The aim of this study was to propose a nonlinear multiscale principal component analysis (NLMSPCA) methodology for process monitoring and fault detection based upon multilevel wavelet decomposition and nonlinear principal component analysis via an input-training neural network. Prior to assessing the capabilities of the monitoring scheme on a nonlinear industrial process, the data is first pre-processed to remove heavy noise and significant spikes through wavelet thresholding. The thresholded wavelet coefficients are used to reconstruct the thresholded details and approximations. The significant details and approximations are used as the inputs for the linear and nonlinear PCA algorithms in order to construct detail and approximation conformance models. At the same time non-thresholded details and approximations are reconstructed and combined which are used in a similar way as that of the thresholded details and approximations to construct a combined conformance model to take account of noise and outliers. Performance monitoring charts with non-parametric control limits are then applied to identify the occurrence of non-conforming operation prior to interrogating differential contribution plots to help identify the potential source of the fault. A novel summary display is used to present the information contained in bivariate graphs in order to facilitate global visualization. Positive results were achieved. / Dissertation (M Eng (Control Engineering))--University of Pretoria, 2007. / Chemical Engineering / unrestricted

Vector-borne diseases, such as those transmitted by mosquitoes, pose a significant public health concern in many countries worldwide. In this thesis, I explored the role of a number of risk factors defined at multiple scales on vector-borne disease prevalence, focusing on malaria and arboviral infections in several regions of North-Eastern Tanzania, with the principal aim of improving the overall diagnosis of febrile illness in this region. First, I investigated the influence of household-wealth on prevalence of malaria and arboviral infections using principal component analysis (PCA), and then described the methodological challenges associated with this statistical technique when used to compute indices from smaller datasets. I then employed a multilevel modelling approach to simultaneously incorporate household-level anthropogenic factors and village-level environmental characteristics to investigate key determinants of Anopheles malaria vector density among rural households. These analyses provided methodologically rigorous approaches to studying vector-borne diseases at a very fine-scale and also have significant public health relevance as the research findings can assist in guiding policy decisions regarding surveillance efforts as well as inform where and when to prioritize interventions.

Recent inquiry into the nomenclature of several species within Spinocyrtia has led to questions concerning name applicability and validity, particularly whether Delthyris granulosa and Spinocyrtia (Spirifer) granulosa are synonymous. This study utilized two-dimensional outline landmark analysis, a form of geometric morphometric analysis, to evaluate interspecific variation among these species. I took over a thousand photographs of over a hundred specimens of brachiopods belonging to the family Spinocyrtiidae. Ninety-six specimens originated from the Givetian outcrop belt of New York state, three were from northwestern Ohio, there was single Canadian specimen, and there was a single German specimen. The results from these analyses indicate that the mophospaces of Spinocyrtia (Spirifer) congesta, S. (Spirifer) granulosa, and S?. (Spirifer) marcyi are statistically (p < 0.05) distinct from one another.

The research shows a novel approach for the Magnetic Anomaly Differentiation and Localization Algorithm, which simultaneously localizes multiple magnetic anomalies with weak total field signatures (tens of nT). In particular, it focuses on the case where there are two homogeneous targets with known magnetic moments. This was done by analyzing the magnetic signals and adapting Independent Component Analysis (ICA) and Simulated Annealing (SA) to solve the problem statement. The results show the groundwork for using a combination of fastICA and SA to give localization errors of 3 meters or less per target in simulation and achieved a 58% success rate. Experimental results experienced additional errors due to the effects of magnetic background, unknown magnetic moments, and navigation error. While one target was localized within 3 meters, only the latest experimental run showed the second target approaching the localization specification. This highlighted the need for higher signal-to-noise ratio and equipment with better navigational accuracy. The data analysis was used to provide recommendations on the needed equipment to minimize observed errors and improve algorithm success. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

There are numerous approaches to quantitatively measure well-being. Most well-beingresearch are based on income or health situation from economics perspective. The needfor research on women’s relationship between education and well-being is an area thathas not been fully investigated. It is also important to know how the situation ofwomen’s well-being compare with men’s. The purpose of this research is to estimatewomen’s well-being and understand how well-being women is compared with men inChina. Different characteristics of men and women is considered and estimate thespecific relationship between education and well-being. Two measure of well-being areused: self-assessed unidimensional subjective well-being and parametrically estimatedmultidimensional well-being. Two measurement will help to understand the differencebetween subjectivity and objectivity of well-being. To achieve this goal, this researchcomputes and compares the well-being of 34,054 women and men by using ChineseGeneral Social Survey in 2012, 2013 and 2015. Well-being is measured by computingmultidimensionally by principal component analysis which depend on differentdomains of identity, capability, material well-being. All the domains contribute toindividual’s well-being. The findings suggest that, multidimensional well-being indexdiffer from the subjective well-being in ranking individual grouped by importantcommon characterizes. The difference is attributed to multidimensionality of the well-being index. Under this circumstance, education still does influence well-beingpositively conditional on controlling for identity, capability and material well-being.

