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process-monitoring
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Applying data mining techniques to business process reengineering based on simultaneous use of two novel proposed approaches
, Article International Journal of Business Process Integration and Management ; Volume 6, Issue 3 , 2013 , Pages 247-267 ; 17418763 (ISSN) ; Khanbabaei, M ; Saniee Abadeh, M ; Sharif University of Technology
2013
Abstract
Business process reengineering (BPR) can help organisations to identify and improve their business processes. A major problem is the high volume of business process datasets with characteristics such as high dimensionality, noise, uncertainty in process datasets and complicated interactions among process variables. Data mining (DM) techniques facilitate the identification and analysis of business processes, and improve their performance by extracting the hidden knowledge in business process datasets. In this paper, we present the application of DM to BPR, based on two novel approaches. By a literature review, the first approach proposes DMbBPR model, mainly focuses on the applications of...
Detection and classification mean-shifts in multi-attribute processes by artificial neural networks
, Article International Journal of Production Research ; Volume 46, Issue 11 , 2008 , Pages 2945-2963 ; 00207543 (ISSN) ; Abbasi, B ; Sharif University of Technology
2008
Abstract
To monitor the quality of a multi-attribute process, some issues arise. One of them being the occurrence of a high number of false alarms (type I error) and the other an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, based upon the artificial neural network capabilities we develop a new methodology to overcome this problem. We design a perceptron neural network to monitor either the proportions of several types of product nonconformities (instead of using several np charts) or the number of different types of defects (instead of using several c charts) in a product. Moreover, while the...
Phase-I monitoring of log-linear model-based processes (a case study in health care: Kidney patients)
, Article Quality and Reliability Engineering International ; Volume 35, Issue 6 , 2019 , Pages 1766-1788 ; 07488017 (ISSN) ; Amiri, A ; Akhavan Niaki, S. T ; Sharif University of Technology
John Wiley and Sons Ltd
2019
Abstract
Processes with multiple correlated categorical quality characteristics are called multivariate categorical processes. These processes are usually shown by contingency tables and are characterized by log-linear models. In this paper, two monitoring approaches including likelihood ratio test (LRT) and F test are developed to monitor multivariate categorical processes based on the contingency table in Phase I. In addition, a change point estimator for multivariate categorical processes is developed in Phase I. The performances of the two proposed approaches are evaluated in terms of probability of signal, while the performance of the proposed change point estimator is evaluated in terms of...
Bootstrap method approach in designing multi-attribute control charts
, Article International Journal of Advanced Manufacturing Technology ; Volume 35, Issue 5-6 , 2007 , Pages 434-442 ; 02683768 (ISSN) ; Abbasi, B ; Sharif University of Technology
2007
Abstract
In a production process, when the quality of a product depends on more than one correlated characteristic, multivariate quality control techniques are used. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In monitoring the quality of a product or process in multi-attribute environments in which the attributes are correlated, several issues arise. For example, a high number of false alarms (type I error) occur and the probability of not detecting defects (type II error) increases when the process is monitored by a set of independent uni-attribute control charts. In this...
Monitoring high-yields processes with defects count in nonconforming items by artificial neural network
, Article Applied Mathematics and Computation ; Volume 188, Issue 1 , 2007 , Pages 262-270 ; 00963003 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
2007
Abstract
In high-yields process monitoring, the Geometric distribution is particularly useful to control the cumulative counts of conforming (CCC) items. However, in some instances the number of defects on a nonconforming observation is also of important application and must be monitored. For the latter case, the use of the generalized Poisson distribution and hence simultaneously implementation of two control charts (CCC & C charts) is recommended in the literature. In this paper, we propose an artificial neural network approach to monitor high-yields processes in which not only the cumulative counts of conforming items but also the number of defects on nonconforming items is monitored. In order to...
Skewness reduction approach in multi-attribute process monitoring
, Article Communications in Statistics - Theory and Methods ; Volume 36, Issue 12 , 2007 , Pages 2313-2325 ; 03610926 (ISSN) ; Abbasi, B ; Sharif University of Technology
2007
Abstract
Since the product quality of many industrial processes depends upon more than one dependent variable or attribute, they are either multivariate or multi-attribute in nature. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In this article, we develop a new methodology to monitor multi-attribute processes. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewness. Then, we estimate the transformed covariance matrix and apply the well-known T2 control chart. In order to illustrate the proposed method...
Practical design of low-cost instrumentation for industrial electrical impedance tomography (EIT)
, Article ; 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings, 13 May 2012 through 16 May 2012, Graz , 2012 , Pages 1259-1263 ; 9781457717710 (ISBN) ; Vosoughi Vahdat, B ; Mortazavi, M ; Hy, W ; Soleimani, M ; Sharif University of Technology
IEEE
2012
Abstract
Electrical Impedance Tomography (EIT), is one of the medical imaging technologies. It can also be used in industrial process monitoring. In this method, the image of the electrical conductivity distribution of the inner part of a conductive subject can be reconstructed. The image reconstruction process is done by injecting an accurate current into the boundary of a conductive subject (e.g. body), measuring the voltages around the boundary and transmitting them to a computer, and processing on acquired data with a software (e.g., MATLAB). The images are obtained from the peripheral data by using an algorithm. Precise EIT instrumentation plays an important role in the final images quality. In...
