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There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart par
Evaluating the effect of measurement errors on either adaptive or simultaneous control charts has been a topic of interest for the researchers in the recent years. Nevertheless, the effect of measurement errors on both adaptive and simultaneous monitoring control charts has not been considered yet. In this paper, through extensive numerical studies, we evaluate the effect of measurement errors on
Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using t
Due to advances in technology, sampling procedures and short lag times between successive sampling, autocorrelation among the measured data has become common in most applications. Neglecting autocorrelation leads to a poor false alarm performance. In the current paper, the effect of the autocorrelation on the performance of a variable-parameters multivariate single control chart is investigated in
Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and tw
The effect of measurement errors on the performance of multivariate adaptive control charts has not been considered yet. In this article, we investigate the effect of measurement errors on the performance of the variable sampling intervals (VSI) Hotelling's T2 control chart in the case of known parameters. A linearly covariate error model is used as the measurement error function. In order to meas
In this paper, we develop an inventory model with price dependant demand rate, under time value of money and inflation, finite time horizon, exponential backlogging rate and exponential deterioration rate with the objective of maximizing the present worth of the total system profit. Using a dynamic programming based solution algorithm, we are able to find the optimal sequence of the cycles and als
This paper considers adaptive schemes for the simultaneous monitoring of the mean and variability of a multivariate normal quality characteristic. At first, we extend an already existing bivariate non-adaptive simultaneous control chart to a multivariate one. Then, we develop several adaptive schemes, which will cover both previously bivariate and newly multivariate charts. After having designed a
It is proved that adaptive control charts have better performance than classical control charts due to adaptability of some or all of their parameters to the previous process information. Fuzzy classical control charts have been occasionally considered by many researchers in the last two decades; however, fuzzy adaptive control charts have not been investigated. In this paper, we introduce a new a
In certain situations, the quality of a process is determined by dependent variables in relation to independent variables, often modeled through a regression framework referred to as a profile. The practice of monitoring and preserving this relationship is known as profile monitoring. In this paper, we propose an innovative approach that uses different machine-learning (ML) techniques for construc
In this research, we develop three statistical based control charts: the Hotelling's T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profil
This Letter introduces a novel, to the best of our knowledge, method for assessing fruit quality using white-light biospeckle displacement analysis. The primary aim is to demonstrate that speckle displacement behavior differs between healthy and decaying regions, offering a unique means of gauging bioactivity levels, a feature that has not been explored in previous biospeckle research. By examinin
The effect of measurement errors on the performance of adaptive control charts has rarely been investigated in the univariate case and, as far as we know, it has not been investigated at all in the multivariate case. In this paper, we evaluate the effect of measurement errors on the VSS (Variable Sample Sizes) Hotelling’s T2 control chart. To do so, we suggest using six different performance measu
In this paper we develop two models; the without shortages and the completely backlogging shortages with the price dependant demand rate under time value of money and inflation, finite time horizon and exponentially deterioration rate with the objective of maximizing the present worth of the total system profit. Using the dynamic programming method for each model, we are able to obtain different s
There has been a growing interest in research regarding monitoring a process through a regression model (called a profile) rather than a simple quality characteristic. This paper proposes a new monitoring scheme to simultaneously monitor the multivariate multiple linear profiles’ parameters. This scheme is based on the Shewhart control chart concept and only has one single (max-type) control chart
Conventional image-based process control schemes require distinct, detectable features (e.g., edges or textures). In the absence of such features, current methods fail or require additional preprocessing. They are also prone to false detections from random noise, dirt, or scratches on the surface. In addition, they often focus on pixel intensity differences, making them less effective for subtle s
Episodic memory allows the flexible retrieval of commonalities and idiosyncrasies of overlapping life events. For example, seeing a woman in the city with your colleague's daughter may form an integrated memory representation involving the woman and your colleague. However, you may also keep a specific representation of the city event to talk with your colleague about the circumstances of having m
This paper explores how pro-active government policies could promote artificial intelligence (AI) in healthcare for the public good. Building on insights from the literature on industrial policy, we argue that without clear conditions and guardrails to ensure future public benefit, state assistance and subsidies will be nothing more than corporate welfare with unpredictable, if any, societal benef
X-ray fluorescence emission spectroscopy is a powerful tool to gain chemical information on a wide variety of samples. Its combination with focused X-ray beams and translation stages enables X-ray fluorescence microscopy, generating quantitative distribution maps for sets of chemical elements, depending on incident photon energy and detector specifications. The use of synchrotron radiation for X-r
Background: The primary aim was to assess psychological distress in patients with longstanding hip and groin pain (LHGP) and compare them with healthy controls. The secondary aim was to explore the association between psychological distress and symptom duration, self-reported pain and symptoms in patients with LHGP. Methods: Seventy-two patients with LHGP and sixty-three controls were included. Th
