scholarly journals Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality® Protocol to Determine Main Components of Behavior

2021 ◽  
Vol 2 ◽  
Author(s):  
Lena Friedrich ◽  
Joachim Krieter ◽  
Nicole Kemper ◽  
Irena Czycholl
Keyword(s):  
Principal Component Analysis ◽  
Exploratory Behavior ◽  
Principal Component ◽  
Component Analysis ◽  
Qualitative Behavior ◽  
Behavioral Indicators ◽  
Independent Variables ◽  
Variable Reduction ◽  
Welfare Quality ◽  
Main Components

Understanding behavior is important in terms of welfare assessments to be able to evaluate possible changes in behavior among different husbandry systems. The present study applied principal component analysis (PCA) to reveal relationships between behavioral indicators to identify the main components of sows' behavior promoting feasibility of welfare assessments by providing possibilities for variable reduction and aggregation. The indicators of the Welfare Quality® protocol's principle to assess behavior were repeatedly applied by two observers on 13 farms in Northern Germany. This included Qualitative Behavior Assessments (QBA) to evaluate animals' body language using 20 pre-defined adjectives, assessments of social and exploratory behavior, stereotypies, and human–animal relationship tests. Two separate PCA were performed with respect to the QBA: (1) adjectives were included as independent variables and (2) adjectives were pre-aggregated using the calculation rules of the Welfare Quality® protocol for fattening pigs since a calculation for sows does not yet exist. In both analyses, two components described sows' behavior. Most variance was explained by the solution with adjectives as independent variables (51.0%). Other behavioral elements not captured as indicators by the protocol may still be important for all-inclusive welfare assessments as the required variance of 70% was not achieved in the analyses. Component loadings were used to determine components' labels as (1) “satisfaction of exploratory behavior” and (2) “social resting”. Both components reflected characteristics of sows' natural behavior and can subsequently be used for variable reduction but also for development of component scores for aggregation. As defined for PCA, component 1 explained more variance than component 2. PCA is useful to determine the main components of sows' behavior, which can be used to enhance feasibility of welfare assessments.

scholarly journals Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Principal Component Analysis ◽  
Optic Disc ◽  
Retinal Image ◽  
Principal Component ◽  
Component Analysis ◽  
Retinal Images ◽  
Image Area ◽  
Main Components ◽  
Vessel Tree ◽  
Optic Discs

A retinal image has blood vessels, optic disc, fovea, and so forth as the main components of an image. Segmentation of these components has been investigated extensively. Principal component analysis (PCA) is one of the techniques that have been applied to segment the optic disc, but only a limited work has been reported. To our knowledge, fovea segmentation problem has not been reported in the literature using PCA. In this paper, we are presenting the segmentation of optic disc and fovea using PCA. The PCA was trained on optic discs and foveae using ten retinal images and then applied on seventy retinal images with a success rate of 97% in case of optic discs and 94.3% in case of fovea. Conventional algorithms feed one patch at a time from a test retinal image, and the next patch separated by one pixel part is fed. This process is continued till the full image area is covered. This is time consuming. We are suggesting techniques to cut down the processing time with the help of binary vessel tree of a given test image. Results are presented to validate our idea.

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 Chemical Composition and Variability of Leaf and Berry oils from Corsican Juniperus macrocarpa

2018 ◽  
Vol 13 (2) ◽  
pp. 1934578X1801300
Author(s):  
Joséphine Ottavioli ◽  
Ange Bighelli ◽  
Joseph Casanova ◽  
Félix Tomi
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Chemical Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Germacrene D ◽  
Leaf Oil ◽  
Main Components ◽  
Main Component ◽  
C Nmr

The chemical composition of five leaf oil samples and eighteen berry oil samples from Corsican Juniperus macrocarpa have been investigated by GC(RI), GC-MS and 13C NMR. The composition of berry oils was dominated by monoterpene hydrocarbons with α-pinene (56.4-78.9%) as main component followed by myrcene (2.2-11.9%). Germacrene D (4.5-103%) was the major sesquiterpene. The contents of the main components of leaf oils varied drastically from sample to sample: α-pinene (28.7-76.4%), δ3-carene (up to 17.3%), β-phellandrene (up to 12.3%), manoyl oxide (up to 8.1%). The occurrence of the unusual ( Z)-pentadec-6-en-2-one (0.1-1.2%) should be pointed out. Statistical analysis (Principal Component Analysis and k- means partition) suggested a unique group with atypical samples.

