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Bayesian control chart</title> <meta name="description" content="Search results for: multivariate Bayesian control chart"> <meta name="keywords" content="multivariate Bayesian control chart"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="multivariate Bayesian control chart"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 11763</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: multivariate Bayesian control chart</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11763</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11762</span> On the Bootstrap P-Value Method in Identifying out of Control Signals in Multivariate Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Ikpotokin">O. Ikpotokin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20control%20limit" title="bootstrap control limit">bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/77853/on-the-bootstrap-p-value-method-in-identifying-out-of-control-signals-in-multivariate-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11761</span> Optimal Bayesian Chart for Controlling Expected Number of Defects in Production Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Makis">V. Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Jafari"> L. Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20u-chart" title="Bayesian u-chart">Bayesian u-chart</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20design" title=" economic design"> economic design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20stopping" title=" optimal stopping"> optimal stopping</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a> </p> <a href="https://publications.waset.org/abstracts/62841/optimal-bayesian-chart-for-controlling-expected-number-of-defects-in-production-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">573</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11760</span> An AK-Chart for the Non-Normal Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Hau%20Liu">Chia-Hau Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tai-Yue%20Wang"> Tai-Yue Wang </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20control%20chart" title="multivariate control chart">multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20classification%20method" title=" one-class classification method"> one-class classification method</a>, <a href="https://publications.waset.org/abstracts/search?q=non-normal%20data" title=" non-normal data"> non-normal data</a> </p> <a href="https://publications.waset.org/abstracts/7485/an-ak-chart-for-the-non-normal-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7485.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">422</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11759</span> EWMA and MEWMA Control Charts for Monitoring Mean and Variance in Industrial Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Toro">L. A. Toro</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prieto"> N. Prieto</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Vargas"> J. J. Vargas </a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title="control charts">control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20moving%20average%20%28MEWMA%29" title=" multivariate exponentially moving average (MEWMA)"> multivariate exponentially moving average (MEWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20moving%20average%20%28EWMA%29" title=" exponentially moving average (EWMA)"> exponentially moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20control%20process" title=" industrial control process"> industrial control process</a> </p> <a href="https://publications.waset.org/abstracts/38377/ewma-and-mewma-control-charts-for-monitoring-mean-and-variance-in-industrial-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11758</span> Multivariate Control Chart to Determine Efficiency Measurements in Industrial Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Vargas">J. J. Vargas</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prieto"> N. Prieto</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Toro"> L. A. Toro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts are commonly used to monitor processes involving either variable or attribute of quality characteristics and determining the control limits as a critical task for quality engineers to improve the processes. Nonetheless, in some applications it is necessary to include an estimation of efficiency. In this paper, the ability to define the efficiency of an industrial process was added to a control chart by means of incorporating a data envelopment analysis (DEA) approach. In depth, a Bayesian estimation was performed to calculate the posterior probability distribution of parameters as means and variance and covariance matrix. This technique allows to analyse the data set without the need of using the hypothetical large sample implied in the problem and to be treated as an approximation to the finite sample distribution. A rejection simulation method was carried out to generate random variables from the parameter functions. Each resulting vector was used by stochastic DEA model during several cycles for establishing the distribution of each efficiency measures for each DMU (decision making units). A control limit was calculated with model obtained and if a condition of a low level efficiency of DMU is presented, system efficiency is out of control. In the efficiency calculated a global optimum was reached, which ensures model reliability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title="data envelopment analysis">data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=DEA" title=" DEA"> DEA</a>, <a href="https://publications.waset.org/abstracts/search?q=Multivariate%20control%20chart" title=" Multivariate control chart"> Multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=rejection%20simulation%20method" title=" rejection simulation method"> rejection simulation method</a> </p> <a href="https://publications.waset.org/abstracts/37123/multivariate-control-chart-to-determine-efficiency-measurements-in-industrial-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11757</span> The Moment of the Optimal Average Length of the Multivariate Exponentially Weighted Moving Average Control Chart for Equally Correlated Variables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edokpa%20Idemudia%20Waziri">Edokpa Idemudia Waziri</a>, <a href="https://publications.waset.org/abstracts/search?q=Salisu%20S.