Control charts are fundamental SPC tools for process monitoring. When a control chart or combination of charts signals, knowing the change point, which distributional parameter changed, and/or the change size helps to identify the cause of the change, remove it from the process or adjust the process back in control correctly and immediately. In this study, we proposed using maximum likelihood (ML) estimation of the current process parameters and their ML confidence intervals after a signal to identify and estimate the changed parameters. The performance of this ML diagnostic procedure is evaluated for several different charts or chart combinations for the cases of sample sizes and , and compared to the traditional approaches to diagnostics. None of the ML and the traditional estimators performs well for all patterns of shifts, but the ML estimator has the best overall performance. The ML confidence interval diagnostics are overall better at determining which parameter has shifted than the traditional diagnostics based on which chart signals. The performance of the generalized likelihood ratio (GLR) chart in shift detection and in ML diagnostics is comparable to the best EWMA chart combination. With the application of the ML diagnostics naturally following a GLR chart compared to the traditional control charts, the studies of a GLR chart during process monitoring can be further deepened in the future. / Ph. D.

Traditional control charts for process monitoring are based on taking samples from the process at regular time intervals. However, it is often possible in practice for observations, and even entire samples, to be missing. This dissertation investigates missing observations in Exponentially Weighted Moving Average (EWMA) and Multivariate EWMA (MEWMA) control charts. The standardized sample mean is used since this adjusts the sample mean for the fact that part of the sample may be missing. It also allows for constant control limits even though the sample size varies randomly. When complete samples are missing, the weights between samples should also be adjusted. In the univariate case, three approaches for adjusting the weights of the EWMA control statistic are investigated: (1) ignoring missing samples; (2) adding the weights from previous consecutive missing samples to the current sample; and (3) increasing the weights of non-missing samples in proportion, so that the weights sum to one. Integral equation and Markov chain methods are developed to find and compare the statistical properties of these charts. The EI chart, which adjusts the weights by ignoring the missing samples, has the best overall performance. The multivariate case in which information on some of the variables is missing is also examined using MEWMA charts. Two methods for adjusting the weights of the MEWMA control statistic are investigated and compared using simulation: (1) ignoring all the data at a sampling point if the data for at least one variable is missing; and (2) using the previous EWMA value for any variable for which all the data are missing. Both of these methods are examined when the in-control covariance matrix is adjusted at each sampling point to account for missing observations, and when it is not adjusted. The MS control chart, which uses the previous value of the EWMA statistic for a variable if all of the data for that variable is missing at a sampling point, provides the best overall performance. The in-control covariance matrix needs to be adjusted at each sampling point, unless the variables are independent or only weakly correlated. / Ph. D.

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A performance analysis of three different Minimax control charts is performed with respect to their Chi-Square control chart counterparts under several different conditions. A unique control chart must be constructed for each process described by a unique combination of quality characteristic mean vector and associated covariance matrix. The three different charts under consideration differ in the number of quality characteristic variables of concern. In each case, without loss of generality the in-control quality characteristic mean vector is assumed to have zero entries and the associated covariance matrix is assumed to have non-negative entries. The performance of the Chi-Square and Minimax charts are compared under different values of the sample size, the probability of a Type I error, and selected shifts in the quality characteristic mean vector. Minimax and Chi-Square charts that are compared share identical in-control average run lengths (ARL) making the out-of-control ARL the appropriate performance measure. A combined Tausworthe pseudorandom number generator is used to generate the out-of-control mean vectors. Issues regarding multivariate uniform pseudorandom number generation are addressed. / Master of Science

This project involved the design and testing of a prototype in-line chip quality monitor for gathering process control information for the manufacturers of wood chips. This monitor specifically addresses three common complaints with current chip sampling procedures. Chip sampling occurs too late in the process. It is inadequate. It is too infrequent to develop management information. The monitor is composed of a double screen drum separator to divide chips into oversize, accepts, and pins/fines. Counterbalanced tip buckets are used to weigh each size class. Tip bucket cycles are recorded by a computer via magnetic proximity switches attached to each bucket. This information is then used to chart production of chip size classes, updated continuously over the sorting period. This monitor is capable of sorting one ton of chips per hour. Two trials were conducted to test the monitor. One in a lab environment, and one on site at a chip mill. Both trials compared monitor output with independent samples classified using a Williams classifier. The trials showed that outputs were consistent with Williams output. This monitor can effectively chart chip distribution information. This process control information provides the manufacturer with immediate knowledge of chipper performance. / Master of Science

