scholarly journals Change point detection in process control with robust individuals control chart

2021 ◽  
Vol 36 ◽  
pp. 01006
Author(s):  
Kooi Huat Ng ◽  
Kok Haur Ng ◽  
Jeng Young Liew
Keyword(s):  
Control Chart ◽  
Real Data ◽  
Change Point Detection ◽  
Absolute Deviation ◽  
Data Set ◽  
Data Collection Process ◽  
Process Standard Deviation ◽  
Exploratory Data ◽  
Point Detection ◽  
Better Than

It is crucial to realize when a process has changed and to what extent it has changed, then it would certainly ease the task. On occasion that practitioners could determine the time point of the change, they would have a smaller search window to pursue for the special cause. As a result, the special cause can be discovered quicker and the necessary actions to improve quality can be triggered sooner. In this paper, we had demonstrated the use of so-called exploratory data analysis robust modified individuals control chart incorporating the M-scale estimator and had made some comparisons to the existing charts. The proposed modified robust individuals control chart which incorporates the M-scale estimator in order to compute the process standard deviation offers substantial improvements over the existing median absolute deviation framework. With respect to the application in real data set, the proposed approach appears to perform better than the typical robust control chart, and outperforms other conventional charts particularly in the presence of contamination. Thus, it is for these reasons that the proposed modified robust individuals control chart is preferred especially when there is a possible existence of outliers in data collection process.

Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

2020 ◽  
pp. 507-522
Author(s):  
Parisa Torkaman
Keyword(s):  
Least Squares ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Real Data ◽  
Minimum Variance ◽  
Cumulative Distribution ◽  
Estimation Methods ◽  
Data Set ◽  
Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

scholarly journals Fast effect size shrinkage software for beta-binomial models of allelic imbalance

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2024
Author(s):  
Joshua P. Zitovsky ◽  
Michael I. Love
Keyword(s):  
Allelic Imbalance ◽  
Real Data ◽  
Shrinkage Estimators ◽  
Data Set ◽  
Bayesian Shrinkage ◽  
In Cis ◽  
Posterior Estimation ◽  
Binomial Models ◽  
Better Than ◽  
Diploid Organism

Allelic imbalance occurs when the two alleles of a gene are differentially expressed within a diploid organism and can indicate important differences in cis-regulation and epigenetic state across the two chromosomes. Because of this, the ability to accurately quantify the proportion at which each allele of a gene is expressed is of great interest to researchers. This becomes challenging in the presence of small read counts and/or sample sizes, which can cause estimators for allelic expression proportions to have high variance. Investigators have traditionally dealt with this problem by filtering out genes with small counts and samples. However, this may inadvertently remove important genes that have truly large allelic imbalances. Another option is to use pseudocounts or Bayesian estimators to reduce the variance. To this end, we evaluated the accuracy of four different estimators, the latter two of which are Bayesian shrinkage estimators: maximum likelihood, adding a pseudocount to each allele, approximate posterior estimation of GLM coefficients (apeglm) and adaptive shrinkage (ash). We also wrote C++ code to quickly calculate ML and apeglm estimates and integrated it into the apeglm package. The four methods were evaluated on two simulations and one real data set. Apeglm consistently performed better than ML according to a variety of criteria, and generally outperformed use of pseudocounts as well. Ash also performed better than ML in one of the simulations, but in the other performance was more mixed. Finally, when compared to five other packages that also fit beta-binomial models, the apeglm package was substantially faster and more numerically reliable, making our package useful for quick and reliable analyses of allelic imbalance. Apeglm is available as an R/Bioconductor package at http://bioconductor.org/packages/apeglm.

