighted principal component analysis

Opis:

2 wt.% TiB2 (mean particle size: 400 nm) reinforced Al 7075 metal matrix composites (MMCs) fabricated through mechanical stirring and ultrasonic agitation integrated squeeze casting process were subjected to electrical discharge machining (EDM) after determining the physical and mechanical properties. EDM was conducted with Cu electrode tools to investigate influence of machining factors, i.e. peak current (IP), pulse on time (TON) and gap voltage (VG) on the tool wear rate (TWR), material removal rate (MRR) and average surface roughness (ASR) of the machined surfaces. All the three responses increased on increasing IP and TON, but reduced on increasing VG. The machined surfaces were studied through scanning electron microscope (SEM). Significance of the EDM parameters on the individual responses were studied using analysis of variance (ANOVA) and regression models for the responses were developed using response surface method (RSM). The responses under consideration were optimized simultaneously using Taguchi embedded weighted principal component analysis (WPCA), which resulted the parametric combination of 4A (current), 100 μs (pulse duration) and 75V (voltage) was the optimal setting for the multi-criteria decision problem. Finally, the result of optimization was validated by conducting some confirmatory experiments.

Źródło:

Archives of Metallurgy and Materials; 2023, 68, 2; 551--562
1733-3490

Pojawia się w:

Archives of Metallurgy and Materials

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

Unemployment rate for various countries since 2005 to 2012: comparison of its level and pace using functional principal component analysis

Autorzy:

Jaworski, Stanisław
Furmańczyk, Konrad

Powiązania:

https://bibliotekanauki.pl/articles/453714.pdf

Data publikacji:

2012

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki

Tematy:

B-splines basis system
functional principal component analysis,
unemployment rate

Opis:

We apply the functional principal component analysis to compare the unemployment rate in euro area, Japan and USA since 2005 to 2012. For preprocessing analysis we used B-splines system with roughness penalty for smoothing the data. The analysis enables to reveal the most important type of variation in unemployment rate and its pace's in examined countries.

Źródło:

Metody Ilościowe w Badaniach Ekonomicznych; 2012, 13, 2; 40-47
2082-792X

Pojawia się w:

Metody Ilościowe w Badaniach Ekonomicznych

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

Tytuł:

Analiza nieliniowych składowych głównych dla danych czasowo‑przestrzennych geograficznie ważonych
Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data

Autorzy:

Krzyśko, Mirosław
Łukaszonek, Wojciech
Ratajczak, Waldemar
Wołyński, Waldemar

Powiązania:

https://bibliotekanauki.pl/articles/658130.pdf

Data publikacji:

2018

Wydawca:

Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego

Tematy:

nieliniowa analiza składowych głównych
dane geograficznie ważone
dane czasowo‑przestrzenne
nonlinear principal component analysis
geographically weighted data
temporal‑spatial data

Opis:

Schölkopf, Smola and Müller (1998) have proposed a nonlinear principal component analysis (NPCA) for fixed vector data. In this paper, we propose an extension of the aforementioned analysis to temporal‑spatial data and weighted temporal‑spatial data. To illustrate the proposed theory, data describing the condition of state of higher education in 16 Polish voivodships in the years 2002–2016 are used.
Schölkopf, Smola i Müller (1998) zaproponowali analizę nieliniowych składowych głównych (NPCA) dla ustalonych danych wektorowych. Niniejszy artykuł zawiera rozszerzenie tej metody na dane czasowo‑przestrzenne oraz czasowo‑przestrzenne geograficznie ważone. Każdy obiekt jest scharakteryzowany za pomocą macierzy Xi, rozmiaru T × p, zawierającej wartości p cech zaobserwowanych w T momentach czasowych, i = 1, …, n. Macierze te są przekształcane nieliniowo do przestrzeni Hilberta i budowana jest scentrowana macierz jądrowa. Ostatecznie macierz ta jest podstawą konstrukcji nieliniowych składowych głównych. W przypadku danych geograficznie ważonych macierz Xizostaje zastąpiona macierzą wiXi, gdzie wijest dodatnią wagą geograficzną związaną z i‑tym miejscem obserwacji, i = 1, …, n. Teoria zilustrowana jest przykładem dotyczącym stanu szkolnictwa wyższego w 16 polskich województwach, notowanego w latach 2002–2016.

Źródło:

Acta Universitatis Lodziensis. Folia Oeconomica; 2018, 4, 337; 169-181
0208-6018
2353-7663

Pojawia się w:

Acta Universitatis Lodziensis. Folia Oeconomica

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 34.

Tytuł:

Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods

Autorzy:

Alipour, N.
Nasseri, A.
Mohammbdi, T.A.
Pazira, E.

Powiązania:

https://bibliotekanauki.pl/articles/971544.pdf

Data publikacji:

2019

Wydawca:

Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej

Tematy:

artificial neural network
geostatistical analysis
irrigation
soil infiltration

Opis:

Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods.

