Early identification and detection of abnormal time series patterns is vital for a number of manufacturing. Slide shifts and alterations of time series patterns might be indicative of some anomaly in the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough for practical purposes. In this study we propose the use of a consensus clustering framework. Computational results show robust behavior compared to individual clustering algorithms.

Profile monitoring is a relatively new approach in quality control best used where the process data follow a profile (or curve) at each time period. The essential idea for profile monitoring is to model the profile via some parametric, nonparametric, and semiparametric methods and then monitor the fitted profiles or the estimated random effects over time to determine if there have been changes in the profiles. The majority of previous studies in profile monitoring focused on the parametric modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification. Our work considers those cases where the parametric model for the family of profiles is unknown or at least uncertain. Consequently, we consider monitoring profiles via two techniques, a nonparametric technique and a semiparametric procedure that combines both parametric and nonparametric profile fits, a procedure we refer to as model robust profile monitoring (MRPM). Also, we incorporate a mixed model approach to both the parametric and nonparametric model fits. For the mixed effects models, the MMRPM method is an extension of the MRPM method which incorporates a mixed model approach to both parametric and nonparametric model fits to account for the correlation within profiles and to deal with the collection of profiles as a random sample from a common population. For each case, we formulated two Hotelling's T 2 statistics, one based on the estimated random effects and one based on the fitted values, and obtained the corresponding control limits. In addition,we used two different formulas for the estimated variancecovariance matrix: one based on the pooled sample variance-covariance matrix estimator and a second one based on the estimated variance-covariance matrix based on successive differences. A Monte Carlo study was performed to compare the integrated mean square errors (IMSE) and the probability of signal of the parametric, nonparametric, and semiparametric approaches. Both correlated and uncorrelated errors structure scenarios were evaluated for varying amounts of model misspecification, number of profiles, number of observations per profile, shift location, and in- and out-of-control situations. The semiparametric (MMRPM) method for uncorrelated and correlated scenarios was competitive and, often, clearly superior with the parametric and nonparametric over all levels of misspecification. For a correctly specified model, the IMSE and the simulated probability of signal for the parametric and theMMRPM methods were identical (or nearly so). For the severe modelmisspecification case, the nonparametric andMMRPM methods were identical (or nearly so). For the mild model misspecification case, the MMRPM method was superior to the parametric and nonparametric methods. Therefore, this simulation supports the claim that the MMRPM method is robust to model misspecification. In addition, the MMRPM method performed better for data sets with correlated error structure. Also, the performances of the nonparametric and MMRPM methods improved as the number of observations per profile increases since more observations over the same range of X generally enables more knots to be used by the penalized spline method, resulting in greater flexibility and improved fits in the nonparametric curves and consequently, the semiparametric curves. The parametric, nonparametric and semiparametric approaches were utilized for fitting the relationship between torque produced by an engine and engine speed in the automotive industry. Then, we used a Hotelling's T 2 statistic based on the estimated random effects to conduct Phase I studies to determine the outlying profiles. The parametric, nonparametric and seminonparametric methods showed that the process was stable. Despite the fact that all three methods reach the same conclusion regarding the –in-control– status of each profile, the nonparametric and MMRPM results provide a better description of the actual behavior of each profile. Thus, the nonparametric and MMRPM methods give the user greater ability to properly interpret the true relationship between engine speed and torque for this type of engine and an increased likelihood of detecting unusual engines in future production. Finally, we conclude that the nonparametric and semiparametric approaches performed better than the parametric approach when the user's model is misspecified. The case study demonstrates that, the proposed nonparametric and semiparametric methods are shown to be more efficient, flexible and robust to model misspecification for Phase I profile monitoring in a practical application. Thus, our methods are robust to the common problem of model misspecification. We also found that both the nonparametric and the semiparametric methods result in charts with good abilities to detect changes in Phase I data, and in charts with easily calculated control limits. The proposed methods provide greater flexibility and efficiency than current parametric methods used in profile monitoring for Phase I that rely on correct model specification, an unrealistic situation in many practical problems in industrial applications. / Ph. D.

