A directional ellipse to describe directional behavior and player activity area in soccer

2021 ◽  
pp. 175433712110247
Author(s):  
Eneko Fernández-Peña ◽  
Julen Castellano ◽  
Stefano Amatori ◽  
Marco BL Rocchi ◽  
Davide Sisti
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Standard Deviation ◽  
Principal Component ◽  
Gps Data ◽  
Independent Variables ◽  
Activity Area ◽  
Ellipse Model ◽  
Independent Variable ◽  
Standard Deviational Ellipse

This study aimed to compare four standard deviational ellipse models to assess directional behavior and player activity area in four small-sided games (SSG) of soccer played on pitches with the same width (40 m) and different lengths (30, 40, 50, and 60 m). Fourteen participants played four 7-a-side SSGs on each of the four pitch sizes. Based on GPS data, four ellipse models were calculated for each outfield player and pitch size: major ranges (MR) measuring standard deviation in fixed length and width directions, linear regression assuming length (LR LvsW) or width (LR WvsL) as the independent variable, and principal component analysis (PCA) assuming both length and width as independent variables. Slope, area, semi-major and semi-minor axes, and eccentricity were calculated for each ellipse model. The PCA and LR LvsW models showed similar and valid results for each variable, especially for larger pitch sizes. LR WvsL showed unreliable results. The length axis should be considered as the independent axis when assessing the main direction of players’ movements and playing area through a standard deviational ellipse in soccer. This methodology could also be applied to evaluate a team’s labor distribution and spatial distribution of its players.

scholarly journals ANALISIS REGRESI KOMPONEN UTAMA UNTUK MENGATASI MASALAH MULTIKOLINIERITAS DALAM ANALISIS REGRESI LINIER BERGANDA

2012 ◽  
Vol 6 (1) ◽  
pp. 31-40
Author(s):  
Gresyea L. Marcus ◽  
Henry J. Wattimanela ◽  
Yopi A. Lesnussa
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Least Square Method ◽  
Component Analysis ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Analysis Technique ◽  
Independent Variable ◽  
Very High

The climate in Ambon, are influenced by sea climate and season climate, cause of this island arrounded by sea, it is make very high rainfall intensity. A very high collinearity between independent variables, make the estimate can not rely be ordinary least square method so it market with not real regretion coefficient and the collinearity. Collinearity can be detected by linier correlation coefficient between independent variables and also with VIF way. Regretion principal component analysis is used to remove collinearity and all of independent variable into model, this analysis is regretion analysis technique wher eare combinated with principal component analysis technique. The object of this analysis is to simplify the variable by overcast it dimension, we can do it removes the correlation between coefficient by transformation. Regresion can help to solve this case rainfall in Ambon on 2010. So the colinearity to independent variables can be overcome and then we can get the best regretion rutes.

scholarly journals Hyperspectral monitoring of fructose content in marzipan

2018 ◽  
Vol 14 (s1) ◽  
pp. 79-88
Author(s):  
Katalin Badak-Kerti ◽  
Szabina Németh ◽  
Andreas Zitek ◽  
Ferenc Firtha
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Spectral Properties ◽  
Oil Content ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Analysis Technique

In our research marzipan samples of different sugar to almond paste ratios (1:1, 2:1, 3:1) were stored at 17 °C. Reducing sugar content was measured by analytical method, texture analysis was done by penetrometry, electric characteristics were measured by conductometry and hyperspectral images were taken 6–8 times during the 16 days of storage. For statistical analyses (discriminant analysis, principal component analysis) SPSS program was used. According to our findings with the hyperspectral analysis technique, it is possible to identify how long the samples were stored (after production), and to which class (ratio of sugar to almond) the sample belonged. The main wavelengths which gave the best discrimination results among the days of storage were between 960 and 1100 nm. The type of the marzipan was easy to distinguish with the hyperspectral data; the biggest differences were observed at 1200 and 1400 nm, which are connected to the first overtone of C-H bound, therefore correlate with the oil content. The spatial distribution of penetrometric, electric and spectral properties were also characteristic to fructose content. The fructose content of marzipan is difficult to measure by usual optical ways (polarimetry, spectroscopy), but since fructose is hygroscopic, the spatial distribution of spectral properties can be characteristic.

scholarly journals Correlation of environmental factors and spatial distribution of moluscs communities in mangrove reboisation areas of Seribu Islands, Indonesia

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Syahrial Syahrial ◽  
Eryc Pranata ◽  
Hendri Susilo
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Environmental Factors ◽  
Principal Component ◽  
Quality Standard ◽  
Small Plot ◽  
Line Transects ◽  
Relationship Of ◽  
The Relationship ◽  
Mangrove Reforestation

Mangrove reforestation is often carried out in various regions or regions, but information about the relationship of environmental factors and the distribution of fauna associations is still very minimal. The Principal Component Analysis (PCA) study on the correlation of environmental factors and the spatial distribution of the molusks community in the Seribu Islands mangrove reforestation area was conducted in March 2014 with the aim of analyzing environmental factors for the diversity and presence of the molusks. Environmental factors are measured insecurely, while the moluccan community is collected by making line transects and plots measuring 10 x 10 m2 and in the size of 10 x 10 m2, a small plot of 1 x 1 m2 is made. The results of the study show that environmental factors are not so different between stations and do not exceed the quality standard for the lives of 4 species of mollusks, where the parameters of aquatic pH are the environmental factors that most influence their distribution.Keywords: environmental factors, distribution, mollusks community, mangrove reforestation, Seribu Islands

scholarly journals Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wenjing Zhao ◽  
Yue Chi ◽  
Yatong Zhou ◽  
Cheng Zhang
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Component Analysis ◽  
Noise Estimation ◽  
Noise Standard Deviation ◽  
Estimation Algorithms

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

scholarly journals Attitude Towards Zakah on Employment Income : Comparing Outcomes Between Single Score and Multidimensional Scores

2020 ◽  
Author(s):  
Kamil Md Idris ◽  
Ahmad Mahdzan Ayob
Keyword(s):  
Principal Component Analysis ◽  
Goodness Of Fit ◽  
Principal Component ◽  
Complex Object ◽  
Factor Scores ◽  
Multivariate Logistic Regression ◽  
Independent Variables ◽  
Compliance Behavior ◽  
Single Score ◽  
The Relationship

Study on attitude towards regulated social activities have been carried out in many areas (such as tax and zakah payment). However, many of these studies applied a single score of attitude in their analyses. Such a procedure, to some researchers is considered less informative, especially in the study of a complex attitude which has several dimensions. Many researchers have suggested that attitude towards a complex object should be studied by decomposing the object or issue into smaller and less complex elements on the basis of component parts, specific functions, or particular contexts. Thus, this paper offers a comparative study of outcomes between attitude measured by a single summative score and attitude measured by multidimensional factor scores. The object of attitude in this paper is zakah on employment income by eligible Muslim. In the first approach, a total of 24 items of attitude were used to represent the single score of attitude. In the second approach, principal component analysis with varimax ratation was first applied to determine the underlying dimensions of attitude. Each dimension was then named and treated as anew variable, each measured by the factor scores. Both approach were applied separately to an analysis on compliance behavior of zakah on employment income. Results suggest that attitude measured by multidimensionality scores is more informative as compared to the single summative score. Futher, the use of multidimensional scores in multivariate logistic regression improved the goodness of fit of the model over that of the single score of attitude. Thus, this improvement affects the interpretation of the whole model with respect to the relationship between the independent variables and the dependent variable, which is zakah compliance.  

scholarly journals The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

2021 ◽  
Vol 13 (24) ◽  
pp. 13859
Author(s):  
Shu Wu
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Forest Fire ◽  
Forest Fires ◽  
Inner Mongolia ◽  
Information Diffusion ◽  
Principal Component ◽  
Component Analysis ◽  
Expected Number

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.

CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240032 ◽  
Author(s):  
S. VINITHA SREE ◽  
DHANJOO N. GHISTA ◽  
KWAN-HOONG NG
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Nervous System ◽  
Standard Deviation ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Cardiac Cells ◽  
Ecg Signal ◽  
Mean Values

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

scholarly journals Magnetic properties of surface sediments in Schirmacher Oasis, East Antarctica: spatial distribution and controlling factors

2020 ◽  
Author(s):  
Anish Kumar Warrier ◽  
Joju George Sebastian ◽  
K. Amrutha ◽  
A. S. Yamuna Sali ◽  
B. S. Mahesh ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Grain Size ◽  
Magnetic Properties ◽  
Spatial Distribution ◽  
Iron Oxide ◽  
Iron Oxides ◽  
Remanent Magnetization ◽  
Principal Component ◽  
East Antarctica ◽  
Schirmacher Oasis

Abstract Purpose We investigated the magnetic properties (abundance, grain size, and mineralogy) of iron oxides present in Lake L-55 sediments, Schirmacher Oasis, East Antarctica, with an aim to understand their spatial distribution and the underlying mechanisms that control their formation and distribution. Methods Twenty-five surficial sediments retrieved from different parts of Lake L-55 were subjected to the entire range of environmental magnetic (magnetic susceptibility, anhysteretic remanent magnetization (ARM), isothermal remanent magnetization (IRM)) measurements (at different field strengths). Inter-parametric ratios (χARM/SIRM, χARM/χlf, χARM/χfd, IRM20 mT/SIRM, IRM20 mT/ARM, S-ratio, L-ratio) provided insights into the magnetic properties (abundance, grain size, and mineralogy of iron oxides). Scanning electron microscopic-energy dispersive X-ray spectroscopic (SEM-EDS) analysis was performed on magnetic extracts from a few sediments. Besides, organic matter (%) was also calculated for the sediment samples. Principal component analysis was performed to gain information on the presence of different components and their relative dominance. Results The iron oxides are strongly magnetic (high values of concentration-dependent parameters). The principal iron oxide is magnetite (S-ratio > 0.90) which is coarse-grained (multi-domain (MD) and stable single-domain (SSD) grains), and there is no influence of authigenic greigite, bacterial magnetite, and anthropogenic magnetite. The mineralogy is confirmed by SEM-EDS data. The iron oxides are of different grain sizes, and their contribution is in the order of MD > SSD > SP as shown by the principal component analysis. Pedogenic iron oxide minerals seem to be present in the samples whose formation is due to the oxidation of magnetite into hematite. However, they are of SSD size and not SP, suggesting that the intensity of pedogenesis is not sufficient to form SP grains. Conclusion The iron oxide minerals are mainly terrigenous, and the biogenic activity within the lake is not sufficient to modify the ferrimagnetic minerals. Spatial distribution patterns suggest the non-uniform distribution of magnetite/titanomagnetite of varying sizes in the lake basin which is transported by both melt water streams and winds.

A Real-Time Combustion Control With Reconstructed In-Cylinder Pressure by Principal Component Analysis for a CRDI Diesel Engine

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Jaesung Chung ◽  
Junhyeong Oh ◽  
Myoungho Sunwoo
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Real Time ◽  
Principal Components ◽  
Control Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Cylinder Pressure ◽  
Combustion Control ◽  
Pca Method

This paper proposes a real-time combustion control algorithm using reconstructed in-cylinder pressure traces by principal component analysis (PCA). The PCA method reconstructs the in-cylinder pressure traces using the principal components of the in-cylinder pressure traces. It was shown that using only five principal components, we were able to reconstruct the in-cylinder pressure traces within 1% root mean squared percent error. Furthermore, the reconstructed in-cylinder pressure traces were validated to effectively reduce the cycle-to-cycle variations caused by the noise signals. As a result, the standard deviation of MFB50 which was calculated from the reconstructed in-cylinder pressure was reduced by 45%. Furthermore, this combustion parameter was applied to a real-time combustion control. Since variations of the control variables for the real-time combustion control were reduced, the control performances were enhanced.

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