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1

Zastosowanie metod statystycznych do określenia związków pomiędzy minerałami a pierwiastkami jako wprowadzenie do analizy chemostratygraficznej

Przelaskowska Anna, Zagórska Urszula

Nafta-Gaz

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2024

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R. 80, nr 4

191--201

PL

Chemostratygrafia to technika korelacji oparta na danych geochemii nieorganicznej. Polega na dobraniu odpowiednich wskaźników korelacyjnych (pierwiastków / stosunków pierwiastków) pozwalających na wyodrębnienie charakterystycznych poziomów chemostratygraficznych w profilu otworu. Dla prawidłowego doboru wskaźników chemostratygraficznych niezbędne jest ustalenie związków między minerałami a pierwiastkami, ponieważ wiele pierwiastków może wchodzić w skład różnych minerałów. Celem pracy było zastosowanie analizy statystycznej do scharakteryzowania związków między minerałami a pierwiastkami dla próbek piaskowców czerwonego spągowca. W pracy wykorzystano takie metody jak: analiza korelacyjna oparta na interpretacji macierzy korelacji (CC) i wykresach korelacyjnych oraz analiza głównych składowych (PCA). PCA służy do redukcji liczby zmiennych opisujących zjawiska oraz do odkrycia prawidłowości między zmiennymi. Uzyskane wyniki pozwoliły na wyróżnienie kilku grup pierwiastków wzajemnie ze sobą powiązanych, kumulujących się w podobnych minerałach. Wyodrębniono szereg grup związanych z różnymi minerałami, między innymi z minerałami ciężkimi, minerałami ilastymi i dodatkami z płuczki. Pierwiastki ziem rzadkich (REE) rozdzieliły się na dwie grupy: lekkie ziemie rzadkie (LREE) i ciężkie ziemie rzadkie (HREE), co świadczy o tym, że mogą być związane z nieco innymi asocjacjami minerałów ciężkich. Analiza korelacyjna potwierdziła wnioski uzyskane na podstawie analizy PCA, jak również pozwoliła na uszczegółowienie niektórych zależności. Podsumowując, w ramach pracy scharakteryzowano związki pomiędzy pierwiastkami a minerałami w profilu otworu Pł 3. Zaprezentowano właściwy sposób analizy danych geochemicznych, który jest podstawą budowania podziału chemostratygraficznego.

EN

Chemostratigraphy is a correlation technique based on inorganic geochemistry data. It involves the selection of appropriate correlation indices (elements/element ratios) that allow to determine characteristic chemozones in the borehole profile. Determination of element-mineral links is necessary for the correct selection of chemostratigraphic indices, as many elements can be part of different minerals. The purpose of this study was to apply statistical analysis to characterize the relationships between minerals and elements for Rotliegend sandstone samples. The following methods were used in the study: correlation analysis based on interpretation of correlation matrix (CC), correlation plots and principal component analysis (PCA). Principal component analysis (PCA) is used to reduce the number of variables describing phenomena and to discover regularities between them. The results made it possible to distinguish several groups of elements related to each other, accumulating in similar minerals. A number of groups associated with various minerals were distinguished, including, among others, heavy minerals, clay minerals and mud additives. Rare earth elements (REEs) separated into two groups: light rare earths (LREE) and heavy rare earths (HREE), indicating that they may be associated with slightly different heavy mineral associations. The correlation analysis confirmed the conclusions obtained from the PCA analysis and allowed for a more detailed analysis of some relationships. In conclusion, the paper characterizes the relationships between elements and minerals in the profile of the Pł 3 borehole. The correct method of analysing geochemical data, which is the basis for building a chemostratigraphic division, was presented.

2

Registration, modal decomposition and analysis of human left ventricles

Stankiewicz Witold

Vibrations in Physical Systems

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2023

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Vol. 34, nr 2

art. no. 2023224

EN

Cardiovascular diseases, especially myocardial infarction and heart failure, are among the most common causes of death. Proper, timely diagnosis can be a key factor in reducing the mortality of these diseases. In the present paper, statistical data analysis of left ventricle of human heart is presented. Raster DICOM images are processed, segmented and registered, in order to mark the left ventricle on medical images, and then to obtain its geometrical 3D models of constant topology. Registered, geometrical data, obtained for whole cardiac cycle of patients with healthy hearts, hypertrophy and heart failure, is then decomposed using Principal Component Analysis. The obtained modes represent the movement of the ventricle during one heart cycle. The proposed approach allows neglecting unimportant, noisy signal and enables the interpretation of the heart cycle. It is shown that modal decomposition might be used to distinguish the hearts with heart failure and the group containing healthy hearts and the ones with hypertrophy. Being a non-invasive method, this approach enables the diagnosis of various hearts, including prenatal ones.

