scholarly journals A holistic decision-making approach for identifying influential parameters affecting sustainable production process of canola bast fibres and predicting end-use textile choice using principal component analysis (PCA)

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06235
Author(s):  
Ikra Iftekhar Shuvo
Keyword(s):  
Decision Making ◽  
Principal Component Analysis ◽  
Production Process ◽  
Principal Component ◽  
Component Analysis ◽  
Sustainable Production ◽  
End Use ◽  
Influential Parameters

Introducing a Taxonomy for Classifying Qualitative Spreadsheet Errors

2015 ◽  
Vol 27 (1) ◽  
pp. 33-56 ◽  
Author(s):  
Linda Leon ◽  
Zbigniew H. Przasnyski ◽  
Kala Chand Seal
Keyword(s):  
Decision Making ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
End Users ◽  
Future Research ◽  
Spreadsheet Model ◽  
Spreadsheet Models ◽  
Mission Critical

Most organizations use large and complex spreadsheets that are embedded in their mission-critical processes and are used for decision-making. Identification of the various types of errors that can be present in these spreadsheets is, therefore, an important first step to creating controls that organizations can use to govern their spreadsheets. While a considerable amount of research on quantitative error taxonomies exists, there is comparatively little research concerning qualitative error taxonomies. In this paper, we propose a taxonomy for categorizing qualitative errors in spreadsheet models that offers an exploratory framework for evaluating the quality of a spreadsheet model before it is released for use by others in the organization. The classification was developed based on types of qualitative errors identified in the literature and errors committed by end-users in developing a spreadsheet model for Panko's (1996) “Wall Problem.” A principal component analysis of the errors reveals four logical groupings thereby creating four categories of qualitative errors. The usability and limitations of the proposed taxonomy and areas for future research are discussed.

scholarly journals Determining Youth Profile using Principle Component Analysis for Identifying Talent in Sports

2019 ◽  
Vol 8 (2S7) ◽  
pp. 212-215
Keyword(s):  
Decision Making ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Factor Loading ◽  
Component Analysis ◽  
Talent Identification ◽  
Sitting Height ◽  
Data Analyses ◽  
Program Data ◽  
High Factor Loading

This study attempts to determine youth profile in sports talent identification program. Data of anthropometric and physical fitness included power, agility, speeds, flexibility, strength and endurance were obtained from 600 participants in a sports talent identification program aged 13-15 years. Data analyses were carried out using multivariate analysis of principal component analysis (PCA). PCA revealed three principal components with 71.5% total variation for this studied group. The first component consists of high factor loading in speed variables (10 meter run, 20 meter run, 40 meter run) and endurance (predicted VO₂ max). The second component was constituted by anthropometric variables (weight, height, sitting height and armspan). While third component contains flexibility variable (sit and reach). These selected variables of anthropometric and fitness are, therefore, revealed as the essential attributes those must be prioritized for a talent scouting in sports. Present results had demonstrated youth profile that capable of providing an information that could help coaches in decision making during athlete selection in sports talent identification.

Research on Nonlinear Process Monitoring and Fault Diagnosis Based on Kernel Principal Component Analysis

2009 ◽  
Vol 413-414 ◽  
pp. 583-590 ◽  
Author(s):  
Fei He ◽  
Min Li ◽  
Jian Hong Yang ◽  
Jin Wu Xu
Keyword(s):  
Principal Component Analysis ◽  
Production Process ◽  
Statistical Process Control ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process

In order to monitor nonlinear production process effectively, multivariate statistical process control based on kernel principal component analysis is applied to process monitoring and diagnosis. Squared prediction error (SPE) statistic of the kernel principal component analysis (KPCA) model is used for process monitoring, and the fault causes of the production process could be tracked by the methods of data reconstruction and the optimal neighbor selection strategy. Simulation data and Tennessee Eastman process data are used for model validation, as a result the proposed method has better performance on abnormality detecting, compared with multivariate statistical process control based on linear principal component analysis. What is more, the causes of the faults are tracked effectively, thus the production process can be adjusted to prevent substandard products.

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