scholarly journals Principal component analysis for human gait recognition system

2019 ◽  
Vol 8 (2) ◽  
pp. 569-576
Author(s):  
Othman O. Khalifa ◽  
Bilal Jawed ◽  
Sharif Shah Newaj Bhuiyn
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Gait Recognition ◽  
Principal Component ◽  
Initial Step ◽  
Component Analysis ◽  
Recognition System ◽  
Human Gait ◽  
The One ◽  
Main Components

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

scholarly journals Detection of structural alterations in metal bodies: An approximation using Fourier transform and principal component analysis (PCA)

2020 ◽  
Vol 25 (2) ◽  
pp. 255-260
Author(s):  
Brayan Eduardo Tarazona ◽  
Camilo Leonardo Sandoval R ◽  
Carlos Gerardo Cárdenas Arias ◽  
Javier Gonzalo Ascanio V. ◽  
Jhon Jairo Valencia N.
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Standard Deviation ◽  
Mechanical Vibration ◽  
Principal Component ◽  
Component Analysis ◽  
Repeated Measurements ◽  
Statistical Parameters ◽  
Structural Alterations ◽  
Main Components

In this theme some advances have been developed, verified in the background, where attempts have been made to determine the existence of structural alterations such as perforations, defective welding and dents in metal structures; a pattern of mechanical vibration that allows to differentiate each alteration has not yet been clearly defined. In this work, the data taking was carried out taking into account the position of the sensors, two beams were added without alteration, in order to be able to interact with the five configurations, which were adopted for the experimental design.  To the tests of repeated measurements, in each configuration, analysis (ANOVA) was used for the validation of NULL hypotheses, and thus to determine the number of test to be treated. After having the defined matrices representing each configuration, in each anomaly, it is necessary to apply the principal component Analysis (PCA), to the data obtained by the calculation of the fast Fourier transform (FFT). And thus determine the number of components by means of three Criteria (Jollife, Kaiser and PVA), using a classification algorithm, which evaluates the percentage of classification vs lower standard deviation. In this analysis the descriptors were not calculated but the main components of each criterion were taken as a description tool.  The process of extraction of characteristics was fundamental to determine the proper configuration in each alteration (fissure, welded, perforated, deformed).  On the other hand, statistical parameters were calculated (average, standard deviation, variation factor, Euclidean distance) of each anomaly. Taking as descriptors. 

scholarly journals Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse

2018 ◽  
Vol 15 (2) ◽  
pp. 44
Author(s):  
Georgina M. Tinungki ◽  
Nurtiti Sunusi
Keyword(s):  
Principal Component Analysis ◽  
Sparse Matrix ◽  
Principal Component ◽  
Component Analysis ◽  
Sparse Principal Component Analysis ◽  
Sparse Pca ◽  
Variable Reduction ◽  
Zero Entry ◽  
Pca Method ◽  
New Variables

Abstract Sparse Principal Component Analysis (Sparse PCA) is one of the development of  PCA. Sparse PCA modifies new variables as a linier combination of  p old variables (original variable) which  is yielded by PCA method. Modifying new variables is conducted by producing a loading yang sparse matrix, such that old variable which is not effective (value of loading is zero) able be exit from PCA.  In this study, Sparse PCA method was applied on data of Indonesia Poverty population in 2015, that contains 13 variables and 34 observation with variable reduction such that yields 4 (four) new variables, which can explain 80.1% of total variance data. This study show, the loading matrix that has been yielded by using Sparse PCA method to become sparse with there exist 11 elements (loading value) zero entry of matrix, such that the model that has been produced to be simpler and easy to be interpreted. Keywords:  Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse Abstrak Sparse Principal Component Analysis (Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari  variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan. Kata Kunci: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse

scholarly journals Assessment of Under Five Child Mortality in Tanzania Mainland Based on Principal Component Analysis

2019 ◽  
pp. 1-13
Author(s):  
Privatus Christopher
Keyword(s):  
Principal Component Analysis ◽  
Child Mortality ◽  
Vitamin A Deficiency ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Set ◽  
Under Five ◽  
Long Time ◽  
Main Components