%20Umar"> Salisu S. Umar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Hotellng鈥檚 T^2 is a well-known statistic for detecting a shift in the mean vector of a multivariate normal distribution. Control charts based on T have been widely used in statistical process control for monitoring a multivariate process. Although it is a powerful tool, the T statistic is deficient when the shift to be detected in the mean vector of a multivariate process is small and consistent. The Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is one of the control statistics used to overcome the drawback of the Hotellng鈥檚 T statistic. In this paper, the probability distribution of the Average Run Length (ARL) of the MEWMA control chart when the quality characteristics exhibit substantial cross correlation and when the process is in-control and out-of-control was derived using the Markov Chain algorithm. The derivation of the probability functions and the moments of the run length distribution were also obtained and they were consistent with some existing results for the in-control and out-of-control situation. By simulation process, the procedure identified a class of ARL for the MEWMA control when the process is in-control and out-of-control. From our study, it was observed that the MEWMA scheme is quite adequate for detecting a small shift and a good way to improve the quality of goods and services in a multivariate situation. It was also observed that as the in-control average run length ARL0卢 or the number of variables (p) increases, the optimum value of the ARL0pt increases asymptotically and as the magnitude of the shift 蟽 increases, the optimal ARLopt decreases. Finally, we use the example from the literature to illustrate our method and demonstrate its efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain" title=" markov chain"> markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20weighted%20moving%20average" title=" multivariate exponentially weighted moving average"> multivariate exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20smoothing%20parameter" title=" optimal smoothing parameter"> optimal smoothing parameter</a> </p> <a href="https://publications.waset.org/abstracts/42648/the-moment-of-the-optimal-average-length-of-the-multivariate-exponentially-weighted-moving-average-control-chart-for-equally-correlated-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42648.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">422</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11756</span> A Study on the False Alarm Rates of MEWMA and MCUSUM Control Charts When the Parameters Are Estimated</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Farouk%20Abbas">Umar Farouk Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Danjuma%20Mustapha"> Danjuma Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamisu%20Idi"> Hamisu Idi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is now a known fact that quality is an important issue in manufacturing industries. A control chart is an integrated and powerful tool in statistical process control (SPC). The mean 碌 and standard deviation 蟽 parameters are estimated. In general, the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) are used in the detection of small shifts in joint monitoring of several correlated variables; the charts used information from past data which makes them sensitive to small shifts. The aim of the paper is to compare the performance of Shewhart xbar, MEWMA, and MCUSUM control charts in terms of their false rates when parameters are estimated with autocorrelation. A simulation was conducted in R software to generate the average run length (ARL) values of each of the charts. After the analysis, the results show that a comparison of the false alarm rates of the charts shows that MEWMA chart has lower false alarm rates than the MCUSUM chart at various levels of parameter estimated to the number of ARL0 (in control) values. Also noticed was that the sample size has an advert effect on the false alarm of the control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=MCUSUM%20chart" title=" MCUSUM chart"> MCUSUM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=MEWMA%20chart" title=" MEWMA chart"> MEWMA chart</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm%20rate" title=" false alarm rate"> false alarm rate</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/106504/a-study-on-the-false-alarm-rates-of-mewma-and-mcusum-control-charts-when-the-parameters-are-estimated" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11755</span> Adaptive Process Monitoring for Time-Varying Situations Using Statistical Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seulki%20Lee">Seulki Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling鈥檚 T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20control%20chart" title="multivariate control chart">multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20method" title=" nonparametric method"> nonparametric method</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20data%20description" title=" support vector data description"> support vector data description</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20process" title=" time-varying process"> time-varying process</a> </p> <a href="https://publications.waset.org/abstracts/52078/adaptive-process-monitoring-for-time-varying-situations-using-statistical-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52078.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11754</span> A Posterior Predictive Model-Based Control Chart for Monitoring Healthcare</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Fan%20Lin">Yi-Fan Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20P.%20Howley"> Peter P. Howley</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20A.