Źródło:

Polish Journal of Soil Science; 2019, 52, 1; 59-74
0079-2985

Pojawia się w:

Polish Journal of Soil Science

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 35.

Tytuł:

Evaluation of dimensions of farmer attitudes in principal component analysis (PCA)
Ocena wymiarów postaw rolników w analizie głównych składowych (PCA)

Autorzy:

Domagalska-Grędys, M.

Powiązania:

https://bibliotekanauki.pl/articles/1790269.pdf

Data publikacji:

2020

Wydawca:

The Polish Association of Agricultural and Agribusiness Economists

Tematy:

relations and attitudes of farmers implementing biodiversity processes
social capital
bond
relationship opportunism
PCA
relacje i postawy rolników realizujących procesy bioróżnorodności
kapitał
społeczny
więź
oportunizm relacji

Opis:

The aim of the article was to identify leading relationship attitudes among farmers keeping animals of conservative breeds. The practical justification for the adopted analyses was to identify factors that foster desirable relations (attitudes) in agriculture based on ties. The research was conducted among 145 farms using an interview questionnaire in the poviats of three voivodships (Malopolskie, Podkarpackie, Lubelskie), where operations with livestock conservation breeds occurred. Collective selection was deliberate, meeting the criteria for the use of the extended diversity of breeds of farmed animals in 3 categories (cows, sheep and pigs). On the basis of the PCA test and analysis, 2 types of attitudes were selected: bonded and opportunistic. In the implemented accounts represented by farms according to three species of animals of conservative breeds, the opportunistic attitude was more prevalent than the prison attitude. The opportunism of pig and cattle breeders was particularly valued. In addition, the distribution of attitudes in groups was analysed, among others, due to the characteristics of farmers (age, sex, education and professional experience) and the presence of a successor on the farm. What was confirmed, among others, was the impact of a lack of professional experience of farmers on pro-bonding attitudes. In addition, in the groups, the distribution of attitudes was analysed, among others, according to the characteristics of farmers (age, gender, education, professional experience) and the presence of a successor on the farm. The influence of the lack of professional experience of farmers on relationship-oriented attitudes was confirmed. The younger generation of farmers may be more effective in implementing programmes of genetic biodiversity of farm animals. Small-scale farms, developed by better-educated farmers, with short work experience in agriculture and less experience in keeping animals of conservative breeds, prove to be developmental. The obtained results are illustrative of purposely selected objects, with restrictions, they can be related to the population of all Polish farms keeping animals of conservative breeds.
Celem artykułu jest identyfikacja wiodących postaw relacyjnych wśród rolników utrzymujących zwierzęta ras zachowawczych. Uzasadnieniem praktycznym przyjętych analiz było wskazanie czynników, sprzyjających pożądanym relacjom (postawom) w rolnictwie opartym na więzi. Badania przeprowadzono w 145 gospodarstwach z wykorzystaniem kwestionariusza wywiadu w powiatach trzech województw (małopolskiego, podkarpackiego i lubelskiego), w których występowały gospodarstwa z rasami zachowawczymi zwierząt gospodarskich. Dobór gospodarstw był celowy, spełniający kryterium zapewnienia największego zróżnicowania ras zwierząt hodowlanych 3 gatunków (krów, owiec i świń). Na podstawie testu i analizy PCA wyłoniono 2 typy postaw, tj. typ więzi i typ oportunistyczny. W realizowanych relacjach rolników reprezentujących gospodarstwa według trzech gatunków zwierząt ras zachowawczych, mocniej zaznaczyła się postawa oportunistyczna niż więziowa. Szczególnie wysoko cenili oportunizm hodowcy świń i bydła. Dodatkowo w grupach przeanalizowano rozkłady postaw, m.in. ze względu na cechy rolników (wiek, płeć, wykształcenie, doświadczenie zawodowe) i obecność następcy w gospodarstwie. Potwierdzono m.in. wpływ braku doświadczeń zawodowych rolników na postawy prowięziowe. Młodsze pokolenie rolników jest bardziej efektywne w realizacji programów bioróżnorodności genetycznej zwierząt gospodarskich. Rozwojowe okazały się mniejsze powierzchniowo gospodarstwa, prowadzone przez lepiej wykształconych rolników, o krótkim stażu pracy w rolnictwie i mniejszych doświadczeniach w utrzymywaniu zwierząt ras zachowawczych. Uzyskane wyniki są poglądowe dla dobranych celowo obiektów, z ograniczeniami można je odnosić do populacji wszystkich polskich gospodarstw utrzymujących zwierzęta ras zachowawczych.

Źródło:

Annals of The Polish Association of Agricultural and Agribusiness Economists; 2020, 22, 1; 66-76
2657-781X
2657-7828

Pojawia się w:

Annals of The Polish Association of Agricultural and Agribusiness Economists

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 36.

