Electromechanical Actuators (EMAs) are being increasingly used in many applications. There is a need to augment good design of EMAs with continuous awareness of their operational capability and make them ‘intelligent’ for two key objectives: enhancing performance to address exigent task requirements and to track any changes from their ‘as-built and certified’ state for condition-based maintenance. These objectives are achieved using a decision making philosophy where the human system operator supervises EMA operation using performance criteria and decision surfaces; updated by in-situ measurement of the variables of interest via a suite of diverse sensors. However, operational decisions made on the basis of faulty data could result in unwelcome consequences. With unexpected variations in a sensor’s output from its anticipated values, the challenge is to determine if it indicates a problem in the sensor or the monitored system. In addressing this conundrum, it is also essential to account for the inherent uncertainties present in the values being analyzed. To this end, this dissertation presents the development of a novel Sensor and Process Fault Detection and Isolation (SPFDI) algorithm. This provides a framework to utilize data from all the available sensors in a holistic manner to detect any faults in individual sensors or the system components concurrently. The algorithm uses a Bayesian network to model a system; populated with extensive empirical data. The probabilistic foundations of this method allow for incorporating and propagating uncertainties. The construction of a modular testbed and its Bayesian network are discussed in detail. Several design/ operational criteria have been proposed to aid in the creation of more usable networks in the future. The SPFDI algorithm estimates multiple values for each measurand using different combinations of input variables and probabilistic inferencing. These values are compared against those indicated by the corresponding sensors; a difference between them is indicative of a potential problem. Quantitative indicators to track the condition of different system components and sensors, termed as belief values, are modified after each comparison. The final belief values obtained at the end of an iteration of the algorithm provide a definitive indication of the sources of anomalies in the observed data and can provide guidance to the operator on decisions such as whether or not to use data from a particular sensor for updating existing decision surfaces. The representative examples and experimental results confirm the efficacy of the algorithm in detecting and isolating single as well as multiple sensor faults. The algorithm has also been found to be capable of distinguishing between sensor and system/process faults. Special categories of faults and factors that influence the execution characteristics and quality of results from the algorithm were also explored meticulously and suitable modifications have been suggested to enable the algorithm to continue to function effectively in these situations. To demonstrate the flexibility of the proposed SPFDI algorithm, its potential utilization in four broad classes of applications consisting of complex systems monitored by multiple sensors was also explored in this report. / text

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational framework. Such integrative research to link different knowledge domains faces several practical challenges. The complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. This thesis aims to overcome some of these challenges, and to demonstrate how new modeling approaches can provide successful integrative water resources research. It focuses on the development of new integrated modeling approaches which allow integration of not only physical processes but also socio-economic and environmental issues and uncertainties inherent in water resources systems. To achieve this goal, two new approaches are developed in this thesis. At first, a Bayesian network (BN)-based decision support tool is developed to conceptualize hydrological and socio-economic interaction for supporting management decisions of coupled groundwater-agricultural systems. The method demonstrates the value of combining different commonly used integrated modeling approaches. Coupled component models are applied to simulate the nonlinearity and feedbacks of strongly interacting groundwater-agricultural hydrosystems. Afterwards, a BN is used to integrate the coupled component model results with empirical knowledge and stakeholder inputs. In the second part of this thesis, a fuzzy-stochastic multiple criteria decision analysis tool is developed to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrates physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approaches are applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structures. The results show the effectiveness of the proposed methods. The first method can aid in the impact assessment of alternative management interventions on sustainability of aquifer systems while accounting for economic (agriculture) and societal interests (employment in agricultural sector) in the study area. Results from the second method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. The new approaches can be applied to address the complexities and uncertainties inherent in water resource systems to support management decisions, while serving as a platform for stakeholder participation.