Change point detection in social networks using a multivariate exponentially weighted moving average chart

2019 ◽  
Vol 46 (6) ◽  
pp. 790-809
Author(s):  
Ali Salmasnia ◽  
Mohammadreza Mohabbati ◽  
Mohammadreza Namdar
Keyword(s):  
Social Networks ◽  
Control Chart ◽  
Change Point ◽  
Moving Average ◽  
Change Point Detection ◽  
Network Density ◽  
Exponentially Weighted Moving Average ◽  
The Social ◽  
Point Detection ◽  
Weighted Moving Average

Although the significant role of social networks in communications between individuals has attracted researchers’ attention to the social networks, only few authors investigated social network monitoring in their studies. Most of the existing studies in this context suffer from the following three main drawbacks: (1) using the case-based network attributes such as person experiences and departments instead of the main attributes such as network density and centrality attributes, (2) monitoring the social attributes separately with the assumption that they are independent of each other and (3) ignoring detection of real time of change in the network. To overcome the above-mentioned disadvantages, this research develops a statistical method for monitoring the connections among actors in the social networks with the four most important network attributes consisting of (1) network density, (2) degree centrality, (3) betweenness centrality and (4) closeness centrality. To this end, a multivariate exponentially weighted moving average (MEWMA) control chart is used for simultaneous monitoring of these four correlated attributes. Furthermore, since the control chart usually does not alert a signal in the exact time of change due to type II error, this study presents a change point detection method to reduce cost and time required for diagnosing the control chart signal. Eventually, the efficiency of the proposed approach in comparison with the existing methods is evaluated through a simulation procedure. The results indicate that the suggested method has better performance than the univariate approach in detecting change point.

scholarly journals Multiple Dependent State Sampling-Based Chart Using Belief Statistic under Neutrosophic Statistics

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ahmed Ibrahim Shawky ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Control Process ◽  
Real Data ◽  
Process Conditions ◽  
Neutrosophic Statistics ◽  
The Mean ◽  
Run Lengths ◽  
Dependent State ◽  
Better Than

In this paper, a control chart scheme has been introduced for the mean monitoring using gamma distribution for belief statistics using multiple dependent (deferred) state sampling under the neutrosophic statistics. The coefficients of the control chart and the neutrosophic average run lengths have been estimated for specific false alarm probabilities under various process conditions. The offered chart has been compared with the existing classical chart through simulation and the real data. From the comparison, it is concluded that the performance of the proposed chart is better than that of the existing chart in terms of average run length under uncertain environment. The proposed chart has the ability to detect a shift quickly than the existing chart. It has been observed that the proposed chart is efficient in quick monitoring of the out-of-control process and a cherished addition in the toolkit of the quality control personnel.

scholarly journals Constructing a Lightweight Key-Value Store Based on the Windows Native Features

2019 ◽  
Vol 9 (18) ◽  
pp. 3801 ◽  
Author(s):  
Hyuk-Yoon Kwon
Keyword(s):  
State Of The Art ◽  
Main Idea ◽  
Real Data ◽  
Data Sets ◽  
Parameter Setting ◽  
Data Set ◽  
Multi Level ◽  
Windows Registry ◽  
Best Parameter ◽  
Better Than

In this paper, we propose a method to construct a lightweight key-value store based on the Windows native features. The main idea is providing a thin wrapper for the key-value store on top of a built-in storage in Windows, called Windows registry. First, we define a mapping of the components in the key-value store onto the components in the Windows registry. Then, we present a hash-based multi-level registry index so as to distribute the key-value data balanced and to efficiently access them. Third, we implement basic operations of the key-value store (i.e., Get, Put, and Delete) by manipulating the Windows registry using the Windows native APIs. We call the proposed key-value store WR-Store. Finally, we propose an efficient ETL (Extract-Transform-Load) method to migrate data stored in WR-Store into any other environments that support existing key-value stores. Because the performance of the Windows registry has not been studied much, we perform the empirical study to understand the characteristics of WR-Store, and then, tune the performance of WR-Store to find the best parameter setting. Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. That is, WR-Store becomes much more efficient than the other key-value stores as the size of data set increases. In addition, we show that the performance of WR-Store is maintained even in the case of intensive registry workloads where 1000 processes accessing to the registry actively are concurrently running.