Tytuł:

The quality of public finance in the light of fiscal governance concept: implications for the European Union countries

Autorzy:

Kargol-Wasiluk, Aneta
Wildowicz-Giegiel, Anna

Powiązania:

https://bibliotekanauki.pl/articles/22446543.pdf

Data publikacji:

2018

Wydawca:

Instytut Badań Gospodarczych

Tematy:

public finance
quality
fiscal governance
principal component analysis

Opis:

Research background: The research area on the quality of public finance (QPF) appears to be intellectually attractive. In the light of the challenges of the 21st century, public finance should be characterized by adequate quality, ensuring effective implementation of the economic functions of government. The problem of QPF is increasingly more frequent in the face of a deteriorating fiscal situation of most countries in Europe and around the world. Hence, it is worth considering which factors determine the quality of public finance. Purpose of the article: This article aims to show the possibility of assessing the quality of public finance in the light of fiscal governance concept.  The identification of the key components of QPF seems to be useful from the point of view of empirical research, and can be implemented to assess the quality of public finance in the EU?28. Methods: Descriptive analysis along with principal component analysis (PCA) was implemented to indicate dimensions of QPF. Findings & Value added: The quality of public finance consists of a well-designed fiscal rules (numerical and non-numerical) and institutions, as well as structural reforms. The obtained results allow to characterize the quality of public finance through the prism of six identified principal components. They have a mixed character, two of them are partly or totally related to the institutional aspects of public finance, which proves their importance in the process of improving the quality of public finance. Improving the quality of public finance remains a key challenge for policy makers in the EU. The growing impact of globalization and the aging population also cause the need to improve the qualitative aspects of fiscal policy. The study contributes to the literature on public finance, particularly in the empirical dimension through broadening the knowledge on institutional factors which can be used to measure QPF index. The results of research have certainly enriched the existing knowledge on the phenomenon of QPF and the ways of its measurement.

Źródło:

Equilibrium. Quarterly Journal of Economics and Economic Policy; 2018, 13, 3; 411-426
1689-765X
2353-3293

Pojawia się w:

Equilibrium. Quarterly Journal of Economics and Economic Policy

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 37.

Tytuł:

The use of some morphological traits for the assessment of genetic diversity in spinach (Spinacia oleracea L.) landraces

Autorzy:

Ebadi-Segheloo, Asghar
Asadi-Gharneh, Hossein ali
Mohebodini, Mehdi
Janmohammadi, Mohsen
Nouraein, Mojtaba
Sabaghnia, Naser

Powiązania:

https://bibliotekanauki.pl/articles/2199739.pdf

Data publikacji:

2014-06-19

Wydawca:

Instytut Hodowli i Aklimatyzacji Roślin

Tematy:

breeding
cluster analysis
diversity
landraces
principal component analysis

Opis:

Investigation of native accessions of spinach (Spinacia oleracea L.) would be aid in the development of new genetically improved varieties, so in this research 121 spinach landraces, collected from the various spinach growing areas of Iran, were evaluated to determine their diversity using several agro-morphological traits. High coefficients of variation (CV) were recorded in fresh yield, leaf area and dry yield. Using principal component (PC) analysis, the first three PCs with eigenvalues more than 0.9 contributed 80.56% of the variability among accessions. The first PC was related to leaf yield performance (fresh and dry yields, leaf numbers at flowering and lateral branches) while the PC2 was related to leaf characteristic (leaf width, petiole length, petiole diameter and leaf area). The third PC was related to seed characteristic (seed yield and 1000-seed weight) and was named as seed property component. The 121 spinach landraces were grouped into six clusters using cluster analysis. Each cluster had some specific characteristics of its own and the clusters I and II were clearly separated from clusters III and V and also from clusters IV and VI. The studied accessions are an important resource for the generation of a core collection of spinach in the world. The results of present research will support tasks of conservation and utilization of landraces in spinach breeding programs.

Źródło:

Plant Breeding and Seed Science; 2014, 69; 69-80
1429-3862
2083-599X

Pojawia się w:

Plant Breeding and Seed Science

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 38.

Tytuł:

Assessing domestic factors determining water consumption in a semi-arid area (Sedrata City) using artificial neural networks and principal component analysis

Autorzy:

Zeroual, Menal
Hani, Azzedine
Boustila, Amir

Powiązania:

https://bibliotekanauki.pl/articles/1844342.pdf

Data publikacji:

2021

Wydawca:

Instytut Technologiczno-Przyrodniczy

Tematy:

artificial neural networks
domestic water use determinants
household water consumption
principal component analysis
semi-arid area

Opis:

The growing demand for fresh water and its scarcity are the major problems encountered in semi-arid cities. Two different techniques have been used to assess the main determinants of domestic water in the Sedrata City, North-East Algeria: principal component analysis (PCA) and artificial neural networks (ANNs). To create the ANNs models based on the PCA, twelve explanatory variables are initially investigated, of which nine are socio-economic parameters and three physical characteristics of building units. Two optimum ANNs models have been selected where correlation coefficients equal to 0.99 in training, testing and validation phases. In addition, results demonstrate that the combination of socio-economic parameters with physical characteristics of building units enhances the assessment of household water consumption.

