scholarly journals K-means Based Soil Classification System Applicable to a Brazilian Mineral Province

2021 ◽  
Author(s):  
Pablo dos Santos Cardoso Coelho ◽  
Gustavo Henrique Nogueira ◽  
Leonardo Alberto Sala ◽  
Tatiana Barreto Santos
Keyword(s):  
Principal Component Analysis ◽  
Classification System ◽  
Principal Component ◽  
Soil Classification ◽  
Test Results ◽  
Geotechnical Parameters ◽  
Geotechnical Characterization ◽  
Laboratory Test Results ◽  
Quadrilátero Ferrífero ◽  
Using Data

Abstract This article presents a geotechnical soil classification system proposed for application on soils of a tropical mineral province, located in Minas Gerais state, Brazil. The system was constructed using data mining techniques, i.e., principal component analysis and k-means cluster analysis, which were applied to a dataset composed of 101 geotechnical characterization laboratory test results of soils from the Province of Quadrilátero Ferrífero. The main objective of the proposed soil classification method was to establish a regional soil classification system, which encompass the interpretability of the main geotechnical parameters of soils by means of the classification, given the little explanatory capacity of the Unified Soil Classification System classification system for the performance of such task. It was possible to establish a chart for soil classification capable of explaining 81.68% of the variability of the analyzed parameters, being established the soil classes A, B and C for the studied soils.

scholarly journals A phenetic study of the genus Rasahus Amyot & Serville (Heteroptera, Reduviidae)

1990 ◽  
Vol 1 (3) ◽  
pp. 131-144
Author(s):  
María Coscarón
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Morphological Characters ◽  
Basal Plate ◽  
Using Data ◽  
Diagnostic Importance

Cluster analysis by four methods and a principal component analysis were performed using data on 24 morphological characters of 27 species of the genus Rasahus (Peiratinae). The results obtained by the different techniques show general agreement. They confirm the present number of taxa and reveal the existence within the genus of three groups of species: scutellaris , hamatus and vittatus. The scutellaris group is constituted by R. aeneus (Walker), R. maculipennis (Lepelletier and Serville), R. bifurcatas Champion, R. castaneus Coscarón, R. guttatipennis (Stål), R. flavovittarus Stål, R. costarricensis Coscarón, R. scutellaris (Fabricius), R. atratus Coscarón, R. peruensis Coscarón, R. paraguayensis Coscarón, R. surinamensis Coscarón, R. albomaculatus Mayr, R. brasiliensis Coscarón and R. sulcicollis (Serville).The hamatus group contains R. rufiventris (Walker), R. hamatus (Fabricius), R. amapaensis Coscarón, R. arcitenens Stål, R. limai Pinto, R. angulatus coscarón, R. thoracicus Stål, R. biguttatus (Say), R. arcuiger (Stål), R. argentinensis Coscarón and R. grandis Fallou. The vittatus group contains R. vittatus Coscarón. The characters used to separate the groups of species are: shape of the pygophore, shape of the parameres, basal plate complexity, shape of the postocular region and hemelytra pattern. Illustrations of the structures of major diagnostic importance are included.

scholarly journals EVALUATING MBSE POTENTIAL USING PRODUCT AND DEVELOPMENT CHARACTERISTICS – A STATISTICAL INVESTIGATION

2020 ◽  
Vol 1 ◽  
pp. 2385-2394
Author(s):  
M. Schöberl ◽  
E. Rebentisch ◽  
J. Trauer ◽  
M. Mörtl ◽  
J. Fottner
Keyword(s):  
Principal Component Analysis ◽  
Systems Engineering ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Investigation ◽  
Product Complexity ◽  
Organizational Complexity ◽  
Model Based ◽  
Using Data ◽  
Model Based Systems Engineering

AbstractAs model-based systems engineering (MBSE) is evolving, the need for evaluating MBSE approaches grows. Literature shows that there is an untested assertion in the MBSE community that complexity drives the adoption of MBSE. To assess this assertion and support the evaluation of MBSE, a principal component analysis was carried out on eight product and development characteristics using data collected in an MBSE course, resulting in organizational complexity, product complexity and inertia. To conclude, the method developed in this paper enables organisations to evaluate their MBSE adoption potential.

scholarly journals Principal component analysis of performance test traits in Hungarian Sporthorse mares

2007 ◽  
Vol 50 (2) ◽  
pp. 125-135
Author(s):  
J. Posta ◽  
I. Komlósi ◽  
S. Mihók
Keyword(s):  
Principal Component Analysis ◽  
Performance Test ◽  
Principal Component ◽  
Test Results ◽  
Principal Component Analyses ◽  
Performance Traits ◽  
Probable Consequence ◽  
Highly Correlated ◽  
Analysis Of Performance ◽  
Trait Groups

Abstract. The analysis utilized data on performance traits recorded between 1993 and 2004 on 3- and 4-year-old Hungarian Sporthorse mares. Traits were categorized in three groups, chosen to describe conformation, free jumping and movement. Low to moderate correlations were found among traits within each of those groups. There were high correlations between type and frame for both ages; and within free jumping performance traits, jumping style and jumping ability were highly correlated as well. In principal component analyses of test results for 3- and 4-year-old mares, 9 factors (ratio of variance = 80.935) and 7 factors (ratio of variance = 74.115) were identified, respectively. Dendograms based upon cluster analysis verified the separation of trait groups. The trait of "impulsion in elasticity of movement" could be assigned to movement traits in 3-year-old; but as a probable consequence of training, it could be assigned to conformation traits, especially to overall impression, when horses were 4-year-old.

scholarly journals Spacecraft Actuator Diagnosis with Principal Component Analysis: Application to the Rendez-Vous Phase of the Mars Sample Return Mission

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Othman Nasri ◽  
Imen Gueddi ◽  
Philippe Dague ◽  
Kamal Benothman
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Isolation ◽  
Test Results ◽  
Sample Return ◽  
Rotation Rates ◽  
Analysis Application ◽  
Sample Return Mission

This paper presents a fault detection and isolation (FDI) approach in order to detect and isolate actuators (thrusters and reaction wheels) faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. The principal component analysis (PCA) has been adopted to estimate the relationships between the various variables of the process. To ensure the feasibility of the proposed FDI approach, a set of data provided by the industrial “high-fidelity” simulator of the MSR and representing the opening (resp., the rotation) rates of the spacecraft thrusters (resp., reaction wheels) has been considered. The test results demonstrate that the fault detection and isolation are successfully accomplished.

Learning Principal Component Analysis by Using Data from Air Quality Networks

2017 ◽  
Vol 94 (4) ◽  
pp. 458-464 ◽  
Author(s):  
Luis Vicente Pérez-Arribas ◽  
María Eugenia León-González ◽  
Noelia Rosales-Conrado
Keyword(s):  
Principal Component Analysis ◽  
Air Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Using Data

scholarly journals Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhaoqin Peng ◽  
Chun Cao ◽  
Qiusheng Liu ◽  
Wentao Pan
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Human Walking ◽  
Support Vector ◽  
Walking Pattern ◽  
Using Data

Algorithms based on the ground reflex pressure (GRF) signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA) and kernel principal component analysis (KPCA) for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM), SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

Sign in / Sign up

Export Citation Format

Share Document