Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "principal component analysis" wg kryterium: Wszystkie pola

  Lista filtrów
Wyrównaj
Wyświetlanie 1-25 z 204
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
Wyświetlanie 1-25 z 204

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies