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.

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.

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.

Background: Movement screens are frequently used to identify abnormal movement patterns that may increase risk of injury and/or hinder performance. However, abnormal patterns are often detected visually based on the observations of a coach or clinician leading to poor inter- and intrarater reliability. In addition, they have been criticized for having poor validity and sensitivity. Quantitative, or data-driven methods can increase objectivity, remove issues related to inter-rater reliability and offer the potential to detect new and important features that may not be observable by the human eye. The combination of motion capture data, pattern recognition and machine learning could provide a quantitative method to better assess movement competency. Purpose: The purpose of this doctoral thesis was to create the foundation for the development of an objective movement screening tool that combines motion capture data, pattern recognition and machine learning. This doctoral thesis is part of a larger project to bring an objective movement screening tool for use in the field to market. Methods: This thesis is comprised of four studies based on a single data collection and a common series of pre-processing steps. Data from 542 athletes were collected by Motus Global, a for-profit biomechanics company, with athletes ranging in competition level from youth to professional and competing in a wide-range of sports. For the first study of this thesis, an online software program was developed to examine the inter- and intra-reliability of a movement screen, with intrareliability being further examined to compare reliability when body-shape was and was not modified. The second study developed the objective movement screen framework that utilized motion capture, pattern recognition and machine learning. Study 3 and 4 assessed different types of input data, classification goals (e.g., skill level and sport played), feature reduction and selection methods, and increasingly complex machine learning algorithms. Results: For Study 1, when looking at inter- and intra-rater reliability of expert assessors during subjective scoring of movements, intra-rater reliability was better than inter-rater reliability. When assessing the effects of body-shape, on average, reliability worsened when body-shape was manipulated. Study 2 provided proof-of-principle that athletes were able to be classified based on skill level using marker-based optical motion capture data, principal component analysis (PCA) and linear discriminant analysis. For Study 3, PCA in combination with linear classifiers outperformed non-linear classifiers when classifying athletes based on skill level; feature selection increased classification rates, and classification rates when using simulated inertial measurement unit data as the input data were on average better than when using marker-based optical motion capture data. In Study 4, athletes were able to be differentiated based on sport played and recurrent neural nets (RNNs) and PCA in combination with traditional linear classifiers were the optimal machine learning algorithms when classifying athletes based on skill level and sport played. Conclusion: This thesis demonstrates that objective methods can differentiate athletes based on desired demographics using motion capture, pattern recognition and machine learning. This thesis is part of a larger project to bring an objective movement screening tool for field-use to market and provides a solid foundation to use in the continued development of an objective movement screening tool.

Forensic fiber evidence plays an important role in many criminal investigations. Nondestructive techniques that preserve the physical integrity of the fibers for further court examination are highly valuable in forensic science. Non-destructive techniques that can either discriminate between similar fibers or match a known to a questioned fiber - and still preserve the physical integrity of the fibers for further court examination - are highly valuable in forensic science. When fibers cannot be discriminated by non-destructive tests, the next reasonable step is to extract the questioned and known fibers for dye analysis with a more selective technique such as high-performance liquid chromatography (HPLC) and/or gas chromatography-mass spectrometry (GC-MS). The common denominator among chromatographic techniques is to primarily focus on the dyes used to color the fibers and do not investigate other potential discriminating components present on the fiber. Differentiating among commercial dyes with very similar chromatographic behaviors and almost identical absorption spectra and/or fragmentation patterns is a challenging task. This dissertation explores a different aspect of fiber analysis as it focuses on the total fluorescence emission of fibers. In addition to the contribution of the textile dye (or dyes) to the fluorescence spectrum of the fiber, we investigate the contribution of intrinsic fluorescence impurities – i.e. impurities imbedded into the fibers during fabrication of garments - as a reproducible source of fiber comparison. Differentiation of visually indistinguishable fibers is achieved by comparing excitation-emission matrices (EEMs) recorded from single textile fibers with the aid of a commercial spectrofluorimeter coupled to an epi-fluorescence microscope. Statistical data comparison was carried out via principal component analysis. An application of iv this statistical approach is demonstrated using challenging dyes with similarities both in twodimensional absorbance spectra and in three dimensional EEM data. High accuracy of fiber identification was observed in all the cases and no false positive identifications were observed at 99% confidence levels.