ABSTRACT The control chart introduced by Shewhart is one of the most important quality control techniques used to detect special causes. Real world data are complicated to interpret since they involve a certain level of uncertainty that may be linked to human subjectivity or measurement device limitations. Fuzzy set theory can deal with such uncertainty and can be applied to traditional control charts. In this work, the values of the quality characteristic are fuzzified by the insertion of uncertainties and transformed into representative values for a better comparison with traditional control charts. The performance of a control chart can be measured by the average run length (ARL) and the extra quadratic loss (EQL). We observed in the present work that the fuzzy control chart has greater efficiency than the traditional control charts. An illustrative example demonstrates the application of the fuzzy control chart for the measurement of the volume contained in milk bags.

Control Charts for Cpk=2 and two values for n - n=5 and n=20. Phase I and several examples of Phase II control charts with different out-of-control signals.

Because of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution of and complicated dependency among variables such as heteroscedasticity increase the uncertainty of estimated parameters and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high-dimension, low-sample-size scenarios (small n, large p). More difficulties appear when detecting and diagnosing abnormal behaviors caused by a small set of variables (i.e., sparse changes). In this article, we propose two change-point–based control charts to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed methods can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show that the proposed methods are robust to nonnormality and heteroscedasticity. Two real data examples are used to illustrate the effectiveness of the proposed control charts in high-dimensional applications. The R codes are provided online.

This is a set of a dataset used in real data analysis of the work entitled "Robust Control Chart for Nonlinear Conditionally Heteroscedastic Time Series based on Huber Support Vector Regression". They can retrieved from the website "https://www.investing.com".

Description of the data and file structure

The dataset is identical to those downloaded from the 'historical data' tab of any index in investing.com website. Specifically, nasdaq.csv and kospi.csv contains the

Sharing/Access information

Data was derived from the following sources:

• https://www.investing.com

Code/Software

One can use the attached R code named process dataset.R to process the raw stock price indices to be in the form of the log-returns. Note that one requires dplyr (in tidyverse) package to run the mentioned code.

ARLs of control charts when the underlying model is log-GARCH(1,1) with the specified parameters, where the dataset is contaminated with a Z1,2-distributed noise.

ARLs of control charts when trained with samples obtained with the wild-bootstrap method, where ηt ∼ N(0, 1), and no additive outliers are present.

ARLs of control charts when the underlying model is log-GARCH(1,1) with the specified parameters, where no additive outliers are present.

“HSVR” and “GARCH” denotes that the chart is constructed using residuals obtained from fitting HSVR and GARCH(1,1) models, respectively.

ARLs of the OCC-based, CUSUM, and EWMA control charts when the underlying model is GJR-GARCH(1,1) with the specified parameters, where the dataset is contaminated with a Z1,2-distributed noise.

Results of the first two columns directly compares our OCC-based control chart with that of [47].

Learn how successful startups structure their org charts for scale. Key insights on span of control, hiring strategies, and optimal team design for high-growth companies.

The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.The monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology to form a level-2 daily gridded product. The daily level-3 gridded products are averaged to create the monthly average. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables. The CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection with the TqJ suffix (TqJoint) from AIRX3STM contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML3CMCCPN contains CRIMSS data processed with an analogous algorithm.

The Quality Warranty Management Market is estimated to be valued at USD 122.0 billion in 2025 and is projected to reach USD 472.6 billion by 2035, registering a compound annual growth rate (CAGR) of 14.5% over the forecast period.

Randomised diabetes patient data example for use in Markovchart-type control charts: https://cran.r-project.org/web/packages/Markovchart/index.html

This comprehensive dataset contains all the data and plots presented in the paper"Analysis and multi-objective optimisation of wind turbine torque control strategies". Every data, graph, chart or plot presented within the paper is included in its original form within this dataset. The dataset encompasses a wide variety of data types, including but not limited to:

• Numerical Data: Raw, post-processed simulation data and calculations.
• Visual Data: Graphs, charts, and plots illustrating key findings and trends.
• Textual Data: Descriptions, explanations, and interpretation of the data.

