A control chart pattern recognition system for feedback-control processes

Torre-Gutiérrez, Héctor De La; Pham, Duc Truong

doi:10.1016/j.eswa.2019.112826

Cited by 16 publications

(6 citation statements)

References 45 publications

(42 reference statements)

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“…Then, to make it easier, the image is transformed into a reduced set of features. This operation selects and measures the representative properties of raw input data in a reduced form [28]. Moreover, such a set represents the relevant piece of information required to perform a desired task.…”

Section: Machine Learning In the Image Processing Contextmentioning

confidence: 99%

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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Introduction: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. Methods:The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized.Discussion: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques.Conclusions: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.

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Section: Machine Learning In the Image Processing Contextmentioning

confidence: 99%

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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“…The researchers have also developed control chart pattern recognition methods for the feedback control process (De la Torre-Gutiérrez and Pham, 2019) as well as for the SPC-EPC process (Shao and Chiu, 2016). De la Torre Gutierrez and Pham (2016) proposed a new scheme comprising of change point detection and mean change categorization for estimating abnormal pattern parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

A control chart pattern recognition methodology for controlling information technology-enabled service (ITeS) process customer complaints

John

2021

IJPPM

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PurposeThe purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.Design/methodology/approachA two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.FindingsThe validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.Practical implicationsThis study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.Originality/valueThe application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

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“…Second, it provides indicative information which can effectively prevent potential defects [3]. Third, the information on manufacturing system ability is provided [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

Wu,

Hou,

Lei

et al. 2023

Mathematics

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Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

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A control chart pattern recognition system for feedback-control processes

Cited by 16 publications

References 45 publications

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

A control chart pattern recognition methodology for controlling information technology-enabled service (ITeS) process customer complaints

A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

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