As a result of its capacity for material discrimination, hyperspectral imaging has been utilized for applications ranging from mining to agriculture to planetary exploration. One of the most common methods of exploiting hyperspectral images is spectral unmixing, which is used to discriminate and locate the various types of materials that are present in the scene. When this processing is done without the aid of a reference library of material spectra, the problem is called blind or unsupervised spectral unmixing. Independent component analysis (ICA) is a blind source separation approach that operates by finding outputs, called independent components, that are statistically independent. ICA has been applied to the unsupervised spectral unmixing problem, producing intriguing, if somewhat unsatisfying results. This dissatisfaction stems from the fact that independent components are subject to a scale ambiguity which must be resolved before they can be used effectively in the context of the spectral unmixing problem. In this dissertation, ICA is explored as a spectral unmixing approach. Various processing steps that are common in many ICA algorithms are examined to assess their impact on spectral unmixing results. Synthetically-generated but physically-realistic data are used to allow the assessment to be quantitative rather than qualitative only. Additionally, two algorithms, class-based abundance rescaling (CBAR) and extended class-based abundance rescaling (CBAR-X), are introduced to enable accurate rescaling of independent components. Experimental results demonstrate the improved rescaling accuracy provided by the CBAR and CBAR-X algorithms, as well as the general viability of ICA as a spectral unmixing approach.

Gait initiation (GI) is an important locomotor transition task that includes anticipatory postural adjustments and the joint propulsion necessary for the first step of walking. Metrics associated with this task are known to change across the lifespan and may provide valuable information for fall risk indication, as falls often occur during transitional tasks. Assessments of discrete variables between fallers and non-fallers at GI have provided insight into differences between groups. However, more complex approaches such as time-series principal component analysis (PCA) may allow the examination of changes in magnitude, pattern, and timing not detectable using discrete comparisons alone. Therefore, this thesis aims to characterize differences between fallers and non-fallers by examining the kinematics and kinetics of gait initiation using time-series PCA. A sample of 56 community-dwelling older adults was recruited for this study and completed five walking trials where GI was measured by two force platforms. PCA of centre of pressure kinematics and kinetics time-series data were used to identify the critical features of the signal, and multivariate analysis of covariance was used to compare the individual loading scores of each principal component for each phase between groups. It was revealed that fallers demonstrated differences in the range of mediolateral movement during weight transfer and forward progression, a greater range of anteroposterior movement in forward progression, and a more gradual rise in vertical forces in the first step, associated with a shorter first step length. These findings point to a tendency for fallers to prioritize stability over forward progression performance, and differences in postural control strategies, compared to non-fallers. Further, the use of time-series PCA helped to highlight differences not detectable using discrete analysis alone. / Graduate

A principal component least mean square (PC-LMS) adaptive algorithm is described that has considerable benefits for large control systems used to implement feedforward control of single frequency disturbances. The algorithm is a transform domain version of the multichannel filtered-x LMS algorithm. The transformation corresponds to the principal components of the transfer function matrix between the sensors and actuators in a control system at a single frequency. The method is similar to other transform domain LMS algorithms because the transformation can be used to accelerate convergence when the control system is ill-conditioned. This ill-conditioning is due to actuator and sensor placement on a continuous structure. The principal component transformation rotates the control filter coefficient axes to a more convenient coordinate system where (1) independent convergence factors can be used on each coordinate to accelerate convergence, (2) insignificant control coordinates can be eliminated from the controller, and (3) coordinates that require excessive control effort can be eliminated from the controller. The resulting transform domain algorithm has lower computational requirements than the filtered-x LMS algorithm. The formulation of the algorithm given here applies only to single frequency control problems, and computation of the decoupling transforms requires an estimate of the transfer function matrix between control actuators and error sensors at the frequency of interest. The feasibility of the method was demonstrated in real-time noise control experiments involving 48 microphones and 12 control actuators mounted on a closed cylindrical shell. Convergence of the PC-LMS algorithm was more stable than the filtered-x LMS algorithm. In addition, the PC-LMS controller produced more noise reduction with less control effort than the filtered-x LMS controller in several tests. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20072号 / 医博第4165号 / 新制||医||1018(附属図書館) / 33188 / 京都大学大学院医学研究科医学専攻 / (主査)教授 前川 平, 教授 小川 誠司, 教授 小柳 義夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Rotylenchulus reniformis is a nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the intraield variable nematode population, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence estimating the nematode infestation on the cotton crop using remote sensing and machine learning techniques which are cost and time effective is the motivation for this study. In the current research, the concept of multi-temporal remote sensing has been implemented in order to design a robust and generalized Nematode detection regression model. Finally, a user friendly web-service is created which is gives trustworthy results for the given input data and thereby reducing the nematode infestation in the crop and their expenses on nematicides.