The evaluation of discrete processes are performed for industrial and healthcare processes. Count data may be used to measure the number of defective items in industrial applications or the incidence of a certain disease at a health facility. Another classification of a discrete random variable is for binary data, where information on an item can be classified as conforming or nonconforming in a manufacturing context, or a patient's status of having a disease in health-related applications. The first phase of this research uses discrete count data modeled from the Poisson and negative binomial distributions in a healthcare setting. Syndromic counts are currently monitored by the BioSense program within the Centers for Disease Control and Prevention (CDC) to provide real-time biosurveillance. The Early Aberration Reporting System (EARS) uses recent baseline information comparatively with a current day's syndromic count to determine if outbreaks may be present. An adaptive threshold method is proposed based on fitting baseline data to a parametric distribution, then calculating an upper-tailed p-value. These statistics are then converted to an approximately standard normal random variable. Monitoring is examined for independent and identically distributed data as well as data following several seasonal patterns. An exponentially weighted moving average (EWMA) chart is also used for these methods. The effectiveness of these methods in detecting simulated outbreaks in several sensitivity analyses is evaluated. The second phase of research explored in this dissertation considers information that can be classified as a binary event. In industry, it is desirable to have the probability of a nonconforming item, p, be extremely small. Traditional Shewhart charts such as the p-chart, are not reliable for monitoring this type of process. A comprehensive literature review of control chart procedures for this type of process is given. The equivalence between two cumulative sum (CUSUM) charts, based on geometric and Bernoulli random variables is explored. An evaluation of the unit and group--runs (UGR) chart is performed, where it is shown that the in--control behavior of this chart is quite misleading and should not be recommended for practitioners. / Ph. D.

This dissertation reports the investigation and combination of optimization methodologies and the result of implementing them within a production environment. A literature survey was conducted on the optimization methodologies Lean Manufacturing and theory of constraints (TOC). A number of production optimization methodologies were studied and considered for application to the case study organisation. Due to the small size and relative simplicity of the operation, these methodologies had to be simplified and combined into a more relevant form. A refractory manufacturer was used as a case study for the investigation into the optimization of the dry batch plant. Lean Manufacturing and TOC are optimization methodologies that could be employed to optimize the dry batch plant. Tools from these methodologies were used to investigate problems identified within the production process that were causing the batching plant to perform non-optimally. A time and motion study was conducted and a process flow chart was created to understand the production process. Wasteful activities were identified using a value stream map and a flow process chart was used to visualise the movement within the production process. A 5-Why analysis was conducted to determine the root causes. An optimization plan was created to eliminate the wasteful activities and the operational measures, that is throughput, inventory and operating expense, were used as to determine what the effect the optimization plan would have on the wasteful activities (Lean Manufacturing) found within the batching plant and the organisation. The results of the combined effect of the optimization plan are discussed focusing on the improvements in the operational measures and the increase in profit from sales. Future research is suggested to improve the benchmarking of the optimization plan and any future improvements that the organisation might implement. / MSc (Development and Management Engineering), North-West University, Potchefstroom Campus, 2015

This dissertation reports the investigation and combination of optimization methodologies and the result of implementing them within a production environment. A literature survey was conducted on the optimization methodologies Lean Manufacturing and theory of constraints (TOC). A number of production optimization methodologies were studied and considered for application to the case study organisation. Due to the small size and relative simplicity of the operation, these methodologies had to be simplified and combined into a more relevant form. A refractory manufacturer was used as a case study for the investigation into the optimization of the dry batch plant. Lean Manufacturing and TOC are optimization methodologies that could be employed to optimize the dry batch plant. Tools from these methodologies were used to investigate problems identified within the production process that were causing the batching plant to perform non-optimally. A time and motion study was conducted and a process flow chart was created to understand the production process. Wasteful activities were identified using a value stream map and a flow process chart was used to visualise the movement within the production process. A 5-Why analysis was conducted to determine the root causes. An optimization plan was created to eliminate the wasteful activities and the operational measures, that is throughput, inventory and operating expense, were used as to determine what the effect the optimization plan would have on the wasteful activities (Lean Manufacturing) found within the batching plant and the organisation. The results of the combined effect of the optimization plan are discussed focusing on the improvements in the operational measures and the increase in profit from sales. Future research is suggested to improve the benchmarking of the optimization plan and any future improvements that the organisation might implement. / MSc (Development and Management Engineering), North-West University, Potchefstroom Campus, 2015

