MULTIVARIATE STATISTICAL PROCESS CONTROL FOR CORRELATION MATRICES

Measures of dispersion in the form of covariance control charts are the multivariate analog to the univariate R-chart, and are used in conjunction with multivariate location charts such as the Hotelling T2 chart, much as the R-chart is the companion to the univariate X-bar chart. Significantly more research has been directed towards location measures, but three multivariate statistics (|S|, Wi, and G) have been developed to measure dispersion. This research explores the correlation component of the covariance statistics and demonstrates that, in many cases, the contribution of correlation is less significant than originally believed, but also offers suggestions for how to implement a correlation control chart when this is the variable of primary interest.This research mathematically analyzes the potential use of the three covariance statistics (|S|, Wi, and G), modified for the special case of correlation. A simulation study is then performed to characterize the behavior of the two modified statistics that are found to be feasible. Parameters varied include the sample size (n), number of quality characteristics (p), the variance, and the number of correlation matrix entries that are perturbed. The performance and utility of the front-running correlation (modified Wi) statistic is then examined by comparison to similarly classed statistics and by trials with real and simulated data sets, respectively. Recommendations for the development of correlation control charts are presented, an outgrowth of which is the understanding that correlation often does not have a large effect on the dispersion measure in most cases

Regression-based Statistical Change Point Analysis for Damage Localization

Structural health monitoring (SHM) research has become a vital tool in maintaining the integrity of structures that has been refined over the years. There are numerous methods for damage detection and localization; yet some are not efficient. For example, researchers have used dynamic properties as damage features to monitor a structure because they change in the presence of damage; however, these methods are global in nature. Research in improving them (i.e. having automated, statistical monitoring techniques) is critical to the advancement of the civil engineering field. This thesis presents the implementation of damage detection methods using an experimental structure. Damage features are created from linear regression models and are utilized in control charts to localize damage because they represent the changing properties of a structure in the event of damage. Therefore, this thesis evaluates the performance of different damage features and change point analysis methods in detecting and localizing damage

A Quality Systems Economic-Risk Design Theoretical Framework

Quality systems, including control charts theory and sampling plans, have become essential tools to develop business processes. Since 1928, research has been conducted in developing the economic-risk designs for specific types of control charts or sampling plans. However, there has been no theoretical or applied research attempts to combine these related theories into a synthesized theoretical framework of quality systems economic-risk design. This research proposes to develop a theoretical framework of quality systems economic-risk design from qualitative research synthesis of the economic-risk design of sampling plan models and control charts models. This theoretical framework will be useful in guiding future research into economic risk quality systems design theory and application

Camouflage assessment:Machine and human

A vision model is designed using low-level vision principles so that it can perform as a human observer model for camouflage assessment. In a camouflaged-object assessment task, using military patterns in an outdoor environment, human performance at detection and recognition is compared with the human observer model. This involved field data acquisition and subsequent image calibration, a human experiment, and the design of the vision model. Human and machine performance, at recognition and detection, of military patterns in two environments was found to correlate highly. Our model offers an inexpensive, automated, and objective method for the assessment of camouflage where it is impractical, or too expensive, to use human observers to evaluate the conspicuity of a large number of candidate patterns. Furthermore, the method should generalize to the assessment of visual conspicuity in non-military contexts.</p

A study of new and advanced control charts for two categories of time related processes

Ph.DDOCTOR OF PHILOSOPH

Affine invariant signed-rank multivariate exponentially weighted moving average control chart for process location monitoring

Multivariate statistical process control (SPC) charts for detecting possible shifts in mean vectors assume that data observation vectors follow a multivariate normal distribution. This assumption is ideal and seldom met. Nonparametric SPC charts have increasingly become viable alternatives to parametric counterparts in detecting process shifts when the underlying process output distribution is unknown, specifically when the process measurement is multivariate. This study examined a new nonparametric signed-rank multivariate exponentially weighted moving average type (SRMEWMA) control chart for monitoring location parameters. The control chart was based on adapting a multivariate spatial signed-rank test. The test was affine-invariant and the weighted version of this test was used to formulate the charting statistic by incorporating the exponentially weighted moving average (EWMA) scheme. The test\u27s in-control (IC) run length distribution was examined and the IC control limits were established for different multivariate distributions, both elliptically symmetrical and skewed. The average run length (ARL) performance of the scheme was computed using Monte Carlo simulation for select combinations of smoothing parameter, shift, and number of p-variate quality characteristics. The ARL performance was compared to the performance of the multivariate exponentially weighted moving average (MEWMA) and Hotelling T2. The control charts for observation vectors sampled the multivariate normal, multivariate t, and multivariate gamma distributions. The SRMEWMA control chart was applied to a real dataset example from aluminum smelter manufacturing that showed the SRMEWMA performed well. The newly investigated nonparametric multivariate SPC control chart for monitoring location parameters--the Signed-Rank Multivariate Exponentially Weighted Moving Average (SRMEWMA)--is a viable alternative control chart to the parametric MEWMA control chart and is sensitive to small shifts in the process location parameter. The signed-rank multivariate exponentially weighted moving average performance for data from elliptically symmetrical distributions is similar to that of the MEWMA parametric chart; however, SRMEWMA\u27s performance is superior to the performance of the MEWMA and Hotelling\u27s T2 control charts for data from skewed distributions