No / Rational, aim, and objectives: A positive patient safety culture in maternity units is linked to higher quality of care and better outcomes for mothers. However, safety culture varies across maternity units. Analyses of variation in safety culture using statistical process control (SPC) methods may help provider units to learn from each other's performance. This study aims to measure patient safety culture across maternity units in Oman using SPC methods. Methods: The 36-item Safety Attitude Questionnaire (SAQ) was distributed to all doctors, nurses, and midwifes working in ten maternity care units in Oman's hospitals and analysed using SPC methods. The SAQ considers six domains: job satisfaction, perception of management, safety climate, stress recognition, teamwork, and work condition. Results: Of the 892 targeted participants, 735 (82%) questionnaires were returned. The overall percentage of positive safety responses in all hospitals ranged from 53% to 66%, but no hospital had the targeted response of above 75%. Job satisfaction had the highest safety score (4.10) while stress recognition was the lowest (3.17). SPC charts showed that the overall percentage of positive responses in three maternity units (H1, H7, and H10) was above and one (H4) was below the control limits that represent special cause variation that merits further investigation. Conclusion: Generally, the safety culture in maternity units in Oman is below target and suggests that considerable work is required to enhance safety culture. Several maternity units showed evidence of high/low special cause variation that may offer a useful starting point for understanding and enhancing safety culture.

Visual acuity is reduced when optotypes are viewed in the presence of surrounding contours. This reduction in acuity is known as the crowding effect and is thought to be caused by a varying combination of contour interaction, gaze instability and attention. Traditional studies have used single optotypes surrounded by flanking bars to investigate crowding. Such targets may not realistically replicate the crowding effect inherent in clinical vision charts. The aim of this thesis was to systematically investigate the effect of crowding on visual thresholds in subjects with normal vision and in subjects with amblyopia, using specially designed charts. In the 1st and 2nd experiment, contour interaction was assessed using a high (80 %) and low contrast (5.8%) Sheridan Gardiner repeat letter (SGRL) chart in subjects with normal vision. The effect of contour interaction was investigated by varying the inter-letter separation in the SGRL chart. Significant contour interaction was obtained at the abutting condition for both the contrast conditions. In the 3rd experiment the same protocol was repeated but in amblyopes. Significant contour interaction was obtained at 0.2 letter separation and the abutting condition for both the contrast conditions. The effect of contour interaction appears to be less for low contrast than for high contrast letters in normal, non-amblyopic and amblyopic eyes. Finally, in the 4th experiment a Sheridan Gardiner Complex Interaction (SGCI) chart that requires imposed gaze fixations was constructed to measure visual acuity in normal’s and amblyopes. The effect of any gaze instability on crowding was investigated by comparing SGRL thresholds to SGCI thresholds. The SGCI thresholds were higher than the SGRL thresholds at all the separations measured, suggesting an important effect of gaze instability on crowding. In conclusion, this research has shown that gaze instability is an important component of the crowding effect for letter chart acuity measurements. Visual acuity especially when screening for amblyopia should be measured using a whole optotype chart that requires optotype to optotype fixation.

An recent finding shows that heart rate data possess self-similar property, which is characterized by a parameter H, as well as a long range dependent parameter d. We estimate H by the EBP(Embedded Branching Process) method to derive the fractional parameter d in the first part. The heart rate and R-R interval data are found to have high differencing parameter(d=0.8 ~0.9) and against the normality assumption. Thus the heart rate and R-R interval data are first fractionally differenced of order 0.5 to achieve stationarity. In the second part, we analyze the RR-interval data on the physionet and obtain the long range parameters. After fractionally differencing 0.5 order, the EBP method is adapted to estimate the long range parameter d. The EWMA and EWRMS control charts of the I(d) processes are constructed to monitor the heart rate mean level and variability, respectively for the 18 RR-interval data sets from the physionet. For the EWMA control chart the out of control percentages are chosen to the nominal probability. However, the out of control percentages are affected by the skewness and kurtosis of the process distribution for the EWRMS control carts. Generally speaking, the I(d)-EWMA and I(d)-EWRMS control charts provide a proper monitor system for heart rate mean level and variability.

The managing of complex business processes, which are changed due to globalization, calls for the development of powerful information systems that offer generic process modeling and process execution capabilities. Even though contemporary information systems are more and more utilized in enterprises, their actual impact in automatizing complex business process is still limited by the difficulties encountered in design phase. Actually this design phase is time consuming, often subjective and incomplete. In the scope of this study, a reverse approach is followed. Instead of starting with process design, the method of discovering interesting patterns from the navigation traces is taken as basis and a new data analysis methodology named &ldquo / From-to Chart Based Process Discovery&rdquo / is proposed. In this hybrid methodology &ldquo / from-to chart&rdquo / , which is fundamentally dedicated to material handling issues on production floor, is used as the front-end to monitor the transitions among activities of a realistic event log and convert these raw relations into optimum activity sequence. Then a revised version of process mining, which is the back-end of this methodology, upgrades optimum activity sequence into process model.