Tuberculosis (TB) has re-emerged as a major worldwide public health challenge in the last decade with an increasing incidence amongst children. The diagnosis of TB in children is difficult as the presentation is not always classical and available diagnostic modalities are imperfect. Diagnosis is, especially complex in developing countries where resources and access to sophisticated diagnostic facilities are limited. Thus practical score charts combining a number of complementary clinical characteristics with affordable special investigations have been developed to aid diagnosis. The new South African primary health care (PHC) nurse-driven system demands that first line nurses be equipped to suspect, diagnose, confirm the diagnosis and treat children with TB. Very little is known about the ability of PHC nurses to diagnose TB in children. In Mpumalanga province relatively low rates of notified paediatric TB prompted an investigation to determine the ability of local PHC nurses to diagnose TB in children and explore whether the PHC setting allowed this. Within method triangulation was used in this quantitative descriptive study by combining a self-completed knowledge survey with clinic visits to audit records and assess access to diagnostic aids and tests. Important deficiencies in knowledge and limited access to certain diagnostic modalities found in this study must be addressed if appropriate management of TB in children is to be assured. / Health Studies / M. A. (Public Health)

近年來,許多調適性管制圖都只探討單一製程,然而現今存在許多相依製程的問題.因此本論文提出兩相依製程之調適性管制圖,並以ATS測量管制圖的績效.本論文所提出的變動抽樣間隔時間之調適性管制圖對於偵測製程中幅度及小幅度的偏移有良好的績效.此外,本論文所提出的變動抽樣樣本大小及變動抽樣間隔時間之調適性管制圖對於偵測製程極小幅度的偏移有良好的績效. / In recent years, many research papers about adaptive control charts all consider a single process step. However, there are many multiple process steps in industry process. In this article, we propose adaptive control charts to monitor two dependent process steps, and their average time to signal (ATS) is calculated by Markov chain approach to measure the performance of these proposed control charts. It has been shown that the performance of the adaptive sampling interval (ASI) control charts in detecting small and moderate shifts in process means is better than the fixed sampling interval control charts, especially for small shifts, and the proposed adaptive sample size and sampling interval (ASSI) control charts have better performance in detecting very small shifts in process means than the fixed sample size and sampling interval control charts and the adaptive sample size control charts.

越來越多帶有時間序列的資料普遍的存在醫學工程、商業統計、財務金融等各領域，例如：在財務金融分析領域中已知的形狀樣式用以預測未來價格趨勢做出買賣的決策。由於時序性資料通常非常的龐大，領域的專家看法也未必相同，所描述出新的形狀樣式剛開始也都是比較粗略的，必須透過不斷的修正才會得到比較精準的結果。有鑒於此，我們實做了一套時序性資料集的形狀查詢語言，透過簡單的語言描述，讓使用者簡便快速的定義出屬於自己的形狀樣式。此外我們也實作出互動式的環境並實際有效率應用於台灣證券交易市場。 / There are more and more time series data in the fields of medical engineering, commerce statistics, finance, etc. For example, in financial analysis, we can forecast the price trends by using some well known chart patterns. People want to find out some new patterns for making their purchase decisions fast and easily. However, it is technical challenging to implement a high-level pattern description language. This thesis implemented a shape query language for time-series datasets. Through the simple syntax, field users can find out there own shape patterns by using a more realistic, easily and fast way. We have also developed an interactive environment that users can apply our shape query language to the data of Taiwan Stock Market efficiently.

In the terrestrial biosphere forests have a significant role as a carbon sink. Under recent climate change, it is increasingly important to detect seasonal change or ‘phenology’ that can influence the global carbon cycle. Monitoring canopies using camera systems has offered an inexpensive means to quantify the phenological changes. However, the reliability is not well known. In order to examine the usefulness of cameras to observe forest phenology, we analysed canopy images taken in two deciduous forests in Japan and England and investigate which colour index is best for tracking forest phenology and predict carbon uptake by trees. A camera test using model leaves under controlled conditions has also carried out to examine sensitivity of colour indices for discriminating leaf colours. The main findings of the present study are: 1) Time courses of colour indices derived from images taken in deciduous forests showed typical patterns throughout the growing season. Although cameras are not calibrated instrument, analysis of images allowed detecting the timings of phenological events such as leaf onset and leaf fall; 2) The strength of the green channel (or chromatic coordinate of green) was useful to observe leaf expansion as well as damage by spring late frost. However, the results of the camera test using model leaves suggested that this index was not sufficiently sensitive to detect leaf senescence. Amongst colour indices, Hue was the most robust metric for different cameras, different atmospheric conditions and different distances. The test also revealed Hue was useful to track nitrogen status of leaves; 3) Modelling results using a light use efficiency model for GPP showed a strong relationship between GPP and Hue, which was stronger than the relationships using alternative traditional indices.

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