Path-based keyword search over XML streams

2015 ◽  
Vol 11 (3) ◽  
pp. 347-369 ◽  
Author(s):  
Savong Bou ◽  
Toshiyuki Amagasa ◽  
Hiroyuki Kitagawa
Keyword(s):  
Keyword Search ◽  
Real Data ◽  
Experimental Results ◽  
Data Set ◽  
Content Type ◽  
Finite Automation ◽  
Powerful Means ◽  
Extensible Markup ◽  
Xml Streams ◽  
Better Than

Purpose – In purpose of this paper is to propose a novel scheme to process XPath-based keyword search over Extensible Markup Language (XML) streams, where one can specify query keywords and XPath-based filtering conditions at the same time. Experimental results prove that our proposed scheme can efficiently and practically process XPath-based keyword search over XML streams. Design/methodology/approach – To allow XPath-based keyword search over XML streams, it was attempted to integrate YFilter (Diao et al., 2003) with CKStream (Hummel et al., 2011). More precisely, the nondeterministic finite automation (NFA) of YFilter is extended so that keyword matching at text nodes is supported. Next, the stack data structure is modified by integrating set of NFA states in YFilter with bitmaps generated from set of keyword queries in CKStream. Findings – Extensive experiments were conducted using both synthetic and real data set to show the effectiveness of the proposed method. The experimental results showed that the accuracy of the proposed method was better than the baseline method (CKStream), while it consumed less memory. Moreover, the proposed scheme showed good scalability with respect to the number of queries. Originality/value – Due to the rapid diffusion of XML streams, the demand for querying such information is also growing. In such a situation, the ability to query by combining XPath and keyword search is important, because it is easy to use, but powerful means to query XML streams. However, none of existing works has addressed this issue. This work is to cope with this problem by combining an existing XPath-based YFilter and a keyword-search-based CKStream for XML streams to enable XPath-based keyword search.

scholarly journals Change Point Detection with Robust Control Chart

2011 ◽  
Vol 2011 ◽  
pp. 1-20
Author(s):  
Ng Kooi Huat ◽  
Habshah Midi
Keyword(s):  
Robust Control ◽  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Process ◽  
Change Point Detection ◽  
Process Data ◽  
To Come ◽  
Point Detection ◽  
Control Limits

Monitoring a process over time using a control chart allows quick detection of unusual states. In phase I, some historical process data, assumed to come from an in-control process, are used to construct the control limits. In Phase II, the process is monitored for an ongoing basis using control limits from Phase I. In Phase II, observations falling outside the control limits or unusual patterns of observations signal that the process has shifted from in-control process settings. Such signals trigger a search for assignable cause and, if the cause is found, corrective action will be implemented to prevent its recurrence. The purpose of this paper is to introduce a new methodology appropriate for constructing a robust control chart when a nonnormal or a contaminated data that may arise in phase I state. Through extensive Monte Carlo simulations, we examine the behaviors and performances of the proposed MM robust control chart when there is a process shift in mean.

scholarly journals A New Control Chart for Monitoring the Process Mean Using Successive Sampling and Multiple Dependent State Repetitive Sampling

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 70
Author(s):  
Mansour Sattam Aldosari ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun ◽  
Khushnoor Khan
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Artificial Data ◽  
Process Mean ◽  
Data Set ◽  
Successive Sampling ◽  
Shift Detection ◽  
Dependent State ◽  
Better Than

In this paper, a new control chart scheme has been developed for monitoring the production process mean using successive sampling over two occasions. The proposed chart reduces to three different existing control charts under different assumptions and is compared with these three existing control charts for monitoring the process average. It has been observed that the proposed control chart performs better than the other existing control charts in terms of average run length (ARL). A simulation study using an artificial data set was included for demonstrating the process shift detection power of the proposed control chart.

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