3

Odtwarzanie położenia wału dla prędkości zerowej w napędzie z PMSM z użyciem analizy głównych składowych

Urbański Konrad, Janiszewski Dariusz

Przegląd Elektrotechniczny

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2023

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R. 99, nr 5

237--242

PL

W pracy przedstawiono sposób odtwarzania położenia wału silnika synchronicznego z magnesami trwałymi z wykorzystaniem dodatkowego prądu wysokiej częstotliwości. Uzyskany hodograf tego prądu przetwarzany jest z użyciem analizy głównych składowych. Rezultatem przetwarzania jest informacja o poziomie dopasowania do poszczególnych wzorców. Wzorzec o najlepszym dopasowaniu określa odtworzone położenie wału maszyny. Badania zostały przeprowadzone z użyciem danych pomiarowych laboratoryjnego układu napędowego z PMSM.

EN

This paper presents a method of estimating the shaft position of a permanent magnet synchronous motor using an additional high-frequency current. The resulting hodograph of this current is processed using principal component analysis. The result of the processing is information about the level of fit to individual patterns. The pattern with the best match determines the estimated shaft position. The research was carried out using measurement data of a laboratory drive with a PMSM.

4

Utilization of ASTER data in lithological and lineament mapping of the southern flank of the Central High Atlas in Morocco

Errami Maryam, Algouti Ahmed, Algouti Abdellah, Farah Abdelouhed, Agli Saloua

Geologos

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2023

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Vol. 29, No. 1

1--20

EN

Geological mapping undoubtedly plays an important role in several studies and remote sensing data are of great significance in geological mapping, particularly in poorly mapped areas situated in inaccessible regions. In the present study, Principal Component Analysis (PCA), Band Rationing (BR) and Minimum Noise Fraction (MNF) algorithms are applied to map lithological units and extract lineaments in the Amezri-Amassine area, by using multispectral ASTER image and global digital elevation model (GDEM) data for the first time. Following preprocessing of ASTER images, advanced image algorithms such as PCA, BR and MNF analyses are applied to the 9ASTER bands. Validation of the resultant maps has relied on matching lithological boundaries and faults in the study area and on the basis of pre-existing geological maps. In addition to the PCA image, a new band-ratio image, 4/6–5/8–4/5, as adopted in the present work, provides high accuracy in discriminating lithological units. The MNF transformation reveals improvement over previous enhancement techniques, in detailing most rock units in the area. Hence, results derived from the enhancement techniques show a good correlation with the existing litho-structural map of the study area. In addition, the present results have allowed to update this map by identifying new lithological units and structural lineaments. Consequently, the methodology followed here has provided satisfactory results and has demonstrated the high potential of multispectral ASTER data for improving lithological discrimination and lineament extraction.

5

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

Berrah Yacine, Chegrouche Aymen, Brahmi Serhane, Boumezbeur Abderrahmane

Geomatics, Landmanagement and Landscape

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2022

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no. 4

95--107

EN

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.

6

A principal component analysis in concrete design

Kobaka Janusz, Katzer Jacek

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

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2022

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Vol. 11

203--219

EN

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.

7

Factors of Quality of Life in a Group of Selected European Union and OECD Countries

Drastichová Magdaléna, Filzmoser Peter

Problemy Ekorozwoju

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2021

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Vol. 16, nr 2

75--93

EN

This work focuses on the evaluation of the factors of quality of life in a sample of 26 countries. Quality of life is a complex, multidimensional concept, which includes various social, cultural, economic, political, demographic and environmental aspects. Regarding this, principal component analysis and regression analysis were chosen as relevant methods to analyse relationships among twenty-five variables related to quality of life, and their rela-tionships with three composite indices reflecting crucial aspects of quality of life, wellbeing and sustainability. These indices, applied as the response variables in the regression analysis, include the inequality-adjusted alter-native of the Human Development Index (IHDI), the Happy Planet Index (HPI), and Healthy Life Years (HLY). The IHDI represents an objective indicator of human development and wellbeing. HLY reflects quality of life in terms of health. The HPI combines the ecological efficiency with which human wellbeing is delivered, while it also includes a subjective measure of wellbeing. Since each of these indices represent different aspects of quality of life to a certain extent, some of the factors (represented by selected indicators) affected them in different ways. After applying a Lasso regression, nine of the 25 indicators – representing crucial factors of quality of life – were identified. Homicide rate (representing the factor of safety) affected all three indices in a negative way, whereas Years in education (representing the factor of education) and Life satisfaction – a subjective indicator of wellbeing representing the dimension of the same name, affected them positively.