Źródło:

Journal of Water and Land Development; 2021, 49; 219-228
1429-7426
2083-4535

Pojawia się w:

Journal of Water and Land Development

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

Geomorphic Control on Soil Erosion – a Case Study in the Subarnarekha Basin, India

Autorzy:

Kathwas, Amar Kumar
Patel, Nilanchal

Powiązania:

https://bibliotekanauki.pl/articles/2088182.pdf

Data publikacji:

2021

Wydawca:

Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej

Tematy:

geomorphological feature
soil erosion
USLE
principal component analysis

Opis:

Geomorphology depicts the qualitative and quantitative characteristics of both terrain and landscape features combined with the processes responsible for its evolution. Soil erosion by water involves processes, which removes soil particles and organic matter from the upper sheet of the soil surface, and then transports the eroded material to distant location under the action of water. Very few studies have been conducted on the nature and dynamics of soil erosion in the different geomorphologic features. In the present investigation, an attempt has been made to assess the control of geomorphologic features on the soil loss. Universal Soil Loss Equation (USLE) was used to determine soil loss from the various geomorphological landforms. Principal component analysis (PCA) was implemented on the USLE parameters to determine the degree of association between the individual principal components and the USLE-derived soil loss. Results obtained from the investigation signify the influence of the various landforms on soil erosion. PC5 is found to be significantly correlated with the USLE-derived soil loss. The results ascertained significant association between the soil loss and geomorphological landforms, and therefore, suitable strategies can be implemented to alleviate soil loss in the individual landforms.

Źródło:

Polish Journal of Soil Science; 2021, 54, 1; 1-24
0079-2985

Pojawia się w:

Polish Journal of Soil Science

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 40.

Tytuł:

Taxonomic position of Pinus ceciliae (Pinaceae) endemic for Balearic Islands as revealed on needle characteristics

Autorzy:

Boratynska, K.
Tomaszewski, D.
Montserrat, J.M.
Marek, S.
Boratynski, A.

Powiązania:

https://bibliotekanauki.pl/articles/2077667.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Instytut Dendrologii PAN

Tematy:

biometry
discrimination analysis
Pinus halepensis
Principal Component Analysis

Opis:

The Cecilian pine (Pinus ceciliae) is endemic to Balearic islands (Spain). It is a small taxon, some- times treated as synonym of Aleppo pine (P. halepensis), to which is closely related, differing mainly with dense crown shape and upright branches. The other characteristics, which differ between P. ceciliae and P. halepensis concern the cone scale and needle length only. We examined biometrically needles of Cecilian pine from Mallorca (5 tress) and Menorca (9 trees) islands, and compared them to Aleppo pine populations rep- resented by 30 trees from each island. Each tree was represented by 5 needles, and they were studied with respect to 17 morphological and anatomical characteristics. We detected that needles of the Cecilian pine were smaller, but only when compared to the Aleppo pine from the same island. In general, this difference was also observed in the number of resin canals, number of stomata and stomatal rows. Interestingly, the proportions of the needle dimensions pattern were similar in both taxa. In conclusion we stated the results support the taxonomic rank of Cecilian pine as a variety, Pinus halepensis var. ceciliae (Llorens & L.Llorens) L.Llorens, Fl. Països Catalans, 1: 197 (1984)

Źródło:

Dendrobiology; 2019, 82; 8-16
1641-1307

Pojawia się w:

Dendrobiology

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 41.

Tytuł:

Klasyczna i neuronowa analiza głównych składowych na przykładzie zadania kompresji obrazu
Classical and neural network-based principal component analysis for image compression

Autorzy:

Bartecki, K.

Powiązania:

https://bibliotekanauki.pl/articles/154740.pdf

Data publikacji:

2013

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

analiza składników głównych
sztuczna sieć neuronowa
kompresja danych
principal component analysis
artificial neural network
data compression

Opis:

W artykule omówiono zastosowanie analizy składników głównych (PCA) w zadaniu kompresji stratnej sygnału na przykładzie kompresji obrazu. Zadanie zrealizowano z wykorzystaniem klasycznej metody PCA oraz dwóch rodzajów sieci neuronowych: jednokierunkowej, dwuwarstwowej sieci z uczeniem nadzorowanym i jednowarstwowej sieci z uczeniem nienadzorowanym. W każdym z przypadków przeanalizowano wpływ struktury modelu PCA na wartości współczynnika kompresji oraz średniokwadratowego błędu kompresji.
In the paper, lossy data compression techniques based on the principal component analysis (PCA) are considered on the example of image compression. The presented task is performed using the classical PCA method based on the eigen-decomposition of the image covari-ance matrix as well as two different kinds of artificial neural networks. The first neural structure used is a two-layer feed-forward network with supervised learning shown in Fig.1, while the second one is a single-layered network with unsupervised Hebbian learning. In each case considered, the effect of the PCA model structure on the data compression ratio and the mean square reconstruction error is analysed. The compression results for a Hebbian neural network with K=4 PCA units are presented in Figs. 2, 3 and 4. They show that only 4 eigenvectors are able to capture the main features of the processed image, giving as a result high value of the data compression ratio. However, the reconstructed image quality is not sufficient from a practical point of view. Therefore, selection of the appropriate value for K should take into account the tradeoff between a sufficiently high value for the compression ratio and a reasonably low value for the image reconstruction error. The summary results for both classical and neural PCA compression approaches obtained for different number of eigenvectors (neurons) are compared in Fig. 5. The author concludes that a positive aspect of using neural networks as a tool for extracting principal components from the image data is that they do not require calculating the correlation matrix explicitly, as in the case of the classical PCA-based approach.

