Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation.  There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate.   Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements.  In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001.  Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels.  Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels.   We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data.  The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm.  EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery.   We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values.  These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential.  These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters.  Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data.  They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects. / Ph. D.

[pt] É importante investigar formas de aumentar a eficiência de gráficos de controle estatístico de processos por atributos, porque o controle por atributos tipicamente exige tamanhos de amostra muito grandes para se obter um tempo rápido de sinalização de desvios no processo, e estes tamanhos de amostra podem ser altamente indesejáveis ou mesmo inviáveis em diversas situações práticas. Os esquemas adaptativos, que consistem em variar um ou mais dos parâmetros do gráfico de controle (tamanho de amostra, intervalo de tempo entre amostras e abertura dos limites de controle) de acordo com a informação precedente fornecida pelo gráfico (isto é, de acordo com a posição do último ponto registrado), proporcionam, para a detecção de desvios pequenos a moderados no processo, uma melhor eficiência que os esquemas tradicionais, de parâmetros fixos. Enquanto que esquemas adaptativos têm sido propostos para gráficos de controle por variáveis, para gráficos por atributos - apesar da necessidade e oportunidade acima mencionada de investigar formas de aumentar a eficiência de gráficos de controle estatístico de processos por atributos - há uma lacuna na literatura, a ser preenchida. Esta é a motivação do presente trabalho, que propõe um esquema adaptativo para gráficos decontrole por atributos, aplicável tanto a gráficos de np como de c, variando todos os parâmetros do gráfico. Desenvolve-se o modelo matemático para cálculo das medidas de desempenho; este modelo foi implementado em uma planilha eletrônica, o que permitiu analisar quantitativamente o desempenho do esquema em uma larga gama de casos, comparando-o ainda com o desempenho de gráficos tradicionais (com parâmetros fixos), e com o de outros esquemas adaptativos, com menor número de parâmetros variando. O esquema proposto mostrou-se sensivelmente mais eficiente na maioria das situações de interesse, respondendo portanto à necessidade, que motivou o trabalho, de buscar formas de aumentar a eficiência de gráficos por atributos. Resultados adicionais do trabalho são: a identificação das situações em que cada esquema é mais eficiente (ou do esquema mais eficiente em cada situação) e conjuntos de valores recomendados para os parâmetros dos gráficos em cada situação. Estes resultados facilitam a operacionalização da ferramenta para uso na prática. / [en] It is important to investigate ways to improve the efficiency of the statistical control charts for attributes, because the controls for attributes typically require very large sample sizes for quick detection of changes in the process, and these sample sizes can be extremely undesirable or eventually unfeasible in many practical situations. The adaptive schemes, which vary one or more parameters of the control charts (sample size, sampling interval and control limit width) according to the most recent information about the process (the position of the last sample point in the chart), were proven to be more efficient than the traditional (fixed parameters) scheme in detecting small to moderate changes in the process. Although there is a large volume of work on adaptive schemes applied to variables control charts, there is a lack of adaptive schemes for control charts for attributes in the literature, despite the already mentioned need and opportunity of investigating ways to improve the efficiency of the statistical control charts for attributes. This is the motivation of this work. A fully adaptive scheme is proposed for both np and charts. The mathematical model is developed for the measures of performance; this model was implemented in a spreadsheet, and used for the quantitative evaluation of the scheme efficiency, and comparison with the fixed-parameter (traditional) scheme and also with other adaptive schemes, with only one or two parameters variable. The proposed scheme proved to be significantly more efficient in the majority of the situations of interest, answering the necessity of finding ways to improve the efficiency of control charts for attributes. Additional results of this work are: the identification of the situations in which each of the schemes here analyzed is the most efficient (or, the other way around: indication of the most efficient scheme for each situation), and a set of recommended values for the design parameters for each scheme, in each situation. These results are meant to make easier the adoption of the scheme in practice, increasing thereby its usefulness. / [es] El desarrollo de técnicas que aumenten la eficiencia de gráficos de control estadístico de procesos por atributos tiene gran importancia porque el control por atributos típicamente exige tamaños de muestra muy grandes para obtener un tiempo rápido de señalización de desvíos en el proceso. En diversas situaciones prácticas estos tamaños de muestra pueden ser altamente indeseables o incluso inviables. Los esquemas adaptativos, que consisten en variar uno o más parámetros del gráfico de control (tamaño de muestra, intervalo de tiempo entre muestras y abertura de los límites de control) de acuerdo con la información precedente ofrecida por el gráfico (esto es, de acuerdo con la posición del último punto registrado), proporcionan, para la detección de desvíos pequeños a moderados, una mayor eficiencia que los esquemas tradicionales, de parámetros fijos. Mientras que los esquemas adaptativos han sido propuestos para gráficos de control por variables, para gráficos por atributos - a pesar de la necesidad y oportunidad mencionada de investigar formas de aumentar la eficiencia de gráficos de control estadístico de procesos por atributos - no existen muchas referencias en la literatura. Esta es la motivación del presente trabajo, que propone un esquema adaptativo para gráficos de control por atributos, aplicable tanto a gráficos de np como de c, variando todos los parámetros del gráfico. Se desarrolla el modelo matemático para el cálculo de las medidas de desempeño; este modelo fue implementado en una planilla electrónica, que permitió analizar cuantitativamente el desempeño del esquema en una amplia gama de casos, comparando con el desempeño de gráficos tradicionales (con parámetros fijos), y con el de otros esquemas adaptativos, con menor número de parámetros variando. El esquema propuesto se mostró sensiblemente más eficiente en la mayoría de las situaciones de interés que motivaron el trabajo. Resultados adicionales del trabajo son: la identificación de las situaciones en que cada esquema es más eficiente (o del esquema más eficiente en cada situación) y conjuntos de valores recomendados para los parámetros de los gráficos en cada situación. Estos resultados facilitan la operacionalización de la ferramenta para uso en la práctica.