Principal component analysis-based control charts using support vector machines for multivariate non-normal distributions
, Article Communications in Statistics: Simulation and Computation ; 2019 ; 03610918 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
Taylor and Francis Inc
2019
Abstract
The growing demand for statistical process monitoring has led to the vast utilization of multivariate control charts. Complicated structure of the measured variables associated with highly correlated characteristics, has given rise to daily increasing urge for reliable substitutes of conventional methods. In this regard, projection methods have been developed to address the issue of high correlation among characteristics by transforming them to an uncorrelated set of variables. Principal component analysis (PCA)-based control charts are widely used to overcome the issue of correlation among measured variables by defining linear transformations of the existing variables to a new uncorrelated...
Principal component analysis-based control charts using support vector machines for multivariate non-normal distributions
, Article Communications in Statistics: Simulation and Computation ; 2019 ; 03610918 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
Taylor and Francis Inc
2019
Abstract
The growing demand for statistical process monitoring has led to the vast utilization of multivariate control charts. Complicated structure of the measured variables associated with highly correlated characteristics, has given rise to daily increasing urge for reliable substitutes of conventional methods. In this regard, projection methods have been developed to address the issue of high correlation among characteristics by transforming them to an uncorrelated set of variables. Principal component analysis (PCA)-based control charts are widely used to overcome the issue of correlation among measured variables by defining linear transformations of the existing variables to a new uncorrelated...
Towards IoT-driven predictive business process analytics
, Article 2020 International Conference on Omni-layer Intelligent Systems, COINS 2020, 31 August 2020 through 2 September 2020 ; 2020 ; Ansari, A ; Farahani, B ; Aliee, F. S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Predictive business process monitoring is concerned with predicting the process-related Key Performance Indicators (KPIs) and forecasting the future behavior of the process in realtime. Despite the amount of work contributed by researches to this field of research, the performance of existing solutions is not desirable for practical settings. Indeed, these approaches are typically context-unaware and lack generality. However, in real-life use cases, business processes are not isolated from the surrounding working environment, and thus they are influenced by many contextual events, such as events generated by IoT devices. To the best of our knowledge, there is no comprehensive study...
Principal component analysis-based control charts using support vector machines for multivariate non-normal distributions
, Article Communications in Statistics: Simulation and Computation ; Volume 49, Issue 7 , 2020 , Pages 1815-1838 ; Akhavan Niaki, S. T ; Sharif University of Technology
Taylor and Francis Inc
2020
Abstract
The growing demand for statistical process monitoring has led to the vast utilization of multivariate control charts. Complicated structure of the measured variables associated with highly correlated characteristics, has given rise to daily increasing urge for reliable substitutes of conventional methods. In this regard, projection methods have been developed to address the issue of high correlation among characteristics by transforming them to an uncorrelated set of variables. Principal component analysis (PCA)-based control charts are widely used to overcome the issue of correlation among measured variables by defining linear transformations of the existing variables to a new uncorrelated...
A Process Mining Approach to Analyze Customer Journeys to Improve Customer Experience
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
With the growth of the number of online service providers and the need to innovate in these services, in this study, the processes and the journeys taken by visitors of a website that provides employment services and employment insurance has been analyzed. In this research, process mining techniques and predictive process monitoring were implemented. With the use of a supervised and unsupervised learning algorithm, it attempted to identify the customer journeys' output and the existing patterns that lead to the complaint. In the first step, the website event log is extracted. Afterward, by using frequency-based encoding methods, the journeys traveled by users were clustered based on the...
Predictive Business Process Monitoring Using Machine Learning Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
In order to survive in today's business world, which is changing at a very fast pace, organizations can detect deviations even before they occur, quickly and with a high percentage of confidence, by analyzing their processes, in order to prevent disruptions in the processes. by monitoring the information systems that automatically execute business processes, it is possible to ensure the correct implementation of the existing processes. For this purpose, various techniques for monitoring business processes have been presented so that managers have a comprehensive and real view of how implement processes and be able to identify possible deviations in the future and try to fix them because the...
Economic and economic-statistical designs of phase II profile monitoring
, Article Quality and Reliability Engineering International ; Vol. 30, issue. 5 , July , 2014 , pp. 645-655 ; ISSN: 07488017 ; Niaki, S. T. A ; Ershadi, M. J ; Sharif University of Technology
Abstract
In economic design of profiles, parameters of a profile are determined such that the total implementation cost is minimized. These parameters consist of the number of set points, n, the interval between two successive sampling, h, and the parameters of a control chart used for monitoring. In this paper, the Lorenzen-Vance cost function is extended to model the costs associated with implementing profiles. The in-control and the out-of-control average run lengths, ARL0 and ARL1, respectively, are used as two statistical measures to evaluate the statistical performances of the proposed model. A genetic algorithm (GA) is developed for solving both the economic and the economic-statistical...