%20Tuyl"> Frank A. Tuyl</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quality measurement and reporting systems are used in healthcare internationally. In Australia, the Australian Council on Healthcare Standards records and reports hundreds of clinical indicators (CIs) nationally across the healthcare system. These CIs are measures of performance in the clinical setting, and are used as a screening tool to help assess whether a standard of care is being met. Existing analysis and reporting of these CIs incorporate Bayesian methods to address sampling variation; however, such assessments are retrospective in nature, reporting upon the previous six or twelve months of data. The use of Bayesian methods within statistical process control for monitoring systems is an important pursuit to support more timely decision-making. Our research has developed and assessed a new graphical monitoring tool, similar to a control chart, based on the beta-binomial posterior predictive (BBPP) distribution to facilitate the real-time assessment of health care organizational performance via CIs. The BBPP charts have been compared with the traditional Bernoulli CUSUM (BC) chart by simulation. The more traditional “central” and “highest posterior density” (HPD) interval approaches were each considered to define the limits, and the multiple charts were compared via in-control and out-of-control average run lengths (ARLs), assuming that the parameter representing the underlying CI rate (proportion of cases with an event of interest) required estimation. Preliminary results have identified that the BBPP chart with HPD-based control limits provides better out-of-control run length performance than the central interval-based and BC charts. Further, the BC chart’s performance may be improved by using Bayesian parameter estimation of the underlying CI rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length%20%28ARL%29" title="average run length (ARL)">average run length (ARL)</a>, <a href="https://publications.waset.org/abstracts/search?q=bernoulli%20cusum%20%28BC%29%20chart" title=" bernoulli cusum (BC) chart"> bernoulli cusum (BC) chart</a>, <a href="https://publications.waset.org/abstracts/search?q=beta%20binomial%20posterior%20predictive%20%28BBPP%29%20distribution" title=" beta binomial posterior predictive (BBPP) distribution"> beta binomial posterior predictive (BBPP) distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20indicator%20%28CI%29" title=" clinical indicator (CI)"> clinical indicator (CI)</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20organization%20%28HCO%29" title=" healthcare organization (HCO)"> healthcare organization (HCO)</a>, <a href="https://publications.waset.org/abstracts/search?q=highest%20posterior%20density%20%28HPD%29%20interval" title=" highest posterior density (HPD) interval"> highest posterior density (HPD) interval</a> </p> <a href="https://publications.waset.org/abstracts/95410/a-posterior-predictive-model-based-control-chart-for-monitoring-healthcare" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95410.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11753</span> Optimal Design for SARMA(P,Q)L Process of EWMA Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yupaporn%20Areepong">Yupaporn Areepong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (位) and width of control limit (H) for designing EWMA chart with minimum of (ARL1). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20parameters" title=" optimal parameters"> optimal parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average%20%28EWMA%29" title=" exponentially weighted moving average (EWMA)"> exponentially weighted moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title=" control chart"> control chart</a> </p> <a href="https://publications.waset.org/abstracts/10653/optimal-design-for-sarmapql-process-of-ewma-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10653.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11752</span> Statistical Design of Synthetic VP X-bar Control Chat Using Markov Chain Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akbar%20Heydari">Ali Akbar Heydari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20approach" title=" markov chain approach"> markov chain approach</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20design" title=" statistical design"> statistical design</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic" title=" synthetic"> synthetic</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20parameter" title=" variable parameter"> variable parameter</a> </p> <a href="https://publications.waset.org/abstracts/146094/statistical-design-of-synthetic-vp-x-bar-control-chat-using-markov-chain-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146094.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11751</span> An Optimal Bayesian Maintenance Policy for a Partially Observable System Subject to Two Failure Modes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akram%20Khaleghei%20Ghosheh%20Balagh">Akram Khaleghei Ghosheh Balagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new maintenance model for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model. A cost-optimal Bayesian control policy is developed for maintaining the system. The control problem is formulated in the semi-Markov decision process framework. An effective computational algorithm is developed and illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title="partially observable system">partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20Bayesian%20control" title=" multivariate Bayesian control"> multivariate Bayesian control</a> </p> <a href="https://publications.waset.org/abstracts/12740/an-optimal-bayesian-maintenance-policy-for-a-partially-observable-system-subject-to-two-failure-modes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11750</span> The Variable Sampling Interval Xbar Chart versus the Double Sampling Xbar Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20B.%20C.%20Khoo">Michael B. C. Khoo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20L.%20Khoo"> J. L. Khoo</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20C.%20Yeong"> W. C. Yeong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20L.