scholarly journals Monitoring multivariate-attribute quality characteristics in two stage processes using discriminant analysis based control charts

2016 ◽  
Vol 23 (2) ◽  
pp. 757-767
Author(s):  
S. Zolfaghari ◽  
Amirhossein Amiri
Keyword(s):  
Discriminant Analysis ◽  
Control Charts ◽  
Quality Characteristics ◽  
Two Stage ◽  
Attribute Quality

scholarly journals Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1838
Author(s):  
Muhammad Ahsan ◽  
Muhammad Mashuri ◽  
Wibawati ◽  
Hidayatul Khusna ◽  
Muhammad Hisyam Lee
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Synthetic Data ◽  
Principal Component ◽  
Quality Characteristics ◽  
Kernel Functions ◽  
Quality Characteristic ◽  
Kernel Pca ◽  
Asymmetric Pattern ◽  
Attribute Quality

The need for a control chart that can visualize and recognize the symmetric or asymmetric pattern of the monitoring process with more than one type of quality characteristic is a necessity in the era of Industry 4.0. In the past, the control charts were only developed to monitor one kind of quality characteristic. Several control charts were created to deal with this problem. However, there are some problems and drawbacks to the conventional mixed charts. In this study, another approach is used to monitor mixed quality characteristics by applying the Kernel Principal Component Analyisis (KPCA) method. Using the Hotelling’s T2 statistic, the kernel PCA mix chart is proposed to simultaneously monitor the variable and attribute quality characteristics. Due to its ability to estimate the asymmetric pattern of the mixed process, the kernel density estimation (KDE) used in the proposed chart has successfully estimated the control limits that produce ARL0 at about 370 for α=0.00273. Through several experiments based on the proportion of the attribute characteristics and kernel functions, the proposed chart demonstrates better performance in detecting outlier and shift in the process. When it is applied to monitor the synthetic data, the proposed chart can detect the shift accurately. Additionally, the proposed chart outperforms the performance of the conventional mixed chart based on PCA mix by producing lower false alarm with more accurate detection of out of control processes.

scholarly journals Synthetic control charts with two-stage sampling for monitoring bivariate processes

2007 ◽  
Vol 27 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Antonio F. B. Costa ◽  
Marcela A. G. Machado
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Quality Characteristics ◽  
Synthetic Control ◽  
Two Stage ◽  
Variable Sample Size ◽  
Second Stage ◽  
Process Disturbances

In this article, we consider the synthetic control chart with two-stage sampling (SyTS chart) to control bivariate processes. During the first stage, one item of the sample is inspected and two correlated quality characteristics (x;y) are measured. If the Hotelling statistic T1² for these individual observations of (x;y) is lower than a specified value UCL1 the sampling is interrupted. Otherwise, the sampling goes on to the second stage, where the remaining items are inspected and the Hotelling statistic T2² for the sample means of (x;y) is computed. When the statistic T2² is larger than a specified value UCL2, the sample is classified as nonconforming. According to the synthetic control chart procedure, the signal is based on the number of conforming samples between two neighbor nonconforming samples. The proposed chart detects process disturbances faster than the bivariate charts with variable sample size and it is from the practical viewpoint more convenient to administer.

Quality Characteristics of High Acidity Apple Vinegar Manufactured Using Two Stage Fermentation

2014 ◽  
Vol 43 (6) ◽  
pp. 877-883 ◽  
Author(s):  
Na-Hye Sung ◽  
Seung-Mi Woo ◽  
Joong-Ho Kwon ◽  
Soo-Hwan Yeo ◽  
Yong-Jin Jeong
Keyword(s):  
Quality Characteristics ◽  
Two Stage ◽  
High Acidity

SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART

2012 ◽  
Vol 12 (04) ◽  
pp. 1250083
Author(s):  
PERSHANG DOKOUHAKI ◽  
RASSOUL NOOROSSANA
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
High Performance ◽  
Discrete Data ◽  
Correlated Data ◽  
Statistical Process ◽  
Control Charting ◽  
Attribute Quality ◽  
Related Process

In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

Scoring to estimate the financial statement fraud risk during the audit

2021 ◽  
Vol 22 (11) ◽  
pp. 1262-1275
Author(s):  
Sergei V. ARZHENOVSKII ◽  
Tat'yana G. SINYAVSKAYA ◽  
Andrei V. BAKHTEEV
Keyword(s):  
Discriminant Analysis ◽  
Statistical Method ◽  
Empirical Data ◽  
Financial Statements ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Two Stage ◽  
Preparation Methods ◽  
Behavioral Traits ◽  
Fraud Risk

Subject. This article assesses the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation, based on their behavioral traits. Objectives. The article aims to develop a scoring type methodology for identifying the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation. Methods. For the study, we used a multidimensional statistical method of discriminant analysis based on empirical data from an author-conducted survey of 515 employees charged with the financial statements preparation in companies. Results. The article presents a two-stage methodology that helps estimate whether a person has traits associated with a hyperpropensity for financial statements fraud risk. Conclusions and Relevance. The developed methodology for detecting the fraud risk is easy to use. It gives the result in binary form and does not violate the principles of audit ethics. The estimated material misstatement risk due to unfair actions makes it possible to justify the need for appropriate audit procedures when developing a strategy and audit plan.

UNCORRELATED PROJECTION DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE IMAGE FEATURE EXTRACTION

2003 ◽  
Vol 17 (08) ◽  
pp. 1325-1347 ◽  
Author(s):  
JIAN YANG ◽  
JING-YU YANG ◽  
ALEJANDRO F. FRANGI ◽  
DAVID ZHANG
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Face Image ◽  
Desirable Property ◽  
Experimental Results ◽  
Image Feature ◽  
Analysis Method ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Projection Analysis

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

A Two-Stage Model of Tenure Mobility

1978 ◽  
Vol 10 (1) ◽  
pp. 81-92 ◽  
Author(s):  
C Jones ◽  
S Gudjonsson ◽  
J Parry Lewis
Keyword(s):  
Discriminant Analysis ◽  
Residential Mobility ◽  
Life Style ◽  
Stage Model ◽  
Multiple Discriminant Analysis ◽  
Two Stage ◽  
Socioeconomic Characteristics ◽  
Decisionmaking Process ◽  
Two Stages ◽  
Sequential Nature

This paper examines the sequential nature of the residential-mobility decisionmaking process. Initially a model of household tenure mobility is considered, consisting of two stages, the decision to move and the choice of tenure; ultimately this assumption is relaxed. Multiple discriminant analysis is used to distinguish between the different groups of households, between movers and nonmovers, and between different tenures, on the basis of a set of variables describing the socioeconomic characteristics of the household. Life-style and demographic factors are shown to influence more the tenure moved to than the decision to move. And although various factors appear to influence the mobility decision in the different tenures, the age of the household is generally found to be the most important discriminator.

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