PL

Niniejsza praca koncentruje się na ocenie czynników jakości życia na próbie 26 krajów. Jakość życia to złożone, wielowymiarowe pojęcie, które obejmuje różne aspekty społeczne, kulturowe, ekonomiczne, polityczne, demograficzne i środowiskowe. W związku z tym wybrano analizę głównych składowych i analizę regresji jako odpowiednie metody analizy relacji między 25 zmiennymi odnoszącymi się do jakości życia oraz ich związków z trzema złożonymi wskaźnikami odzwierciedlającymi kluczowe aspekty jakości życia, dobrostanu i zrównoważonego rozwoju. Wskaźniki te, stosowane jako zmienne odpowiedzi w analizie regresji, obejmują skorygowaną o nierówności alternatywę wskaźnika rozwoju społecznego (IHDI), wskaźnika szczęśliwej planety (HPI) i wskaźnika lat zdrowego życia (HLY). IHDI stanowi obiektywny wskaźnik rozwoju człowieka i dobrobytu. HLY odzwierciedla jakość życia w kategoriach zdrowia. HPI łączy w sobie efektywność ekologiczną, z jaką zapewnia dobrostan człowieka, a także subiektywną miarę dobrostanu. Ponieważ każdy z tych wskaźników w pewnym stopniu reprezentuje różne aspekty jakości życia, niektóre czynniki (reprezentowane przez wybrane wskaźniki) wpływały na nie w różny sposób. Po zastosowaniu regresji Lasso zidentyfikowano dziewięć z 25 wskaźników – reprezentujących kluczowe czynniki jakości życia. Wskaźnik zabójstw (będący czynnikiem bezpieczeństwa) wpłynął negatywnie na wszystkie trzy wskaźniki, natomiast lata nauki (będące czynnikiem wykształcenia) i zadowolenie z życia – subiektywny wskaźnik dobrostanu reprezentujący wymiar o tej samej nazwie – wpłynęły na nie pozytywnie.

8

An effective data reduction model for machine emergency state detection from big data tree topology structures

Iaremko Iaroslav, Senkerik Roman, Jasek Roman, Lukastik Petr

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 4

601--611

EN

This work presents an original model for detecting machine tool anomalies and emergency states through operation data processing. The paper is focused on an elastic hierarchical system for effective data reduction and classification, which encompasses several modules. Firstly, principal component analysis (PCA) is used to perform data reduction of many input signals from big data tree topology structures into two signals representing all of them. Then the technique for segmentation of operating machine data based on dynamic time distortion and hierarchical clustering is used to calculate signal accident characteristics using classifiers such as the maximum level change, a signal trend, the variance of residuals, and others. Data segmentation and analysis techniques enable effective and robust detection of operating machine tool anomalies and emergency states due to almost real-time data collection from strategically placed sensors and results collected from previous production cycles. The emergency state detection model described in this paper could be beneficial for improving the production process, increasing production efficiency by detecting and minimizing machine tool error conditions, as well as improving product quality and overall equipment productivity. The proposed model was tested on H-630 and H-50 machine tools in a real production environment of the Tajmac-ZPS company.

9

Hybrid GRA-PCA and modified weighted TOPSIS coupled with Taguchi for multi-response process parameter optimization in turning AISI 1040 steel

Mst. Nazma Sultana, Nikhil Ranjan Dhar

Archive of Mechanical Engineering

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2021

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LXVIII, nr 1

23--49

EN

The objective of the present study is to optimize multiple process parameters in turning for achieving minimum chip-tool interface temperature, surface roughness and specific cutting energy by using numerical models. The proposed optimization models are offline conventional methods, namely hybrid Taguchi-GRA-PCA and Taguchi integrated modified weighted TOPSIS. For evaluating the effects of input process parameters both models use ANOVA as a supplementary tool. Moreover, simple linear regression analysis has been performed for establishing mathematical relationship between input factors and responses. A total of eighteen experiments have been conducted in dry and cryogenic cooling conditions based on Taguchi L18 orthogonal array. The optimization results achieved by hybrid Taguchi-GRA-PCA and modified weighted TOPSIS manifest that turning at a cutting speed of 144 m/min and a feed rate of 0.16 mm/rev in cryogenic cooling condition optimizes the multi-responses concurrently. The prediction accuracy of the modified weighted TOPSIS method is found better than hybrid Taguchi-GRA-PCA using regression analysis.

10

Face Recognition Comparative Analysis Using Different Machine Learning Approaches

Ahmed Nisar, Khan Farhan Ajmal, Ullah Zain, Ahmed Hasnain, Shahzad Taimur, Ali Nableela

Advances in Science and Technology. Research Journal

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2021

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Vol. 15, no 1

265--272

EN

The problem of a facial biometrics system was discussed in this research, in which different classifiers were used within the framework of face recognition. Different similarity measures exist to solve the performance of facial recognition problems. Here, four machine learning approaches were considered, namely, K-nearest neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Principal Component Analysis (PCA). The usefulness of multiple classification systems was also seen and evaluated in terms of their ability to correctly classify a face. A combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA was used. All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy, i.e. 98%.

11

Assessment of land surface temperature dynamics over the Bharathapuzha River Basin, India

John Jisha, Chithra N. R., Thampi Santosh G.

Acta Geophysica

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2021

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Vol. 69, no. 3

855--876

EN

Anthropogenic interventions have altered the natural environment and afected many of its physical, chemical, and biological characteristics. Changes in land use-land cover (LULC) are one of the main drivers that alter the hydrologic cycle and cause signifcant impacts on local, regional, and even the global climate system. It is now widely recognised and accepted that climate change is one of the gravest problems that our planet Earth is facing at present. This study analyses the impact of LULC dynamics on the spatial and temporal variation of land surface temperature (LST) in an inter-state river basin, which also happens to be the largest river basin in the state of Kerala, India, viz. the Bharathapuzha river basin, during the period 1990–2017. LST time-series analysis (derived from Landsat) revealed that 98% of the river basin experienced LST less than 298 K in January 1990. Over time, along with changes in LULC, LST also increased; in 2017, about 7.82% of the river basin experienced LST greater than 312 K. A notable change in LULC that occurred during this period was the drastic increase in areas with high albedo. The seasonal curves of LST derived from MODIS data are strong evidence of the devastating impacts of change in LULC on LST and, in turn, on climate change. The major spatial and temporal components of change in LST in the study region were identifed by principal component analysis (PCA). The results of this spatiotemporal analysis spread over a period of 28 years can be used for formulating sustainable development policies and mitigation strategies against extreme climatic events in the river basin.

12

Classification models based on association rules for estimation of key process variables in nuclear power plant

Narasimhan S., Rajendran Velayudham

Journal of Power Technologies

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2020

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Vol. 100, nr 4

315--330

EN

Nuclear power plant process systems have developed great lyover the years. As a large amount of data is generated from Distributed Control Systems (DCS) with fast computational speed and large storage facilities, smart systems have taken over analysis of the process. These systems are built using data mining concepts to understand the various stable operating regimes of the processes, identify key performance factors, makes estimates and suggest operators to optimize the process. Association rule mining is a frequently used data-mining conceptin e-commerce for suggesting closely related and frequently bought products to customers. It also has a very wide application in industries such as bioinformatics, nuclear sciences, trading and marketing. This paper deals with application of these techniques for identification and estimation of key performance variables of a lubrication system designed for a 2.7 MW centrifugal pump used for reactor cooling in a typical 500MWe nuclear power plant. This paper dwells in detail on predictive model building using three models based on association rules for steady state estimation of key performance indicators (KPIs) of the process. The paper also dwells on evaluation of prediction models with various metrics and selection of best model.

13

The influence of the principal component analysis of texture features on the classification quality of sponge tissue images

Dzierżak Róża

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2020

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T. 10, nr 3

13--16

EN

The aim of this article was to determine the effect of principal component analysis on the results of classification of spongy tissue images. Four hundred computed tomography images of the spine (L1 vertebra) were used for the analyses. The images were from fifty healthy patients and fifty patients diagnosed with osteoporosis. The obtained tissue image samples with a size of 50x50 pixels were subjected to texture analysis. As a result, feature descriptors based on a grey level histogram, gradient matrix, RL matrix, event matrix, autoregressive model and wavelet transform were obtained. The results obtained were ranked in importance from the most important to the least important. The first fifty features from the ranking were used for further experiments. The data were subjected to the principal component analysis, which resulted in a set of six new features. Subsequently, both sets (50 and 6 traits) were classified using five different methods: naive Bayesian classifier, multilayer perceptrons, Hoeffding Tree, 1-Nearest Neighbour and Random Forest. The best results were obtained for data on which principal components analysis was performed and classified using 1-Nearest Neighbour. Such an algorithm of procedure allowed to obtain a high value of TPR and PPV parameters, equal to 97.5%. In the case of other classifiers, the use of principal component analysis worsened the results by an average of 2%.

PL

Celem niniejszego artykułu było określenie wpływu analizy głównych składowych na wyniki klasyfikacji obrazów tkanki gąbczastej. Do analiz wykorzystano czterysta obrazów tomografii komputerowej kręgosłupa (kręg L1). Obrazy pochodziły od pięćdziesięciu zdrowych pacjentów oraz pięćdziesięciu pacjentów ze zdiagnozowaną osteoporozą. Uzyskane próbki obrazowe tkanki o wymiarze 50x50 pikseli poddano analizie tekstury. W wyniku tego otrzymano deskryptory cech oparte na histogramie poziomów szarości, macierzy gradientu, macierzy RL, macierzy zdarzeń, modelu autoregresji i transformacie falkowej. Otrzymane wyniki ustawiono w rankingu ważności od najistotniejszej do najmniej ważnej. Pięćdziesiąt pierwszych cech z rankingu wykorzystano do dalszych eksperymentów. Dane zostały poddane analizie głównych składowych wskutek czego uzyskano zbiór sześciu nowych cech. Następnie oba zbiory (50 i 6 cech) zostały poddane klasyfikacji przy użyciu pięciu różnych metod: naiwnego klasyfikatora Bayesa, wielowarstwowych perceptronów, Hoeffding Tree, 1-Nearest Neighbour and Random Forest. Najlepsze wyniki uzyskano dla danych, na których przeprowadzono analizę głównych składowych i poddano klasyfikacji za pomocą 1-Nearest Neighbour. Taki algorytm postępowania pozwolił na uzyskanie wysokiej wartości parametrów TPR oraz PPV, równych 97,5%. W przypadku pozostałych klasyfikatorów zastosowanie analizy głównych składowych pogorszyło wyniki średnio o 2%.

14

Changes in the flow and quality of water in the dam reservoir of the Mała Panew catchment (South Poland) characterized by multidimensional data analysis

Wiatkowski Mirosław, Wiatkowska Barbara

Archives of Environmental Protection

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2019

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Vol. 45, no. 1

26--41

EN

Multidimensional exploratory techniques, such as the Principal Component Analysis (PCA), have been used to analyze long-term changes in the flow regime and quality of water of the lowland dam reservoir Turawa (south-west Poland) in the catchment of the Mała Panew river (a tributary of the Odra). The paper proves that during the period of 1998–2016 the Turawa reservoir was equalizing the river’s water flow. Moreover, various physicochemical water quality indicators were analyzed at three measurement points (at the tributary’s mouth into the reservoir, in the reservoir itself and at the outflow from the reservoir). The water quality assessment was performed by analyzing physicochemical indicators such as water temperature, TSS, pH, dissolved oxygen, BOD5, NH4+, NOˉ3, NOˉ2, N, PO43-, P, electrolytic conductivity, DS, SO42- and Clˉ. Furthermore, the correlations between all these water quality indicators were analyzed statistically at each measurement point, at the statistical signifi cance level of p ≤ 0.05. PCA was used to determine the structures between these water quality variables at each measurement point. As a result, a theoretical model was obtained that describes the regularities in the relationships between the indicators. PCA has shown that biogenic indicators have the strongest infl uence on the water quality in the Mała Panew. Lastly, the differences between the averages of the water quality indicators of the infl owing and of the outflowing water were considered and their signifi cance was analyzed. PCA unveiled structure and complexity of interconnections between river flow and water quality. The paper shows that such statistical methods can be valuable tools for developing suitable water management strategies for the catchment and the reservoir itself.

PL

Eksploracyjne techniki wielowymiarowe, takie jak analiza składowych głównych (PCA), zostały zastosowane w celu analizy wieloletnich (lata 1998-2016) zmian przepływów i jakości wód nizinnego zbiornika zaporowego Turawa (południowo-zachodnia Polska) w zlewni rzeki Mała Panew (dopływ rzeki Odry). W pracy wykazano, że w okresie 1998-2016 zbiornik Turawa w znacznym stopniu wyrównywał przepływy wód rzeki Mała Panew. Analizowano również wskaźniki fizykochemiczne jakości wód na trzech stanowiskach pomiarowych (dopływ do zbiornika, w zbiorniku i na odpływie ze zbiornika). Ocenę jakości wody wykonano analizując wskaźniki fizykochemiczne takie jak: temperaturę wody, zawiesinę ogólną, pH, tlen rozpuszczony,BOD5, NH4+, NOˉ3, NOˉ2, N, PO43-, P, przewodność elektrolityczną, substancje rozpuszczone, siarczany SO42- - i chlorki Clˉ. Analizie statystycznej poddano również związki korelacyjne pomiędzy wszystkimi wskaźnikami jakości wody na poszczególnych stanowiskach pomiarowych, istotne statystycznie na poziomie p<0,05. W celu wykrycia struktur zachodzących między wskaźnikami jakości wody na każdym stanowisku pomiarowym, zastosowano analizę składowych głównych (PCA) (Principal Components Analysis), w efekcie której otrzymano teoretyczny model opisujący prawidłowości w zależnościach między analizowanymi wskaźnikami jakości wód. Analiza składowych głównych (PCA) wykazała, że jakość wody rzeki Mała Panew najsilniej determinowały wskaźniki biogenne. Analizowano również istotność różnic między średnimi stężeniami wskaźników jakości wody dopływającej do zbiornika i wody odpływającej ze zbiornika. Na podstawie zastosowanych metod eksploracyjnej analizy danych możliwe było rozpoznanie struktur i złożoności powiązań zachodzących pomiędzy przepływami wód oraz wskaźnikami jakości wód w rzece Mała Panew. W pracy wykazano, że metody te mogą stanowić niezbędne narzędzie w zakresie podejmowania strategicznych decyzji i rozwiązań w zakresie racjonalnego gospodarowania wodą zarówno w zlewni zbiornika jak i w zbiorniku wodnym.

15

The North Sea Bicycle Race ECG project : time-domain analysis

Długosz D., Eftestøl T., Królak A., Wiktorski T., Ørn S.

Journal of Automation Mobile Robotics and Intelligent Systems

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2018

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Vol. 12, No. 1

23--32

EN

Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual cycling competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming deviations are to be identified in this study. This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering. A double stage analysis strategy, aimed at producing hierarchical clusters, is proposed. The first phase allows rough separation of data. Second stage is applied to reveal internal structure of the majority clusters. In both steps, discrepancies driving the separation could stem from three sources. Firstly, they could be signs of abnormalities in electrical activity of the heart. Secondly, they may allow discriminating between natural groups of participants – according to sex, age, physical fitness. Finally, some deviations could result from faults in data extraction, therefore serving in evaluation of the parameters. The clusters were defined predominantly by combinations of features: heartbeat signals correlation, P-wave shape, and RR intervals; none of the features alone was discriminative for all the clusters.

16

A first arrival detection method for low SNR microseismic signal

Hu R., Wang Y.

Acta Geophysica

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2018

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Vol. 66, no. 5

945--957

EN

Most of the microseismic signals have low signal-to-noise ratio (SNR) due to the strong background noise, which makes it difficult to locate the first arrival time. Both accuracy and stability of conventional methods are poor in this situation. To overcome this problem, here we proposed a new method based on the adaptive Morlet wavelet and principal component analysis process in wavelet coefficients matrix. The three components of microseismic signal make it possible to extract the features in wavelet coefficients domain. Then the reconstructed signal from weighted features presents an obvious first arrival. Tests on synthetic signals and real data provide a solid evidence for its feasibility in low SNR microseismic signal.

17

Prediction model of gas quantity emitted from coal face based on PCA-GA-BP neural network and its application

Qiu J. W., Liu Z. G., Zhou L., Qin R. X.