Deaths of children younger than 5 years has been a global problem for long time. This study is focused on evaluating diseases that caused under five child mortality in Tanzania in 2013. Diseases that causes child mortality were collected from 25 regions and analysed for 42 disease variables. The data obtained were standardized and subjected to principal component analysis (PCA) to define the diseases responsible for the variability in child mortality. PCA produced seven significant main components that explain 73:40% of total variance of the original data set. The results reveal that Thyroid Diseases, Snake and Insect Bites, Vitamin A Deficiency /Xerophthalmia, Eye Infections, Schistosomiasis (SS), Intestinal Worms, Ear Infections, Haematological Diseases, Diabetes Mellitus, Ill Defined Symptoms no Diagnosis, Poisoning, Anaemia, HIV/AIDS, Burns, Rheumatic Fever, Bronchial Asthma, Peri-natal conditions and Urinary tract infection are most significant diseases in assessing under five child mortality in Tanzania mainland. This study suggest that PCA technique is useful tool for identification of important diseases that causes death of children less than five years.

Principal Component Analysis of Structure Model Indexes on Composite Friction Materials

2014 ◽  
Vol 1004-1005 ◽  
pp. 459-463
Author(s):  
Jian Hua Du ◽  
Shi Meng Xu ◽  
Run Bo Ma ◽  
Lei Gong
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
General Rule ◽  
Element Model ◽  
Component Analysis ◽  
Friction Material ◽  
Basic Element ◽  
Structure Model ◽  
Friction Materials ◽  
Main Components

For the copper-based composite friction material, the statistical models of the element structure were given in this paper to pick up the main components, that was, through the principal component analysis on basic element model of surface structure, the two kinks of main indicators or indexes are ascertained can be found from the original four indicators. Here, the main components were the area radio and long radius, the others among the four were short radius, inclination. Moreover, the indicator reduction criterion was put forward as a general rule by this paper.

Exploring Automated Concept Generator Output Through Principal Component Analysis

2010 ◽  
Author(s):  
Kerry R. Poppa ◽  
Robert B. Stone ◽  
Seth Orsborn
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Preliminary Evidence ◽  
Design Problems ◽  
Principal Solution ◽  
Large Sets ◽  
Variable Reduction ◽  
Poorly Differentiated ◽  
Generator Output

During conceptual design it is desirable to produce many potential solutions. Recently, computational tools have emerged to help designers more fully explore possible solutions. These automated concept generators use knowledge from existing products and the desired functionality of the new design to suggest solutions. While research has shown these tools can increase the variety of solutions developed, they often provide unmanageably large sets of poorly differentiated results. This work proceeds from the hypothesis that automated concept generator output includes many permutations of a relatively few principal solution variants. A method to discover these underlying solution types from the initial concept generator output is proposed. The proposed method employs principal component analysis for variable reduction followed by cluster analysis for classification. The method is applied to the automatically generated solutions of three sample design problems. Preliminary evidence of the utility and efficiency of the proposed method is presented based upon those sample problems. Finally, a method for extending the proposed technique to much larger solution sets is discussed.

scholarly journals The Main Component of Smart Tourism: A Principal Component Analysis Approach

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 295-307
Author(s):  
Ruci Meiyanti ◽  
Dana Indra Sensuse ◽  
Yudho Giri Sucahyo
Keyword(s):  
Principal Component Analysis ◽  
Information And Communication Technologies ◽  
Principal Component ◽  
Component Analysis ◽  
Successful Implementation ◽  
Information And Communication ◽  
Main Components ◽  
Main Component ◽  
Development And Management ◽  
Smart Tourism

Introduction. The establishment of a smart tourism model is indispensable for the effectiveness of tourism development and management supported by advances in information and communication technology. The successful implementation of smart tourism is determined by the smart tourism model. Determination of the main components has a significant role in building an effective model. The purpose of this study, to determine the main components forming a smart tourism model using Principal Component Analysis (PCA). PCA is used to compress variables by reducing the number of dimensions, without losing much information. Method This research method is a quantitative method using SPSS software version 22. Result. The result obtained 9 main components, namely stakeholders, infrastructure, goals, resources, activities, challenges, innovation in various applications, strategies, and use of information and communication technologies. Conclusion. Those main components are expected to construct a smart tourism model with components that are comprehensive, interrelated, and adaptable.

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