This Master's thesis focuses on improving the understanding and monitoring of the Hot Isostatic Pressing (HIP) sintering process used by Seco Tools AB to manufacture cemented carbides for cutting tools. While essential for producing cutting tools with superior hardness and toughness the HIP sintering process introduces a complex relationship between the selected process parameters and the achieved materials properties. With the goal of establishing batch process monitoring capabilities, this master thesis employs Multivariate Statistical Process Control (MSPC) strategies through the creation of Batch Evolution Models (BEMs) and Batch Level Models (BLMs) to monitor, predict end-product quality, and analyze the batch production HIP sintering process.  The developed models effectively account for significant variation in the HIP sintering process and demonstrate potential in identifying deviant batches. Enhancements to the models' performance are achieved through the incorporation of preprocessing, phase-specific variable selection, and specialized model training. These proposed enhancements yield discernible improvements, as evidenced by enhanced model fit and other statistical metrics.  Challenges arise when the models are tested with real-time data due to progressive changes in some tracked process variables. Block-scaling is applied to restore the real-time monitoring capabilities, but also introduces additional complexity to the models. In addition, this master thesis highlights the need for continuous and regular maintenance of these models to ensure real-time monitoring and anomaly detection capabilities. The models demonstrate varied effectiveness in predicting final product quality. For instance, they exhibit some potential in predicting Magnetic Saturation (MS), but their ability to predict Magnetic Coercivity (HC) seems nonexistent. Despite attempts to improve the predictive abilities the models are still not able to confidently predict these metrics. The master’s thesis highlights variability in powder contents and access to data of known quality nonconformities as potential areas for improving the predictive models.