Źródło:

Pomiary Automatyka Kontrola; 2013, R. 59, nr 1, 1; 34-37
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 42.

Tytuł:

Multi-trait evaluation of value for cultivation and use of early maturing edible potato cultivars registered in Poland

Autorzy:

Rymuza, K.

Powiązania:

https://bibliotekanauki.pl/articles/123780.pdf

Data publikacji:

2015

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

edible potato
potato cultivar
principal component analysis
cluster analysis

Opis:

The work presents an analysis of diversity and comparison of value for cultivation and use of early maturing potato cultivars registered with the Polish National Register of Cultivars. The comparison was based on 17 yield and appearance traits and quality attributes of tubers as well as their resistance to diseases. The analysis employed the following multi-dimensional statistical methods: principal component analysis and cluster analysis. The principal component analysis revealed that over 70% of the total variation was associated with the first 6 principal components. Cluster analysis yielded 4 groups of genotypes. The first group consisted of the cultivars which produced tubers with the most shallow eyes, the best flavour and the least severe darkening of raw flesh. The cultivars in the second group produced high yields and were low in starch, dry matter and glycoalkaloids. The tubers of cultivars which were classified into the third group had the highest starch, dry matter and vitamin C contents. However, they produced the lowest yields and were quite susceptible to most diseases. The fourth group was made up of high-yielding cultivars which tended to accumulate glycoalkaloids but were most resistant to hollow heart in tubers.

Źródło:

Journal of Ecological Engineering; 2015, 16, 1; 50-56
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 43.

Tytuł:

Analiza porównawcza efektywności metod redukcji zmiennych - analiza składowych głównych i analiza czynnikowa
Comparative Analysis of Effectiveness of the Methods for Reduction of Variables - Principal Component Analysis and Factor Analysis

Autorzy:

Czopek, Anna

Powiązania:

https://bibliotekanauki.pl/articles/589975.pdf

Data publikacji:

2013

Wydawca:

Uniwersytet Ekonomiczny w Katowicach

Tematy:

Analiza czynnikowa
Analiza porównawcza
Analizy głównych komponentów
Comparative analysis
Factor analysis
Principal Component Analysis

Opis:

Principal component analysis and factor analysis are the two most popular methods that allow to bring a large number of studied variables to a much smaller number of mutually independent principal components or factors. New variables (principal components or factors) retain a relatively large part of the information contained in the original variables, while each of them is a carrier of other substantive content. Both of these methods of reduction of the variables are often used, because too many pending attributes increases the range of the difficulty of interpretation. The main reason of undertaking the project is an attempt to show, that the abovementioned methods, although they are very similar, cannot be indentified. Despite the fact, that in both cases eigenvalues are calculated, factor loadings, etc., but still there are differences in the way of action, about which it must be remembered. So the usage of these names the variables are unacceptable. The article consists of three parts. The first and second chapter are devoted, respectively, to the analysis of the principal components and factor analysis, where a short characterization of these methods had been made. In the third chapter, on the basis of an empirical example, we compared the effectiveness of the principal components analysis and factor analysis.

Źródło:

Studia Ekonomiczne; 2013, 132; 7-23
2083-8611

Pojawia się w:

Studia Ekonomiczne

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

Assessment of Sustainable Development Using Cluster Analysis and Principal Component Analysis
Ocena zrównoważonego rozwoju za pomocą analizy skupień i analizy głównych składników

Autorzy:

Drastichová, Magdaléna
Filzmoser, Peter

Powiązania:

https://bibliotekanauki.pl/articles/371108.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Komitet Człowiek i Środowisko PAN

Tematy:

European Union (EU)
Hierarchical Cluster Analysis (HCA)
Principal Component Analysis (PCA)
Sustainable Development (SD)
Sustainable Development Goals (SDGs)
JEL Classification
Q01
Q50
Q51
Q54
Q56
Unia Europejska
hierarchiczna analiza skupień
analiza głównych wskaźników
rozwój zrównoważony
cele zrównoważonego rozwoju

Opis:

The European Union (EU) Sustainable Development Goals (SDG) indicator set replaced the EU Sustainable Development Strategy (SDS) in 2017. The selected indicators of this set were chosen for the analysis to classify the sample of the 28 EU countries along with Norway according to their performance in sustainability. In the selection of indicators, priority was given to the indicators reflecting the social dimension of SD, along with important representatives of the economic, ecological and institutional dimensions of SD generally. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were applied to the data of 12 indicators in the period 2012- 2016. By means of the HCA, four clusters were created in each year of the period 2012-2016 using the indicator values of particular years and then using all the indicator values in all the monitored years for the general assignment of countries to particular clusters. According to changes in the assignment to particular clusters over the years, the sustainability of development and the path of SD in the examined countries are assessed. As regards the core countries of each cluster, cluster 1 includes the most developed EU countries and is thus evaluated as the best performing cluster. Cluster 2 including the least developed EU countries is evaluated as the worst performing cluster. Cluster 3 predominantly includes the transitive economies and it is evaluated as the second best performing cluster according to the indicators applied. Cluster 4 containing the Southern countries is assessed as the second worst performing cluster. From the shifts of countries that occurred between the years, the shift of Ireland from cluster 3 to cluster 1 in 2013 must be emphasised as the move towards higher sustainability. The shift of Slovakia and Hungary from cluster 2 to cluster 3 in 2013 is also evaluated as progress towards higher sustainability.
W Unii Europejskiej w 2017 r. Cele zrównoważonego rozwoju zastąpiły dotychczasową Strategię zrównoważonego rozwoju. W tej pracy wybrane wskaźniki odnoszące się do nowych Celów zrównoważonego rozwoju stanowią podstawę klasyfikacji 28 krajów Wspólnoty oraz Norwegii. Wśród tych wskaźników priorytetowo potraktowano te odnoszące się do wymiaru społecznego zrównoważonego rozwoju, uzupełniając dyskusję o podstawowe wskaźniki ekonomiczne, ekologiczne i instytucjonalne. Przeanalizowano okres obejmujący lata 2012-2016. Wobec wybranych 12 wskaźników zastosowano hierarchiczną analizę skupień i analizę głównych składników. Utworzono cztery klastry w ramach każdego roku z analizowanego okresu, określając wartości wskaźników dla poszczególnych lat, a następnie określenie wszystkich wartości wskaźników dla wszystkich monitorowanych lat umożliwiło przypisanie krajów do poszczególnych klastrów. Określenie zmian w przypisaniu do poszczególnych klastrów na przestrzeni lat umożliwiło ocenę zrównoważoności rozwoju i określenie ścieżki zrównoważonego rozwoju badanych krajów. Jeśli chodzi o główne kraje każdego klastra, to klaster 1 obejmuje najbardziej rozwinięte kraje UE i dlatego jest oceniany jako klaster, który osiąga najlepsze wyniki. Klaster 2 uwzględnia najsłabiej rozwinięte kraje i oceniony jest jako ten, który osiąga najgorsze wyniki. Klaster 3 obejmuje głównie gospodarki znajdujące się w okresie przejściowym i jest oceniany jako drugi osiągający najlepsze wyniki. Klaster 4 obejmuje kraje Południa i jest oceniany jako drugi osiągający najgorsze wyniki. Uwzględniając zmiany jakie zaszły w okresie kolejnych lat, należy podkreślić przesunięcie Irlandii z klastra 3 do klastra 1 w 2013 r., co oznacza ruch w kierunku większej zrównoważoności. Tak samo należy ocenić przejście w tym samym roku Słowacji i Węgier z klastra 2 do klastra 3.

Źródło:

Problemy Ekorozwoju; 2019, 14, 2; 7-24
1895-6912

Pojawia się w:

Problemy Ekorozwoju

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 45.

Tytuł:

Use of principal component analysis for the assessment of spring wheat characteristics
Wykorzystanie metody analizy składowych głównych do oceny cech pszenicy jarej

Autorzy:

Rymuza, K.
Turska, E.
Wielogorska, G.
Bombik, A.

Powiązania:

https://bibliotekanauki.pl/articles/47062.pdf

Data publikacji:

2012

Wydawca:

Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB

Opis:

In the studies, the analysis of the diversification of spring wheat characteristics was carried out depending on the growth system. Field experiment was carried out in years 2004-2006 at the Agricultural Experimental Station in Zawady, which is part of the Siedlce University of Natural Sciences and Humanities. The obtained correlation coefficients prove that the relation between wheat characteristics depends on the growth system. The applied method of principal component analysis (PCA) allowed a complex assessment of the relations between the characteristics. It also made it possible to reduce the original seven characteristics to three new variables, which carried over 75% of the information of the input data obtained from the direct sowing and almost 80% for the conventional tillage. The greatest discriminatory power, which diversified the studied plants, was shown by the mass of 1000 grains and grain yield.
W badaniach dokonano analizy zróżnicowania cech pszenicy jarej w zależności od systemu uprawy. Doświadczenie polowe przeprowadzono w latach 2004- -2006 w Rolniczej Stacji Doświadczalnej Zawady, należącej do Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach. Uzyskane wartości współczynników korelacji dowodzą, że współzależność pomiędzy cechami pszenicy zależy od systemu uprawy. Zastosowana metoda analizy składowych głównych (PCA) pozwoliła na kompleksową ocenę współzależności cech. Umożliwiła jednocześnie zredukowanie siedmiu pierwotnych cech do trzech nowych zmiennych, które przenosiły ponad 75% informacji danych wejściowych uzyskanych dla siewu bezpośredniego i prawie 80% dla uprawy tradycyjnej. Najsilniejszą moc dyskryminacyjną, różnicującą badane obiekty, wykazały masa tysiąca ziaren i plon ziarna.