%20Teoh"> W. L. Teoh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Shewhart Xbar control chart is a useful process monitoring tool in manufacturing industries to detect the presence of assignable causes. However, it is insensitive in detecting small process shifts. To circumvent this problem, adaptive control charts are suggested. An adaptive chart enables at least one of the chart鈥檚 parameters to be adjusted to increase the chart鈥檚 sensitivity. Two common adaptive charts that exist in the literature are the double sampling (DS) Xbar and variable sampling interval (VSI) Xbar charts. This paper compares the performances of the DS and VSI Xbar charts, based on the average time to signal (ATS) criterion. The ATS profiles of the DS Xbar and VSI Xbar charts are obtained using the Mathematica and Statistical Analysis System (SAS) programs, respectively. The results show that the VSI Xbar chart is generally superior to the DS Xbar chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20charts" title="adaptive charts">adaptive charts</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20time%20to%20signal" title=" average time to signal"> average time to signal</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20sampling" title=" double sampling"> double sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=charts" title=" charts"> charts</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20sampling%20interval" title=" variable sampling interval"> variable sampling interval</a> </p> <a href="https://publications.waset.org/abstracts/45295/the-variable-sampling-interval-xbar-chart-versus-the-double-sampling-xbar-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11749</span> Application of Hyperbinomial Distribution in Developing a Modified p-Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shourav%20Ahmed">Shourav Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gulam%20Kibria"> M. Gulam Kibria</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Zaman"> Kais Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts graphically verify variation in quality parameters. Attribute type control charts deal with quality parameters that can only hold two states, e.g., good or bad, yes or no, etc. At present, p-control chart is most commonly used to deal with attribute type data. In construction of p-control chart using binomial distribution, the value of proportion non-conforming must be known or estimated from limited sample information. As the probability distribution of fraction non-conforming (p) is considered in hyperbinomial distribution unlike a constant value in case of binomial distribution, it reduces the risk of false detection. In this study, a statistical control chart is proposed based on hyperbinomial distribution when prior estimate of proportion non-conforming is unavailable and is estimated from limited sample information. We developed the control limits of the proposed modified p-chart using the mean and variance of hyperbinomial distribution. The proposed modified p-chart can also utilize additional sample information when they are available. The study also validates the use of modified p-chart by comparing with the result obtained using cumulative distribution function of hyperbinomial distribution. The study clearly indicates that the use of hyperbinomial distribution in construction of p-control chart yields much accurate estimate of quality parameters than using binomial distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20distribution" title="binomial distribution">binomial distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20distribution%20function" title=" cumulative distribution function"> cumulative distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper%20binomial%20distribution" title=" hyper binomial distribution"> hyper binomial distribution</a> </p> <a href="https://publications.waset.org/abstracts/90750/application-of-hyperbinomial-distribution-in-developing-a-modified-p-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11748</span> An EWMA P-Chart Based on Improved Square Root Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saowanit%20Sukparungsee">Saowanit Sukparungsee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=number%20of%20defects" title="number of defects">number of defects</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average" title=" exponentially weighted moving average"> exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title=" average run length"> average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=square%20root%20transformations" title=" square root transformations"> square root transformations</a> </p> <a href="https://publications.waset.org/abstracts/10613/an-ewma-p-chart-based-on-improved-square-root-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11747</span> Optimal Maintenance Policy for a Partially Observable Two-Unit System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari">Leila Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20B.%20Akram%20Khaleghei"> G. B. Akram Khaleghei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1, which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM, has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed and illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition-based%20maintenance" title="condition-based maintenance">condition-based maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20Bayesian%20control%20chart" title=" multivariate Bayesian control chart"> multivariate Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title=" partially observable system"> partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=two-unit%20system" title=" two-unit system"> two-unit system</a> </p> <a href="https://publications.waset.org/abstracts/9588/optimal-maintenance-policy-for-a-partially-observable-two-unit-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">459</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11746</span> Economic Design of a Quality Control Chart for the Proportion of Defective Items</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Encarnaci%C3%B3n%20%C3%81lvarez-Verdejo">Encarnaci贸n 脕lvarez-Verdejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%BAl%20Amor-Pulido"> Ra煤l Amor-Pulido</a>, <a href="https://publications.waset.org/abstracts/search?q=Pablo%20J.%20Moya-Fern%C3%A1ndez"> Pablo J. Moya-Fern谩ndez</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20F.%20Mu%C3%B1oz-Rosas"> Juan F. Mu帽oz-Rosas</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20J.