A New SVDD-Based Multivariate Non-parametric Process Capability Index

Process capability index (PCI) is a commonly used statistic to measure ability of a process to operate within the given specifications or to produce products which meet the required quality specifications. PCI can be univariate or multivariate depending upon the number of process specifications or quality characteristics of interest. Most PCIs make distributional assumptions which are often unrealistic in practice. This paper proposes a new multivariate non-parametric process capability index. This index can be used when distribution of the process or quality parameters is either unknown or does not follow commonly used distributions such as multivariate normal

Optimization of a portable NIR device for the optical supervision of milk coagulation process

The coagulation of milk is the fundamental process in cheese-making, which is based on a gel formation as consequence of physicochemical changes taking place in the casein micelles. Monitoring the whole process of milk curd formation is a dedicated process for dairy researchers and cheese companies. In addition to advances in composition-based applications by means of NIR spectroscopy, researchers are pursuing dynamic applications that show promise especially with regard to tracking a sample in situ during processing The objective of this work is to propose an original portable NIR equipment to supervise the milk coagulation process. The experiments have been carried out on sheep and goat milk, by immersion of the probe directly in the liquid and acquiring spectrum each 1 minute during the 30 minutes of coagulation process. The increasing values of transflected light registered allow identifying, based on PCA analysis, the different kinetics that occur along the gel formation and the time to reach the optimal gel firmness to cut the cur

Integrated Projection and Regression Models for Monitoring Multivariate Autocorrelated Cascade Processes

This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed. The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed

A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the proper Gaussian kernel bandwidth to achieve the best classification performance is always a challenging problem. This paper proposes a new automatic, unsupervised Gaussian kernel bandwidth selection approach which is used with a multiclass SVDD classification scheme. The performance of the multiclass SVDD classification scheme is evaluated on three frequently used hyperspectral data sets, and preliminary results show that the proposed method can achieve better performance than published results on these data sets

Nevirapine- and efavirenz-associated hepatotoxicity under programmatic conditions in Kenya and Mozambique.

To describe the frequency, risk factors, and clinical signs and symptoms associated with hepatotoxicity (HT) in patients on nevirapine- or efavirenz-based antiretroviral therapy (ART), we conducted a retrospective cohort analysis of patients attending the ART clinic in Kibera, Kenya, from April 2003 to December 2006 and in Mavalane, Mozambique, from December 2002 to March 2007. Data were collected on 5832 HIV-positive individuals who had initiated nevirapine- or efavirenz-based ART. Median baseline CD4+ count was 125 cells/μL (interquartile range [IQR] 55-196). Over a median follow-up time of 426 (IQR 147-693) days, 124 (2.4%) patients developed HT. Forty-one (54.7%) of 75 patients with grade 3 HT compared with 21 (80.8%) of 26 with grade 4 had associated clinical signs or symptoms (P = 0.018). Four (5.7%) of 124 patients with HT died in the first six months compared with 271 (5.3%) of 5159 patients who did not develop HT (P = 0.315). The proportion of patients developing HT was low and HT was not associated with increased mortality. Clinical signs and symptoms identified 50% of grade 3 HT and most cases of grade 4 HT. This suggests that in settings where alanine aminotransferase measurement is not feasible, nevirapine- and efavirenz-based ART may be given safely without laboratory monitoring