A hybrid root transformation and decision on belief approach to monitor multiattribute Poisson processes
, Article International Journal of Advanced Manufacturing Technology ; Volume 75, Issue 9-12 , December , 2014 , Pages 1651-1660 ; ISSN: 02683768 ; Javadi, S ; Fallahnezhad, M. S ; Sharif University of Technology
Abstract
Most of industrial applications of statistical process control involve more than one quality characteristics to be monitored. These characteristics are usually correlated, causing challenges for the monitoring methods. These challenges are resolved using multivariate quality control charts that have been widely developed in recent years. Nonetheless, multivariate process monitoring methods encounter a problem when the quality characteristics are of the attribute type and follow nonnormal distributions such as multivariate binomial or multivariate Poisson. Since the data analysis in the latter case is not as easy as the normal case, more complexities are involved to monitor multiattribute...
A Max-EWMA approach to monitor and diagnose faults of multivariate quality control processes
, Article International Journal of Advanced Manufacturing Technology ; Volume 68, Issue 9-12 , 2013 , Pages 2283-2294 ; 02683768 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
2013
Abstract
A new approach is developed in this paper to detect general mean shifts of multivariate quality control systems and to determine the quality characteristic(s) responsible for the shift. This approach takes advantage of both a decomposition method and an EWMA-based control statistics that are employed for multivariate normal distributions. In order to evaluate the performance of the proposed methodology, simulation studies are provided to estimate the in- and out-of-control average run lengths under different mean and variance shift scenarios. Simulation experiments are also given to compare the performances of the proposed procedure with the ones of the well-known MEWMA and MCUSUM methods....
Detecting and estimating the time of a step-change in multivariate Poisson processes
, Article Scientia Iranica ; Volume 19, Issue 3 , June , 2012 , Pages 862-871 ; 10263098 (ISSN) ; Khedmati, M ; Sharif University of Technology
2012
Abstract
In multi-attribute process monitoring, when a control chart signals an out-of-control condition indicating the existence of a special cause, knowing when the process has really changed (the change point) accelerates the identification of the source of the special cause and makes the corrective measures to be employed sooner. This, of course, results in a considerable amount of savings in time and money. Since many real world multi-attribute processes are Poisson and most process changes are step-change, a new method is proposed, in this paper, to derive the maximum likelihood estimator of the time of a step-change in the mean vector of multivariate Poisson processes. In this method, two...
Comprehensive electric arc furnace model for simulation purposes and model-based control
, Article Steel Research International ; Volume 88, Issue 3 , 2017 ; 16113683 (ISSN) ; Saboohi, Y ; Škrjanc, I ; Logar, V ; Sharif University of Technology
Wiley-VCH Verlag
2017
Abstract
The paper presents a comprehensive electric arc furnace (EAF) model, developed for simulation and model-based control of the EAF processes. The model consists of three sub models, i.e., (i) arc model; (ii) chemical and slag model; and (iii) heat-transfer model. Arc model predicts the amount of energy dissipated from the arcs using arc currents and arc lengths; chemical and slag model calculates chemical energy and changes of elements/compounds, slag height and slag quality; while the heat-transfer model uses calculations of the other two models in order to establish a reference energy system (RES) for each zone in the EAF due to the variations in arc length, slag height, and bath height. The...
A perspective on electrostatics in gas-solid fluidized beds: challenges and future research needs
, Article Powder Technology ; Volume 329 , 2018 , Pages 65-75 ; 00325910 (ISSN) ; Bi, X. T ; Grace, J. R ; Sharif University of Technology
Elsevier B.V
2018
Abstract
This paper provides a perspective on the current knowledge and potential areas of future research related to electrostatics in fluidized beds. Aspects addressed include characterization techniques, charge generation and dissipation mechanisms, interplay between the electrostatics and hydrodynamics, charge control methods, applications of tribo-electrostatic fluidization systems, and computational simulations which account for electrostatic charges. This is a complex research field involving fluid mechanics, powders and electrical physics, with potential rewards in terms of safety, process monitoring and new applications. © 2018 Elsevier B.V
Developing potentiometric sensors for scandium
, Article Sensors and Actuators B: Chemical ; Volume 348 , 2021 ; 09254005 (ISSN) ; Legin, E ; Legin, A ; Yaghmaei, S ; Nechaev, A ; Babain, V ; Kirsanov, D ; Sharif University of Technology
Elsevier B.V
2021
Abstract
Scandium is a rare earth element that is not very abundant on Earth, however in recent years due to the employment of scandium in various high-tech fields the interest in the analytical chemistry of this metal is growing. Currently, the methods for scandium quantification are mainly based on inductively coupled plasma atomic emission and mass-spectrometry and to the best of our knowledge no potentiometric sensors for scandium detection were reported so far. This study is devoted to the development of simple and cost-effective ion-selective sensors for quantification of Sc3+ in aqueous media. Employing the variety of lipophilic ligands suggested in liquid extraction for separation of...