Mudança de regime Markoviano aplicado à fase I do CEP em ambientes de múltiplus setups

O controle estatístico do processo (CEP), através de suas ferramentas, tem como objetivo apontar quando o processo passa de um estado sob controle para um estado fora de controle. O CEP é dividido em duas fases: a fase de diagnóstico sobre a estrutura dos dados (Fase I) e a fase de controle do processo (Fase II). Um dos objetivos principais da Fase I é estimar os valores dos parâmetros do processo. A estimação destes parâmetros fica prejudicada em ambientes de produção flexível, pois as séries são relativamente mais curtas, impedindo uma adequada inferência dos parâmetros. O objetivo deste artigo é apresentar a aplicação de um modelo de mudança de regime markoviano para diagnóstico na Fase I do CEP em ambientes de múltiplos setups. O procedimento de mudança de regime markoviano é capaz de estimar os parâmetros das séries em cada regime, levando em consideração a estrutura de variabilidade comum a todos os regimes a partir da análise da característica da qualidade mensurada

Physics-based and Data-driven Methods with Compact Computing Emphasis for Structural Health Monitoring

This doctoral dissertation contributes to both model-based and model-free data interpretation techniques in vibration-based Structural Health Monitoring (SHM). In the model-based category, a surrogate-based finite element (FE) model updating algorithm is developed to improve the computational efficiency by replacing the FE model with Response Surface (RS) polynomial models in the optimization problem of model calibration. In addition, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed RS models. This methodology is applied to a numerical case study of a steel frame with global nonlinearity. Its performance in presence of measurement noise is compared with a method based on sensitivity analysis and it is observed that while having comparable accuracy, proposed method outperforms the sensitivity-based model updating procedure in terms of required time. With the assumption of Gaussian measurement noise, it is also shown that this parameter estimation technique has low sensitivity to the standard deviation of the measurement noise. This is validated through several parametric sensitivity studies performed on numerical simulations of nonlinear systems with single and multiple degrees of freedom. The results show the least sensitivity to measurement noise level, selected time window for model updating, and location of the true model parameters in RS regression domain, when vibration frequency of the system is outside the frequency bandwidth of the load. Further application of this method is also presented through a case study of a steel frame with bilinear material model under seismic loading. The results indicate the robustness of this parameter estimation technique for different cases of input excitation, measurement noise level, and true model parametersIn the model-free category, this dissertation presents data-driven damage identification and localization methods based on two-sample control statistics as well as damage-sensitive features to be extracted from single- and multivariate regression models. For this purpose, sequential normalized likelihood ratio test and two-sample t-test are adopted to detect the change in two families of damage features based on the coefficients of four different linear regression models. The performance of combinations of these damage features, regression models and control statistics are compared through a scaled two-bay steel frame instrumented with a dense sensor network and excited by impact loading. It is shown that the presented methodologies are successful in detecting the timing and location of the structural damage, while having acceptable false detection quality. In addition, it is observed that incorporating multiple mathematical models, damage-sensitive features and change detection tests improve the overall performance of these model-free vibration-based structural damage detection procedures. In order to extend the scalability of the presented data-driven damage detection methods, a compressed sensing damage localization algorithm is also proposed. The objective is accurate damage localization in a structural component instrumented with a dense sensor network, by processing data only from a subset of sensors. In this method, first a set of sensors from the network are randomly sampled. Measurements from these sampled sensors are processed to extract damage sensitive features. These features undergo statistical change point analysis to establish a new boundary for a local search of damage location. As the local search proceeds, probability of the damage location is estimated through a Bayesian procedure with a bivariate Gaussian likelihood model. The decision boundary and the posterior probability of the damage location are updated as new sensors are added to processing subset and more information about location of damage becomes available. This procedure is continued until enough evidence is collected to infer about damage location. Performance of this method is evaluated using a FE model of a cracked gusset plate connection. Pre- and post-damage strain distributions in the plate are used for damage diagnosis.Lastly, through study of potential causes of damage to the Washington Monument during the 2011 Virginia earthquake, this dissertation demonstrates the role that SHM techniques plays in improving the credibility of damage assessment and fragility analysis of the constructed structures. An FE model of the Washington Monument is developed and updated based on the dynamic characteristics of the structure identified through ambient vibration measurement. The calibrated model is used to study the behavior of the Monument during 2011 Virginia earthquake. This FE model is then modified to limit the tensile capacity of the grout material and previously cracked sections to investigate the initiation and propagation of cracking in several futuristic earthquake scenarios. The nonlinear FE model is subjected to two ensembles of site-compatible ground motions representing different seismic hazard levels for the Washington Monument, and occurrence probability of several structural and non-structural damage states is investigated

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

Development of Landsat-based Technology for Crop Inventories: Appendices

There are no author-identified significant results in this report