Process capability assessment for univariate and multivariate non-normal correlated quality characteristics

In today's competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to non-compliance to customer specifications. To assess these losses, industry is extensively using Process Capability Indices for performance evaluation of their processes. Determination of the performance capability of a stable process using the standard process capability indices such as and requires that the underlying quality characteristics data follow a normal distribution. However it is an undisputed fact that real processes very often produce non-normal quality characteristics data and also these quality characteristics are very often correlated with each other. For such non-normal and correlated multivariate quality characteristics, application of standard capability measures using conventional methods can lead to erroneous results. The research undertaken in this PhD thesis presents several capability assessment methods to estimate more precisely and accurately process performances based on univariate as well as multivariate quality characteristics. The proposed capability assessment methods also take into account the correlation, variance and covariance as well as non-normality issues of the quality characteristics data. A comprehensive review of the existing univariate and multivariate PCI estimations have been provided. We have proposed fitting Burr XII distributions to continuous positively skewed data. The proportion of nonconformance (PNC) for process measurements is then obtained by using Burr XII distribution, rather than through the traditional practice of fitting different distributions to real data. Maximum likelihood method is deployed to improve the accuracy of PCI based on Burr XII distribution. Different numerical methods such as Evolutionary and Simulated Annealing algorithms are deployed to estimate parameters of the fitted Burr XII distribution. We have also introduced new transformation method called Best Root Transformation approach to transform non-normal data to normal data and then apply the traditional PCI method to estimate the proportion of non-conforming data. Another approach which has been introduced in this thesis is to deploy Burr XII cumulative density function for PCI estimation using Cumulative Density Function technique. The proposed approach is in contrast to the approach adopted in the research literature i.e. use of best-fitting density function from known distributions to non-normal data for PCI estimation. The proposed CDF technique has also been extended to estimate process capability for bivariate non-normal quality characteristics data. A new multivariate capability index based on the Generalized Covariance Distance (GCD) is proposed. This novel approach reduces the dimension of multivariate data by transforming correlated variables into univariate ones through a metric function. This approach evaluates process capability for correlated non-normal multivariate quality characteristics. Unlike the Geometric Distance approach, GCD approach takes into account the scaling effect of the variance-covariance matrix and produces a Covariance Distance variable that is based on the Mahanalobis distance. Another novelty introduced in this research is to approximate the distribution of these distances by a Burr XII distribution and then estimate its parameters using numerical search algorithm. It is demonstrates that the proportion of nonconformance (PNC) using proposed method is very close to the actual PNC value

Métaheuristiques hybrides pour les problèmes de recouvrement et recouvrement partiel d'ensembles appliqués au problème de positionnement des trous de forage dans les mines

RÉSUMÉ La première étape du cycle minier est l’exploration minérale. Dans cette étape, des longs trous de forage sont forés dans les zones de minéralisation pour extraire des échantillons. Les échantillons sont ensuite analysés et un modèle 3D de la distribution des minéraux dans la mine est construit. Puisque le forage coûte très cher, les géologues et ingénieurs miniers tentent de positionner leurs trous d’une façon qui minimise le coût de forage. Par contre, les techniques courantes utilisées pour minimiser le coût de forage sont peu sophistiquées et ne trouvent généralement pas la solution optimale. Dans cette thèse, nous utilisons des techniques de recherche opérationnelle pour résoudre le problème de positionnement des trous de forage dans les mines. Nous modélisons le problème sous forme d’une variante du problème de recouvrement d’ensembles, qui est un problème très populaire en recherche opérationnelle, et résolvons ce problème à l’aide d’algorithmes métaheuristiques, notamment l’algorithme génétique, la recherche locale itérée et la recherche taboue. Pour évaluer l’efficacité de notre approche, nous comparons les solutions trouvées par notre approche aux solutions trouvées par les approches industrielles sur des problèmes réels. Les résultats obtenus montrent que notre approche permet une réduction des coûts de forage allant jusqu’à 35%. Un autre aspect très important de cette thèse est la résolution du problème de recouvrement d’ensembles (SCP) à l’aide de métaheuristiques. Nous proposons une nouvelle formulation du SCP et un nouvel algorithme pour le résoudre. La nouvelle formulation élimine les problèmes de faisabilité et redondances du SCP. Nos expérimentations ont montré que l’algorithme proposé trouve des meilleurs résultats que la majorit (si pas tous) les algorithmes métaheuristiques existants pour le SCP.---------- ABSTRACT The first steps in the mining cycle are exploration and feasibility. In the exploration stage, geologists start by estimating the potential locations of mineral deposits. Then, they drill many long holes inside the mine to extract samples. The samples are then analyzed and a 3D model representing the distribution of mineralization in the mine is constructed. Because drilling is expensive, geologists and mining engineers try to position their drill holes to cover most potential sites with a minimum amount of drilling. However, the current techniques used to position the drill holes are inefficient and do not generally find the optimal solution. In this thesis, we use operations research techniques to solve the drill holes placement problem. We model the drill holes placement problem as a variant of the set covering problem (which is a very popular optimization problem) and solve the modelled problem using the combination of multiple metaheuristic algorithms, namely the genetic algorithm, iterated local search and tabu search. To evaluate the effectiveness of our approach, we compare the solutions found using our approach to the solutions found by industrial approaches on real world problems. The obtained results show that our approach allow saving up to 35% of drilling cost. Another primary aspect of the thesis is the resolution of the set covering problem (SCP) using metaheuristic approaches. We propose a new formulation of the SCP and a new metaheuristic algorithm to solve it. The new formulation is specially designed for metaheuristic approaches and allows solving the SCP without having to deal with feasibility and set redundancy. Computational results show that our metaheuristic approach is more effective than most (if not all) metaheuristic approaches for the SCP