%20Blanco-Encomienda"> Francisco J. Blanco-Encomienda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proportion" title="proportion">proportion</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20I%20error" title=" type I error"> type I error</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20plan" title=" economic plan"> economic plan</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20function" title=" distribution function"> distribution function</a> </p> <a href="https://publications.waset.org/abstracts/42442/economic-design-of-a-quality-control-chart-for-the-proportion-of-defective-items" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11745</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11744</span> Process Monitoring Based on Parameterless Self-Organizing Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Young%20Jae%20Choung">Young Jae Choung</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical Process Control (SPC) is a popular technique for process monitoring. A widely used tool in SPC is a control chart, which is used to detect the abnormal status of a process and maintain the controlled status of the process. Traditional control charts, such as Hotelling鈥檚 T2 control chart, are effective techniques to detect abnormal observations and monitor processes. However, many complicated manufacturing systems exhibit nonlinearity because of the different demands of the market. In this case, the unregulated use of a traditional linear modeling approach may not be effective. In reality, many industrial processes contain the nonlinear and time-varying properties because of the fluctuation of process raw materials, slowing shift of the set points, aging of the main process components, seasoning effects, and catalyst deactivation. The use of traditional SPC techniques with time-varying data will degrade the performance of the monitoring scheme. To address these issues, in the present study, we propose a parameterless self-organizing map (PLSOM)-based control chart. The PLSOM-based control chart not only can manage a situation where the distribution or parameter of the target observations changes, but also address the nonlinearity of modern manufacturing systems. The control limits of the proposed PLSOM chart are established by estimating the empirical level of significance on the percentile using a bootstrap method. Experimental results with simulated data and actual process data from a thin-film transistor-liquid crystal display process demonstrated the effectiveness and usefulness of the proposed chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter-less%20self-organizing%20map" title=" parameter-less self-organizing map"> parameter-less self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20property" title=" time-varying property"> time-varying property</a> </p> <a href="https://publications.waset.org/abstracts/52108/process-monitoring-based-on-parameterless-self-organizing-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11743</span> The Impact of Artificial Intelligence on Qualty Conrol and Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mary%20Moner%20Botros%20Fanawel">Mary Moner Botros Fanawel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20control%20structure" title=" hierarchical control structure"> hierarchical control structure</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20with%20DBPs%20objectives%20proportion" title=" water quality with DBPs objectives proportion"> water quality with DBPs objectives proportion</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20I%20error" title=" type I error"> type I error</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20plan" title=" economic plan"> economic plan</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20function%20bootstrap%20control%20limit" title=" distribution function bootstrap control limit"> distribution function bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/184564/the-impact-of-artificial-intelligence-on-qualty-conrol-and-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11742</span> Rounded-off Measurements and Their Implication on Control Charts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ran%20Etgar">Ran Etgar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The process of rounding off measurements in continuous variables is commonly encountered. Although it usually has minor effects, sometimes it can lead to poor outcomes in statistical process control using X 虆-chart. The traditional control limits can cause incorrect conclusions if applied carelessly. This study looks into the limitations of classical control limits, particularly the impact of asymmetry. An approach to determining the distribution function of the measured parameter (Y 虆) is presented, resulting in a more precise method to establish the upper and lower control limits. The proposed method, while slightly more complex than Shewhart's original idea, is still user-friendly and accurate and only requires the use of two straightforward tables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inaccurate%20measurement" title="inaccurate measurement">inaccurate measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=SPC" title=" SPC"> SPC</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=rounded-off" title=" rounded-off"> rounded-off</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title=" control chart"> control chart</a> </p> <a href="https://publications.waset.org/abstracts/188545/rounded-off-measurements-and-their-implication-on-control-charts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">40</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11741</span> Developing Variable Repetitive Group Sampling Control Chart Using Regression Estimator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liaquat%20Ahmad">Liaquat Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Aslam"> Muhammad Aslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam"> Muhammad Azam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart鈥檚 x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20shift" title=" process shift"> process shift</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20estimators" title=" regression estimators"> regression estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=repetitive%20group%20sampling" title=" repetitive group sampling"> repetitive group sampling</a> </p> <a href="https://publications.waset.org/abstracts/13539/developing-variable-repetitive-group-sampling-control-chart-using-regression-estimator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13539.