<p>Natural steroidal estrogen hormones in swine wastes generated from Concentrated Animal Feeding Operations (CAFOs) have become a potential pollutant to many aquatic environments due to their adverse impacts on the reproductive biology of aquatic organisms. In North Carolina, the swine CAFO industry is a major agricultural economic enterprise that is responsible for the generation of large volumes of waste. However, there is limited scientific understanding regarding the concentration, fate, and transport of the estrogenic compounds from these swine facilities into terrestrial and aquatic environments. To address this issue, my research involved the development and application of integrated Bayesian networks (BNs) models that can be used to better characterize and assess the generation, fate, and transport of site-specific swine CAFO-derived estrogen compounds. The developed model can be used as decision support tool towards estrogen risk assessment. Modularized and melded BN approaches were used to capture the predictive and casual relationships of the estrogen budget and its movement within and between the three major systems of a swine farrowing CAFO. These systems include the animal barns, the anaerobic waste lagoon, and the spray fields. For the animal barn system, a facility-wide estrogen budget was developed to assess the operation-specific estrogen excretion, using an object-oriented BN (OOBN) approach. The developed OOBN model provides a means to estimate and predict estrogen fluxes from the whole swine facility in the context of both estrogen type and animal operating unit. It also accounts for the uncertainties in the data and in our understanding of the system. Next, mass balance melding BN models were developed to predict the natural estrogen fates and budgets in two lagoon compartments, the slurry and the sludge storage. This involved utilizing mass balance equations to account for the mechanisms of flushing, sorption, transformation, settling, and burial reactions of estrogen compounds in the slurry and sludge storages. As an alternative approach, a regression based BN melding approach was developed to both characterize estrogen fate and budgets as a result of the sequential transformation processes between natural estrogen compounds and to assess the seasonal effects on the estrogen budgets in the two different lagoon compartments. Finally, a dynamic BN model was developed to characterize rainfall-driven estrogen runoff processes from the spray fields. The dynamic BN models were used to assess the potential risk of estrogen runoff to adjacent waterways. In addition, the dynamic model was used to quantify the effects of manure application rates, rainfall frequency, the time of rainfall and irrigation, crop types, on-farm best management practices, seasonal variability, and successive rainfall and manure application events on estrogen runoff. </p><p>The model results indicate that the farrowing barn is the biggest contributor of total estrogen as compared to the breeding and gestation operating barns. Once the estrogen reaches the anaerobic lagoon, settling and burial reactions were shown to be the most significant factors influencing estrogen levels in the slurry and sludge, respectively. The estrogen budgets in the lagoon were also found to vary by season, with higher slurry and sludge estrogen levels in the spring as compared to the summer. The risk of estrogen runoff was predicted to be lower in the summer as compared to the spring, primarily due to the spray field crop management plans adopted. The results also indicated that Bermuda grass performed more favorably when compared to soybean, when it came to retaining surface water runoff in the field. Model predictions indicated that there is a low risk of estrogen runoff losses from the spray fields under multiple irrigation and rainfall events, unless the time interval between irrigation was less than 10 days and/or in the event of a prolonged high magnitude rainstorm event. Overall, the estrone was the most persistent form of natural estrogens in the three major systems of the swine farrowing CAFO.</p> / Dissertation

Constructing a Bayesian Network requires the conditional probabilities table (CPT) to be acquired, one for each variable or node in the network. When data mining is not available, CPTs must be acquired from the domain experts. The complexity of the direct elicitation is exponential on the number of parents of a variable, making direct elicitation from human experts impractical for a large number of causes. Causal models such as Noisy-OR, Noisy-AND, Noisy-MIN, Noisy-MAX and Recursive Noisy-OR have been developed that allow CPTs acquisition to be achieved with linear complexity on the number of causes. Their representation power is measured by their ability to encode the causal interactions. Causal interactions can be categorized into two types: reinforcing and undermining. The Non-Impeding Noisy-AND or NIN-AND tree causal model, developed by Xiang and Jia, is capable of modeling both types of interaction while retaining the linear complexity. The main challenge in utilizing the NIN-AND tree model to generate a CPT is that it requires its tree topology to be elicited. A NIN-AND tree topology is an encoding of the causal interactions between the causes. In this work we present two methods, Structure Elimination (SE) and Pairwise Causal Interaction (PCI), that allow indirect elicitations of the NIN-AND tree topology using some additional probabilities elicited from experts. We conduct human-based experiment to investigate the e ectiveness of the two methods in terms of accuracy by comparing them to the Direct Numerical (DN) elicitation method. We recruit participants from second year Computer Science students at the University of Guelph. The process involves training a participant into domain expert using a known NIN-AND tree model then acquire another NIN-AND tree model by applying the SE and PCI methods. The CPTs produced by the acquired NIN-AND tree models are then compared to the one obtained by using the DN method. Comparable CPT accuracies are obtained among models generated by di erent methods, even though SE and PCI requires a much smaller number of parameters in comparison to DN.

Issues of Disinfection By-Product (DBP) formation in response to chlorination in drinking water treatment systems is a common issue encountered by WTP operators. Efforts to minimize DBP formation are complicated by the presence of zebra mussels, which may inhabit the raw water intake of WTPs. While chlorination at the intake to control zebra mussel populations is effective, the formation of DBPs is exacerbated. Methods for reducing DBPs are explored, including adjusting the location for chlorine additions in the treatment sequence. Multivariate models for Total Trihalomethane (TTHM) and Haloacetic Acid (HAA) subspecies are employed to show that in some instances pre-chlorination can be reduced to lower DBP formation, while post-chlorination can be increased. A Regression model (R2 of 0.75) predicts that DBP levels can be lowered by post-chlorination rather than pre-chlorinating raw water for portions of the year except during the combatable life stage to assist in zebra mussel control. A second multivariate regression model for TTHM (R2 = 0.91) which includes bromide, a variable which, due to lack of data, was previously unused, is described and demonstrates that DBP levels can be reduced by lowering pre-chlorination levels. Finally, a Bayesian network is developed using the Webweavr-IV Toolkit, utilizing causal relationships between raw water quality parameters in the form of conditional probabilities. The results show that the average cancer risk can be decreased by while still maintaining zebra mussel control and simultaneously decreasing the incremental cancer risk, which currently fluctuates between 1 in 50,000 to 100,000 in Ontario. / Canada Research Chair Program, Ontario Research Foundation