Źródło:

Acta Scientiarum Polonorum. Agricultura; 2012, 11, 1
1644-0625

Pojawia się w:

Acta Scientiarum Polonorum. Agricultura

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 46.

Tytuł:

Association between dietary patterns and cardiovascular risk factors in a selected population of Lower Silesia (PURE Study Poland)

Autorzy:

Czekajlo, A.
Różańska, D.
Zatońska, K.
Szuba, A.
Regulska-Ilow, B.

Powiązania:

https://bibliotekanauki.pl/articles/2081935.pdf

Data publikacji:

2018

Wydawca:

Instytut Medycyny Wsi

Tematy:

risk factors
cardiovascular diseases
principal component analysis
dietary patterns

Opis:

Introduction. Dietary pattern analysis is used to describe the dietary habits of a selected population. In many studies, dietary patterns (DPs) have been associated with risk factors for cardiovascular disease (CVD). The aim of the study was to assess the association between dietary patterns identified in the population of Lower Silesia, Poland, with anthropometric and biochemical risk factors for CVD. Materials and method. The study group included 2,025 participants of the Prospective Urban Rural Epidemiological (PURE) Study. Dietary intake was evaluated based on data from the Food Frequency Questionnaire (FFQ). Dietary patterns were derived using principal component analysis (PCA). The relationship between DPs and body mass index (BMI), waist circumference, waist-hip ratio, blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and fasting glucose level, was assessed. Results. Three dietary patterns identified in the study explained 35.6% of total variance. The ‘fruit, vegetables & dairy’ DP, characterized by a high intake of vegetables, fruits, nuts, seeds, raisins, milk and low-fat dairy, was associated with improved lipid profile and anthropometric measures, lower diastolic blood pressure and lower fasting glucose concentration. ‘Traditional’ and ‘fat & sugar’ DPs were unfavourably associated with most of the risk factors for CVD presented in this study. Conclusions. Dietary patterns identified in this study were differently related to selected anthropometric and biochemical risk factors for CVD. ‘Fruit, vegetables & dairy’ DP was favourably associated with the biochemical and anthropometric CVD risk factors, and was characterized by higher nutritional value in comparison with ‘traditional’ and ‘fat & sugar’ DPs.

Źródło:

Annals of Agricultural and Environmental Medicine; 2018, 25, 4; 635-641
1232-1966

Pojawia się w:

Annals of Agricultural and Environmental Medicine

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 47.

Tytuł:

Porównanie liniowych metod PCA (Principal Component Analysis) i LDA (Linear Discriminant Analysis) zastosowanych do klasyfikacji matryc wzbudzeniowo-emisyjnych wybranych grup substancji biologicznych

Autorzy:

Leśkiewicz, Maciej.
Kaliszewski, Miron.
Mierczyk, Zygmunt (1956- ).
Włodarski, Maksymilian.

Powiązania:

Biuletyn Wojskowej Akademii Technicznej 2016, nr 1, s. 15-31

Data publikacji:

2016

Tematy:

Skażenia biologiczne wykrywanie metody
Luminescencja stosowanie
Postęp techniczny

Opis:

Rys., tab.
Bibliogr.

Dostawca treści:

Bibliografia CBW

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Skocz do pozycji: 48.

Tytuł:

Optimal Stock Portfolio – Application of Multivariate Statistical Analysis
Optymalny portfel akcji – zastosowanie wielowymiarowej analizy statystycznej

Autorzy:

Konarzewska, Iwona

Powiązania:

https://bibliotekanauki.pl/articles/904809.pdf

Data publikacji:

2013

Wydawca:

Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego

Tematy:

optimal portfolio models
principal component analysis of rates of return

Opis:

Investigating relationship between risk of the Markowitz optimal portfolio and the strength of interdependence for the set of rates of return for portfolio components we state (Konarzewska, 2008, 2012) that the risk measured as variance/standard deviation is slightly sensitive on small disturbance in data set when the series of data are strongly interrelated. What more, portfolio risk rises as the strength of interdependence declines. We have found that if strong linear relationship is present among series, it is important to control the direction between the portfolio weights vector and the eigenvector corresponding to maximal eigenvalue of the correlation/covariance matrix – the ideal situation being orthogonality of the two vectors. These results can be utilized in: the algorithm for pre-selection of investment portfolio components, construction of the optimal investment portfolio models. Both propositions utilize eigenvalue decomposition of the rates of return correlation or covariance matrix. Theoretical results are illustrated by empirical examples for medium-sized firms being components of mWIG40 index on Stock Exchange in Warsaw. We compare optimal portfolios obtained for Markowitz and PCA – aided models.
W artykule przedstawiamy wybrane wyniki teoretyczne na temat konstrukcji optymalnego portfela akcji z wykorzystaniem informacji dostepnej w wyniku przeprowadzenia analizy głównych składowych macierzy kowariancji czy też macierzy korelacji stóp zwrotu z akcji. Wyniki teoretyczne prowadzą do konstrukcji modeli optymalizacyjnych uwzględniajacych redukcję przestrzeni danych do określonej liczby głównych składowych, co udaje się skutecznie przeprowadzić w warunkach silnych związków o charakterze liniowym między szeregami stóp zwrotu z akcji. W pracy prezentujemy wyniki analiz dla modeli Markowitza oraz modeli opartych o analizę głównych składowych na przykładzie sektora średnich spółek na GPW w Warszawie w latach 2009-2011. Badanie empiryczne pokazuje różnice wyników optymalizacji oraz ryzyka portfeli w przypadkach, kiedy korzystamy z macierzy kowariancji albo korelacji.

Źródło:

Acta Universitatis Lodziensis. Folia Oeconomica; 2013, 286
0208-6018
2353-7663

Pojawia się w:

Acta Universitatis Lodziensis. Folia Oeconomica

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 49.

Tytuł:

Physicochemical characterization of synclinal spring water of Taoura, region of Souk Ahras – North East Algeria

Autorzy:

Bouhafs, Fatma
Laraba, Abdelaziz

Powiązania:

https://bibliotekanauki.pl/articles/1841971.pdf

Data publikacji:

2021

Wydawca:

Instytut Technologiczno-Przyrodniczy

Tematy:

chemistry
principal component analysis
PCA
pollution
springs
Taoura
water quality

Opis:

The springs of the Taoura region flow from a syncline shaped structure. All resources in the region were mobilized as a result of increased demand. However, the development of anthropic activities and population growth in the area pose risk for groundwater. Analytical results obtained from a series of samplings in November 2017–April 2018, express the quality of water suitable for the irrigation of agricultural land. The highest values are recorded in April 2018 at 20.5 to 21.6°C and pH of 8.0 to 8.2. The study recorded high electrical conductivity from 1390 to 1495 μS∙cm–1 and TDS from 1270 to 1500 mg∙dm–3 in November 2017, which shows important mineralization that characterizes spring water. Physical parameters were measured in situ using a HORIBA multi-parameter probe. Chemical analyses were carried out using NFT 90-005 titration, and nitrogen parameters by DIN 38405-D92 spectrophotometry. Maximum levels of nitrates and phosphates were recorded at 228 and 18.4 mg∙dm–3 respectively. The principal component analysis (PCA) showed a good correlation of the November 2017 period with mineralization parameters. Moreover, there is a strong correlation between the wet period and pollution factors. The two methods of analysis has allowed to distinguish three groups of geochemical water types: a bicarbonate calcium group typical for waters having transited in carbonate horizons. A second chloride calcium group shows basic exchange between water and clay levels, and the third chloride bicarbonate calcium group reveals an enrichment in calcium and chloride, which reflects water circulation with an exchange of the carbonated and evaporitic sedimentary rock matrix.

Źródło:

Journal of Water and Land Development; 2021, 50; 27-37
1429-7426
2083-4535

Pojawia się w:

Journal of Water and Land Development

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 50.

Tytuł:

A game theoretical study of generalised trust and reciprocation in Poland. [Part] 2, A description of the study group

Autorzy:

Markowska-Przybyła, U.
Ramsey, D. M.

Powiązania:

https://bibliotekanauki.pl/articles/406286.pdf

Data publikacji:

2015

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

Bayesian networks
principal component analysis
social survey
experimental game theory

Opis:

The first article describing this project presented the three games that the participants played: the Ultimatum Game, the Trust Game and the Public Goods Game. This article describes the study group on the basis of a questionnaire regarding where they study and come from, their social contacts, interest in current issues, views on inequality and outlook on life. A description of the migratory decisions of students is given. In particular, two exploratory methods are used to investigate the data’s structure: Bayesian networks and principal component analysis. Bayesian networks are used to illustrate the associations between categorical variables. Principal component analysis is designed to describe latent variables which reflect the associations between numerical variables. We present the results of this analysis and discuss the advantages and disadvantages of these two methods.

Źródło:

Operations Research and Decisions; 2015, 25, 2; 51-73
2081-8858
2391-6060

Pojawia się w:

Operations Research and Decisions

Dostawca treści:

Biblioteka Nauki

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