Sig sigma is a methodology where companies strive to reproduce results ending up having a 99.9996% chance their product will be void of defects. In order for companies to reach six sigma, statistical process control (SPC) needs to be introduced. SPC has many different tools associated with it, control charts being one of them. Control charts play a vital role in managing how a process is behaving. Control charts allow users to identify special causes, or shifts, and can therefore change the process to keep producing good products, free of defects. There are many factories and manufacturing facilities having implemented some sort of statistical process control. St. Jude Medical implemented control charts to monitor different tools on the manufacturing line. How the data is entered and stored poses a difficult situation for the person monitoring the processes. The program used to keep the control charts is not user friendly and difficult to use. Another program can be produced to provide a greater level of efficiency. The goals of this project are to stress how important control charts are in the manufacturing world, what problems are currently seen for operators and supervisors, and how a new and improved program can help fix the current situation. This paper goes into the reasons for the change as well has what has been improved.

The appropriate selection of failure criterion for solder joint studies is necessary to correctly estimate reliability life. The objective of this study is to compare the effect of different failure criteria on the reliability life estimation. The four failure criteria in this study are a 20% resistance increase defined in the IPC-9701A standard, a resistance beyond 500 Ω, an infinite resistance (hard open), and a failure criterion based on X-bar and R control charts. Accelerated thermal cycling conditions of a low-silver BGA study included 0°C to 100 °C with ten minute dwell times and -40°C to 125°C with ten minute dwell times. The results show that the life estimation based on X-bar and R failure criterion is very similar to the life estimation when a 20% resistance increase defined in the IPC-9701A failure criterion is used. The results also show that the reliability life would be overestimated if the failure criterion of a resistance threshold of 500 Ω or an infinite resistance (hard open) is used.

INTRODUCTION: Inefficiencies in health systems waste resources and reduce quality of care. At Dana Farber Cancer Institute, the majority of patient complaints centered around wait times often due to suboptimal workflows. To reduce infusion wait time (IWT), three initiatives were piloted on one outpatient clinic floor: 1) premixing qualified chemotherapy drugs during off-peak hours of day, 2) sharing late sign data with providers to encourage them to sign medication orders as soon as patients were seen in exam, and 3) moving a pharmacist physically closer to providers on the exam side to support the medication verification process. The goal of this study was to evaluate the three improvement initiatives and assess their impact on IWT. METHODS AND MATERIALS: The baseline period was September–December 2017 and the implementation period was January–September 2018. The three initiatives were implemented in a staggered fashion within the implementation period. IWT was defined as the later of infusion appointment or check-in time to first infusion medication administration time, and the 75th percentile was used to monitor change. Process metrics were also defined and monitored for each improvement initiative. A unique SQL code was written to pull and merge data tables from the electronic medical record (Epic) and real-time locating system (RTLS). Tableau and SAS were used to clean, analyze, and visualize time series data. RESULTS: The 75th percentile IWT decreased from baseline 80 minutes to 68 minutes once all three initiatives were implemented. Moving the pharmacist closer to the exam side where providers signed their medication orders shortened the 75th percentile medication order verification time by 15 minutes (38% reduction) for the gynecology cancer group. Provider late order signing percentages also significantly decreased from 24% to 13%. A higher percentage of qualified premix orders were prepared during off-peak hours (from 78% to 91%) and before the infusion appointment time (from 43% to 87%). Feedback from frontline staff regarding the initiatives were extremely positive. CONCLUSION: Reducing chemotherapy infusion wait time is possible at a comprehensive cancer center. More efficient workflows can translate to patient satisfaction, reduce cost, and improve the quality of care. / 2021-08-29T00:00:00Z