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">565</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11740</span> Establishing Control Chart Limits for Rounded Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ran%20Etgar">Ran Etgar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The process of rounding off measurements in continuous variables is commonly encountered. Although it usually has minor effects, sometimes it can lead to poor outcomes in statistical process control using X虅 chart. The traditional control limits can cause incorrect conclusions if applied carelessly. This study looks into the limitations of classical control limits, particularly the impact of asymmetry. An approach to determining the distribution function of the measured parameter 瘸 is presented, resulting in a more precise method to establish the upper and lower control limits. The proposed method, while slightly more complex than Shewhart's original idea, is still user-friendly and accurate and only requires the use of two straightforward tables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPC" title="SPC">SPC</a>, <a href="https://publications.waset.org/abstracts/search?q=round-off%20data" title=" round-off data"> round-off data</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20limit" title=" control limit"> control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=rounding%20error" title=" rounding error"> rounding error</a> </p> <a href="https://publications.waset.org/abstracts/162235/establishing-control-chart-limits-for-rounded-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162235.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11739</span> Design Data Sorter Circuit Using Insertion Sorting Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Abugharsa">Hoda Abugharsa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=insert%20sorting%20algorithm" title="insert sorting algorithm">insert sorting algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=ASM%20chart" title=" ASM chart"> ASM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=sorter%20circuit" title=" sorter circuit"> sorter circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20machine" title=" state machine"> state machine</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20unit" title=" control unit"> control unit</a> </p> <a href="https://publications.waset.org/abstracts/5614/design-data-sorter-circuit-using-insertion-sorting-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11738</span> Factorization of Computations in Bayesian Networks: Interpretation of Factors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linda%20Smail">Linda Smail</a>, <a href="https://publications.waset.org/abstracts/search?q=Zineb%20Azouz"> Zineb Azouz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20networks" title="Bayesian networks">Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=D-Separation" title=" D-Separation"> D-Separation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20two%20Bayesian%20networks" title=" level two Bayesian networks"> level two Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=factorization%20of%20computation" title=" factorization of computation"> factorization of computation</a> </p> <a href="https://publications.waset.org/abstracts/18829/factorization-of-computations-in-bayesian-networks-interpretation-of-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11737</span> Pattern Identification in Statistical Process Control Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Pramila%20Devi">M. Pramila Devi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20V.%20N.%20Indra%20Kiran"> N. V. N. Indra Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point鈥檚 exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart%20pattern%20recognition" title="control chart pattern recognition">control chart pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalization" title=" generalization"> generalization</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20stopping" title=" early stopping"> early stopping</a> </p> <a href="https://publications.waset.org/abstracts/6307/pattern-identification-in-statistical-process-control-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11736</span> Analysis of Diabetes Patients Using Pearson, Cost Optimization, Control Chart Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Poovarasan"> R. Poovarasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=congenital%20diabetics" title=" congenital diabetics"> congenital diabetics</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20relationship" title=" linear relationship"> linear relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=monotonic%20function" title=" monotonic function"> monotonic function</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking%20samples" title=" ranking samples"> ranking samples</a>, <a href="https://publications.waset.org/abstracts/search?q=pediatric" title=" pediatric"> pediatric</a> </p> <a href="https://publications.waset.org/abstracts/72132/analysis-of-diabetes-patients-using-pearson-cost-optimization-control-chart-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72132.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11735</span> Mixed Integer Programming-Based One-Class Classification Method for Process Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younghoon%20Kim">Younghoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20programming" title=" mixed integer programming"> mixed integer programming</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20classification" title=" one-class classification"> one-class classification</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20data%20description" title=" support vector data description"> support vector data description</a> </p> <a href="https://publications.waset.org/abstracts/52060/mixed-integer-programming-based-one-class-classification-method-for-process-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52060.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11734</span> A Bayesian Multivariate Microeconometric Model for Estimation of Price Elasticity of Demand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jefferson%20Hernandez">Jefferson Hernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20Padilla"> Juan Padilla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=price%20elasticity" title="price elasticity">price elasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20structures" title=" correlation structures"> correlation structures</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20models" title=" Bayesian models"> Bayesian models</a> </p> <a href="https://publications.waset.org/abstracts/122666/a-bayesian-multivariate-microeconometric-model-for-estimation-of-price-elasticity-of-demand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right 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