Journal of Power Technologies

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2017

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Vol. 97, nr 3

169--178

EN

Gas has always been a serious hidden danger in coal mining. The quantity of gas emitted from the coal face is affected by many factors. To overcome the difficulty in accurately predicting the quantity of emission, a novel predictive model (PCA-GABP) based on principal component analysis (PCA), genetic algorithm (GA) and back propagation (BP) neural network was proposed. The model was tested and applied in different coal seams at Panbei Coal Mine in Huainan, China, involving sixteen training samples and four predicting samples. Results showed that: Gas emission quantity was significantly correlated with burial depth, gas content in the mining layer, gas content in the adjacent layer, and layer spacing. The correlations among these variables exceeded 60%. Linear regression analysis using the optimized model was affected by sample size and discreteness. The correlation coefficient (R) and maximum relative error (MRE) of the PCA-GA-BP model were 0.9988 and 3.02%, respectively. The MRE of the optimized model was 70.2% and 53.2% smaller than that of the BP and GA-BP models, respectively. The conclusions obtained in the study provide technical support for the prediction of gas quantity emitted from coal face, and the proposed method can be used in other engineering fields.

18

Volcanic ash cloud detection from MODIS image based on CPIWS method

Liu L., Li Ch., Lei Y., Yin J., Zhao J.

Acta Geophysica

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2017

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Vol. 65, no. 1

151--163

EN

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.

19

Application of ultrafast gas chromatography to recognize odor nuisance

Gȩbicki J., Dymerski T., Namieśnik J.

Environment Protection Engineering

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2016

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Vol. 42, nr 2

97--106

EN

Potentialities of ultrafast gas chromatography applied to periodical monitoring of odor nuisance originating from a municipal landfill have been examined. The results of investigation on classification of the atmospheric air samples collected in a vicinity of the landfill during winter and summer season have been presented. The investigation was performed using ultrafast gas chromatography of Fast/Flash GC type - HERACLES II by Alpha MOS. Data analysis employed principal component analysis (PCA) and linear discriminant function (LDA) supported with the cross-validation method. About 77% of the atmospheric air samples collected during winter season and ca. 87% of the samples collected during summer season were classified correctly. Based on a classification of the atmospheric air samples around the landfill, it can be observed that the biggest number of correctly classified samples originated from the directions characterized by odor nuisance. It was the NW direction during winter season and NE direction during summer season.

20

Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings

Zuber N., Bajrić R.

Eksploatacja i Niezawodność

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2016

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Vol. 18, no. 2

299--306

EN

The article addresses the implementation of feature based artificial neural networks and vibration analysis for automated roller element bearings faults identification purpose. Vibration features used as inputs for supervised artificial neural networks were chosen based on principal component analysis as one of the possible methods of data dimension reduction. Experimental work has been conducted on a specially designed test rig and on a drive of the Ganz port crane in port of Novi Sad, Serbia. Different scalar vibration features derived from time and frequency domain were used as inputs to fault classifiers. Several types of roller elements bearings faults, at different levels of loads were tested: discrete faults on inner and outer race and looseness. It is demonstrated that proposed set of input features enables reliable roller element bearing fault identification and better performance of applied artificial neural networks.

PL

Artykuł omawia zastosowanie sztucznych sieci neuronowych opartych na cechach oraz analizy drgań do celów automatycznej identyfikacji uszkodzeń łożysk tocznych. Cechy drgań mające posłużyć jako dane wejściowe do nadzorowanych sztucznych sieci neuronowych wybrano na podstawie analizy głównych składowych, która stanowi jedną z metod zmniejszania rozmiaru zbioru danych statystycznych. Badania prowadzono na specjalnie do tego celu zaprojektowanym stanowisku badawczym oraz na układzie napędu żurawia portowego firmy Ganz w porcie Novi Sad w Serbii. Jako wejścia klasyfikatorów uszkodzeń wykorzystano różne skalarne cechy drgań określone w dziedzinie czasu i częstotliwości. Badano kilka typów uszkodzeń łożysk tocznych przy różnych poziomach obciążenia: uszkodzenia dyskretne w obrębie pierścienia wewnętrznego i zewnętrznego łożyska oraz nadmierny luz. Wykazano, że proponowany zbiór cech wejściowych umożliwia niezawodną identyfikację uszkodzeń łożysk tocznych oraz zapewnia lepszą wydajność zastosowanych sztucznych sieci neuronowych.