In many applications, the quality of process outputs is described by more than one characteristic variable. These quality variables usually follow a multivariate normal (MN) distribution. This dissertation discusses the monitoring of the mean vector and the covariance matrix of MN processes. The first part of this dissertation develops a statistical process control (SPC) chart based on a generalized likelihood ratio (GLR) statistic to monitor the mean vector. The performance of the GLR chart is compared to the performance of the Hotelling Χ² chart, the multivariate exponentially weighted moving average (MEWMA) chart, and a multi-MEWMA combination. Results show that the Hotelling Χ² chart and the MEWMA chart are only effective for a small range of shift sizes in the mean vector, while the GLR chart and some carefully designed multi-MEWMA combinations can give similarly better overall performance in detecting a wide range of shift magnitudes. Unlike most of these other options, the GLR chart does not require specification of tuning parameter values by the user. The GLR chart also has the advantage in process diagnostics: at the time of a signal, estimates of change-point and out-of-control mean vector are immediately available to the user. All these advantages of the GLR chart make it a favorable option for practitioners. For the design of the GLR chart, a series of easy to use equations are provided to users for calculating the control limit to achieve the desired in-control performance. The use of this GLR chart with a variable sampling interval (VSI) scheme has also been evaluated and discussed. The rest of the dissertation considers the problem of monitoring the covariance matrix. Three GLR charts with different covariance matrix estimators have been discussed. Results show that the GLR chart with a multivariate exponentially weighted moving covariance (MEWMC) matrix estimator is slightly better than the existing method for detecting any general changes in the covariance matrix, and the GLR chart with a constrained maximum likelihood estimator (CMLE) gives much better overall performance for detecting a wide range of shift sizes than the best available options for detecting only variance increases. / Ph. D.

The objective of this dissertation is to investigate GLR control charts based on a sequential sampling scheme (SS GLR charts). Phase II monitoring is considered and the goal is to quickly detect a wide range of changes in the univariate normal process mean parameter and/or the variance parameter. The performance of the SS GLR charts is evaluated and design guidelines for SS GLR charts are provided so that practitioners can easily apply the SS GLR charts in applications. More specifically, the structure of this dissertation is as follows: We first develop a two-sided SS GLR chart for monitoring the mean μ of a normal process. The performance of the SS GLR chart is evaluated and compared with other control charts. The SS GLR chart has much better performance than that of the fixed sampling rate GLR chart. It is also shown that the overall performance of the SS GLR chart is better than that of the variable sampling interval (VSI) GLR chart and the variable sampling rate (VSR) CUSUM chart. The SS GLR chart has the additional advantage that it requires fewer parameters to be specified than other VSR charts. The optimal parameter choices are given, and regression equations are provided to find the limits for the SS GLR chart. If detecting one-sided shifts in μ is of interest, the above SS GLR chart can be modified to be a one-sided chart. The performance of this modified SS GLR chart is investigated. Next we develop an SS GLR chart for simultaneously monitoring the mean μ and the variance 𝜎² of a normal process. The performance and properties of this chart are evaluated. The design methodology and some illustrative examples are provided so that the SS GLR chart can be easily used in applications. The optimal parameter choices are given, and the performance of the SS GLR chart remains very good as long as the parameter choices are not too far away from the optimized choices. / Ph. D.

No description available.

The initial portion of this research focuses on appropriate parameter estimators within a general context of multivariate quality control. The goal of Phase I analysis of multivariate quality control data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. Because of the inherent difficulties in computation many algorithms have been proposed to obtain them. We consider the subsampling algorithm to obtain the MVE estimators and the FAST-MCD algorithm to obtain the MCD estimators. Previous studies have not clearly determined which of these two estimation methods is best for control chart applications. The comprehensive simulation study here gives guidance for when to use which estimator. Control limits are provided. High breakdown estimation methods such as MCD and MVE can be applied to a wide variety of multivariate quality control data. The final, lengthier portion of this research considers profile monitoring. Profile monitoring is a relatively new technique in quality control used when the product or process quality is best represented by a profile (or a curve) at each time period. The essential idea is often to model the profile via some parametric method and then monitor the estimated parameters over time to determine if there have been changes in the profiles. Because the estimated parameters may be correlated, it is convenient to monitor them using a multivariate control method such as the T-squared statistic. Previous modeling methods have not incorporated the correlation structure within the profiles. We propose the use of mixed models (both linear and nonlinear) to monitor linear and nonlinear profiles in order to account for the correlation structure within a profile. We consider various data scenarios and show using simulation when the mixed model approach is preferable to an approach that ignores the correlation structure. Our focus is on Phase I control chart applications. / Ph. D.