Mechanical Manipulation and Characterization of Biological Cells

Mechanical manipulation and characterization of an individual biological cell is currently one of the most exciting research areas in the field of medical robotics. Single cell manipulation is an important process in intracytoplasmic sperm injection (ICSI), pro-nuclei DNA injection, gene therapy, and other biomedical areas. However, conventional cell manipulation requires long training and the success rate depends on the experience of the operator. The goal of this research is to address the drawbacks of conventional cell manipulation by using force and vision feedback for cell manipulation tasks. We hypothesize that force feedback plays an important role in cell manipulation and possibly helps in cell characterization. This dissertation will summarize our research on: 1) the development of force and vision feedback interface for cell manipulation, 2) human subject studies to evaluate the addition of force feedback for cell injection tasks, 3) the development of haptics-enabled atomic force microscope system for cell indentation tasks, 4) appropriate analytical model for characterizing the mechanical property of mouse embryonic stem cells (mESC) and 5) several indentation studies on mESC to determine the mechanical property of undifferentiated and early differentiating (6 days under differentiation conditions) mESC. Our experimental results on zebrafish egg cells show that a system with force feedback capability when combined with vision feedback can lead to potentially higher success rates in cell injection tasks. Using this information, we performed experiments on mESC using the AFM to understand their characteristics in the undifferentiated pluripotent state as well as early differentiating state. These experiments were done on both live as well as fixed cells to understand the correlation between the two during cell indentation studies. Our results show that the mechanical property of undifferentiated mESC differs from early differentiating (6th day) mESC in both live and fixed cells. Thus, we hypothesize that mechanical characterization studies will potentially pave the way for developing a high throughput system with force feedback capability, to understand and predict the differentiation path a particular pluripotent cell will follow. This finding could also be used to develop improved methods of targeted cellular differentiation of stem cells for therapeutic and regenerative medicine

Hybrid training approaches to Hidden Markov Model-based acoustic models for automatic speech recognition

Doctor of Philosoph

Automatic analysis of malaria infected red blood cell digitized microscope images

Malaria is one of the three most serious diseases worldwide, affecting millions each year, mainly in the tropics where the most serious illnesses are caused by Plasmodium falciparum. This thesis is concerned with the automatic analysis of images of microscope slides of Giemsa stained thin-films of such malaria infected blood so as to segment red-blood cells (RBCs) from the background plasma, to accurately and reliably count the cells, identify those that were infected with a parasite, and thus to determine the degree of infection or parasitemia. Unsupervised techniques were used throughout owing to the difficulty of obtaining large quantities of training data annotated by experts, in particular for total RBC counts. The first two aims were met by optimisation of Fisher discriminants. For RBC segmentation, a well-known iterative thresholding method due originally to Otsu (1979) was used for scalar features such as the image intensity and a novel extension of the algorithm developed for multi-dimensional, colour data. Performance of the algorithms was evaluated and compared via ROC analysis and their convergence properties studied. Ways of characterising the variability of the image data and, if necessary of mitigating it, were discussed in theory. The size distribution of the objects segmented in this way indicated that optimisation of a Fisher discriminant could be further used for classifying objects as small artefacts, singlet RBCs, doublets, or triplets etc. of adjoining cells provided optimisation was via a global search. Application of constraints on the relationships between the sizes of singlet and multiplet RBCs led to a number of tests that enabled clusters of cells to be reliably identified and accurate total RBC counts to be made. Development of an application to make such counts could be very useful both in research laboratories and in improving treatment of malaria. Unfortunately, the very small number of pixels belonging to parasite infections mean that it is difficult to segment parasite objects and thus to identify infected RBCs and to determine the parasitemia. Preliminary attempts to do so by similar, unsupervised means using Fischer discriminants, even when applied in a hierarchical manner, though suggestive that it may ultimately be possible to develop such a system remain on the evidence currently available, inconclusive. Appendices give details of material from old texts no longer easily accessible