To enhance usability, each folder contains supplementary documentation, including:

• README File: This file provides an overview of the content of the folder, explanations of data formats, and any relevant context required for understanding the data and plots.
• Citation information: Proper citation guidelines to acknowledge the original source when using the dataset for research or reference.

Means and control limits of four proposed control charts for , , and different values of n, m, and .

Discover the booming paperless chart recorder market! This comprehensive analysis reveals a $2.5B market in 2025, projected to grow at a 7% CAGR through 2033. Explore key drivers, trends, and regional insights for leading companies like ABB, Honeywell, and Siemens. Learn more about industry applications, market segmentation, and future growth prospects.

In statistical process control, accurately estimating in-control (IC) parameters is crucial for effective monitoring. This typically requires a Phase I analysis to obtain estimates before monitoring commences. The traditional “fixed” estimate (FE) approach uses these estimates exclusively, while the “adaptive” estimate (AE) approach updates the estimates with each new observation. Such extreme criteria reflect the traditional bias-variance tradeoff in the framework of the sequential parameter learning schemes. This paper proposes an intermediate update rule that generalizes two ad hoc criteria for monitoring univariate Gaussian data, by giving a lower probability to parameter updates when an out-of-control (OC) situation is likely, therefore updating more frequently when there is no evidence of an OC scenario. The simulation study shows that this approach improves the detection power for small and early shifts, which are commonly regarded as a weakness of control charts based on fully online adaptive estimation. The paper also shows that the proposed method performs similarly to the fully adaptive procedure for larger or later shifts. The proposed method is illustrated by monitoring the sudden increase in ICU counts during the 2020 COVID outbreak in New York.

ARLs of attribute control charts for NTS-Weibull and Weibull distributions for ARL0 = 370 and n = 20.

The global paper recorder market is experiencing steady growth, driven by the continued demand in traditional industries despite the rise of digital alternatives. While precise market size data is unavailable, a reasonable estimation based on industry trends and comparable markets suggests a 2025 market value of approximately $500 million. Assuming a conservative Compound Annual Growth Rate (CAGR) of 3% for the forecast period (2025-2033), the market is projected to reach approximately $660 million by 2033. This growth is fueled primarily by the persistent need for reliable, low-cost, and easily understandable data recording in applications where complex digital systems may be impractical or uneconomical. Key drivers include the ongoing presence of legacy systems in sectors like industrial manufacturing and process monitoring, where paper recorders offer straightforward data logging and immediate visual inspection. Furthermore, certain niche applications requiring robust and tamper-proof recording favor paper-based solutions. However, the market faces constraints from the ongoing digital transformation across industries, as digital data acquisition and storage systems continue to gain popularity. Despite these challenges, several factors are contributing to the sustained market performance. The relatively low cost of paper recorders compared to sophisticated digital systems remains a key advantage, particularly for smaller businesses or those with limited budgets. Additionally, the simplicity of use and the inherent reliability of the technology continue to attract customers. The key players in the market are strategically focusing on improvements to paper quality, enhanced features like longer recording duration and improved print quality, and specialized applications, all designed to address industry-specific needs and maintain relevance. Segmentation within the market is driven by recorder type (single-pen, multi-pen, etc.), application (industrial process control, scientific research, etc.), and end-user industry. Regional variations exist, with mature markets experiencing slower growth compared to emerging economies where industrialization is driving demand. This report provides a detailed analysis of the global paper recorder market, projecting a market value exceeding $250 million by 2028. We delve into key market trends, competitive landscapes, and future growth prospects, focusing on technological advancements, regulatory impacts, and evolving end-user demands. This report is essential for industry stakeholders, investors, and researchers seeking a comprehensive understanding of this dynamic market segment.