Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.

Statistical analysis in multivariate sampling

Magdeburg, Univ., Fak. für Mathematik, Diss., 2011

Multivariate Analysis for Animal Selection in Experimental Research

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Advancing the understanding of brain function with multivariate pattern analysis

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2009

Prognostic Value of Pulmonary Vascular Resistance by Magnetic Resonance in Systolic Heart Failure

Abstract Background: Pulmonary hypertension is associated with poor prognosis in heart failure. However, non-invasive diagnosis is still challenging in clinical practice. Objective: We sought to assess the prognostic utility of non-invasive estimation of pulmonary vascular resistances (PVR) by cardiovascular magnetic resonance to predict adverse cardiovascular outcomes in heart failure with reduced ejection fraction (HFrEF). Methods: Prospective registry of patients with left ventricular ejection fraction (LVEF) < 40% and recently admitted for decompensated heart failure during three years. PVRwere calculated based on right ventricular ejection fraction and average velocity of the pulmonary artery estimated during cardiac magnetic resonance. Readmission for heart failure and all-cause mortality were considered as adverse events at follow-up. Results: 105 patients (average LVEF 26.0 ±7.7%, ischemic etiology 43%) were included. Patients with adverse events at long-term follow-up had higher values of PVR (6.93 ± 1.9 vs. 4.6 ± 1.7estimated Wood Units (eWu), p < 0.001). In multivariate Cox regression analysis, PVR ≥ 5 eWu(cutoff value according to ROC curve) was independently associated with increased risk of adverse events at 9 months follow-up (HR2.98; 95% CI 1.12-7.88; p < 0.03). Conclusions: In patients with HFrEF, the presence of PVR ≥ 5.0 Wu is associated with significantly worse clinical outcome at follow-up. Non-invasive estimation of PVR by cardiac magnetic resonance might be useful for risk stratification in HFrEF, irrespective of etiology, presence of late gadolinium enhancement or LVEF.

Study of human brain metabolites using magnetic resonance spectroscopy methods at 7 TESLA

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2012

Optimal designs for multivariate linear models

Magdeburg, Univ., Fak. für Mathematik, Diss., 2013

Subjektive Vaterschaftskonzepte und ihre Determinanten in drei Familienentwicklungsphasen : eine latente Profilanalyse multipler Gruppen und multivariate Untersuchung verschiedener Determinanten

Magdeburg, Univ., Fak. für Humanwiss., Diss., 2013

The investigation of glutamine and glutamate in the human brain using MR spectroscopy at 7 Tesla

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2014

A multivariate study of the family molossidae (mammalia, chiroptera) : morphology, ecology, evolution / Patricia Waring Freeman.

n.s. no.7(1981)

Investigation of the classification of the rodent genus Eumys from the Middle Oligocene of the Big Badlands of South Dakota using multivariate statistical analysis / Sue Vilhauer Rosser --.

v.39:no.3(1978)

Use and users of electronic journals at Catalan universities: the results of a survey

Es presenten els resultats d’una enquesta sobre l’ús de revistes electròniques realitzada al professorat de les universitats que formen el Consorci de Biblioteques Universitàries de Catalunya (CBUC). Els resultats mostren un elevat grau de coneixement de la col·lecció de revistes electròniques entre el personal docent i investigador i una creixent preferència pel format electrònic en detriment de l’imprès. L’alt grau de coneixement i d’ús dels títols electrònics, i la preferència per aquest suport, comporten una elevada valoració de la col·lecció de revistes electròniques. Al mateix temps, la major part dels usuaris preveu un increment de l’ús dels títols electrònics durant els propers anys. Els resultats també confirmen la importància de la disciplina i de l’edat com a factors explicatius de l’ús de les revistes electròniques.

The Discrimination of Colored Acrylic, Cotton, and Wool Textile Fibers Using Micro-Raman Spectroscopy. Part 1 : In Situ Detection and Characterization of Dyes

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633-nm laser did not provide Raman information. The 514-nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830-nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.

Impact of preoperative risk factors on morbidity after esophagectomy: is there room for improvement?

BACKGROUND: Despite progress in multidisciplinary treatment of esophageal cancer, oncologic esophagectomy is still the cornerstone of therapeutic strategies. Several scoring systems are used to predict postoperative morbidity, but in most cases they identify nonmodifiable parameters. The aim of this study was to identify potentially modifiable risk factors associated with complications after oncologic esophagectomy. METHODS: All consecutive patients with complete data sets undergoing oncologic esophagectomy in our department during 2001-2011 were included in this study. As potentially modifiable risk factors we assessed nutritional status depicted by body mass index (BMI) and preoperative serum albumin levels, excessive alcohol consumption, and active smoking. Postoperative complications were graded according to a validated 5-grade system. Univariate and multivariate analyses were used to identify preoperative risk factors associated with the occurrence and severity of complications. RESULTS: Our series included 93 patients. Overall morbidity rate was 81 % (n = 75), with 56 % (n = 52) minor complications and 18 % (n = 17) major complications. Active smoking and excessive alcohol consumption were associated with the occurrence of severe complications, whereas BMI and low preoperative albumin levels were not. The simultaneous presence of two or more of these risk factors significantly increased the risk of postoperative complications. CONCLUSIONS: A combination of malnutrition, active smoking and alcohol consumption were found to have a negative impact on postoperative morbidity rates. Therefore, preoperative smoking and alcohol cessation counseling and monitoring and improving the nutritional status are strongly recommended.