Risk-based authentication is an increasingly popular component in the security architecture deployed by many organizations in mitigating online identity threat. Risk-based authentication uses contextual and historical information extracted from online communications to build a risk profile for the user that can be used to make accordingly authentication and authorization decisions. Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed and as such put in question the robustness and reliability of the proposed systems. In this thesis, we propose in this work an online risk-based authentication system which provides more robust user identity information by combining mouse dynamics, keystroke dynamics biometrics, and user site actions in a multimodal framework. We propose a Bayesian network model for analyzing free keystrokes and mouse movements involved in web sessions. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 6.91%. This is encouraging considering that we are dealing with free text and mouse movements and the fact that many web sessions tend to be short. / Graduate

The advances of high-throughput technologies, such as next-generation sequencing and microarrays, have rapidly improved the accessibility of molecular profiles in tumor samples. However, due to the immaturity of relevant theories, analyzing these data and systematically understanding the underlying mechanisms causing diseases, which are essential in the development of therapeutic applications, remain challenging. This dissertation attempts to clarify the effects of DNA copy number alterations (CNAs), which are known to be common mutations in genetic diseases, on steady- state gene expression values, time-course expression activities, and the effectiveness of targeted therapy. Assuming DNA copies operate as independent subsystems producing gene transcripts, queueing theory is applied to model the stochastic processes representing the arrival of transcription factors (TFs) and the departure of mRNA. The copy-number-gene-expression relationships are shown to be generally nonlinear. Based on the mRNA production rates of two transcription models, one corresponding to an unlimited state with prolific production and one corresponding to a restrictive state with limited production, the dynamic effects of CNAs on gene expression are analyzed. Simulations reveal that CNAs can alter the amplitudes of transcriptional bursting and transcriptional oscillation, suggesting the capability of CNAs to interfere with the regulatory signaling mechanism. With this finding, a string-structured Bayesian network that models a signaling pathway and incorporates the interference due to CNAs is proposed. Using mathematical induction, the upstream and downstream CNAs are found to have equal influence on drug effectiveness. Scoring functions for the detection of unfavorable CNAs in targeted therapy are consequently proposed. Rigorous experiments are keys to unraveling the etiology of genetic diseases such as cancer, and the proposed models can be applied to provide theory-supporting hypotheses for experimental design.

This paper presents an expert system to monitor social sustainability compliance in supply chains. The system allows to continuously rank suppliers based on their risk of breaching sustainability standards on child labor. It uses a Bayesian network to determine the breach likelihood for each supplier location based on the integration of statistical data, audit results and public reports of child labor incidents. Publicly available statistics on the frequency of child labor in different regions and industries are used as contextual prior. The impact of audit results on the breach likelihood is calibrated based on expert input. Child labor incident observations are included automatically from publicly available news sources using text mining algorithms. The impact of an observation on the breach likelihood is determined by its relevance, credibility and frequency. Extensive tests reveal that the expert system correctly replicates the decisions of domain experts in the fields supply chain management, sustainability management, and risk management.

abstract: This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Cette thèse s'inscrit dans le domaine de la diagnostic, en particulier de Manufacturing Execution System (MES) . Elle apporte sa contribution au diagnostic de système en présence de défaillances potentielles suit à une variation du TRS, un indicateur de performance qui donne une image de l’état de fonctionnement d’un système de production (équipement, ligne, atelier, usine) à travers l’estimation des pertes selon trois origines : disponibilité, performance, qualité. L’objectif est de fournir le maximum d’informations sur les origines d’une variation du TRS afin de permettre à l'exploitant de prendre la bonne décision. Aussi, sur la base d'un tel modèle, nous proposons une méthodologie de déploiement pour intégrer une fonction de diagnostic aux solutions MES existantes dans un contexte industriel. / This Phd thesis takes place in the diagnostic field, especially in contexte of Manufacturing Execution System (MES). It contributes to the diagnostic system in the presence of potential failures following a triggering signal OEE drift, an indicator performance that gives a picture of the production system state (equipment, production line, site, and enterprise) by estimating downtime from 3 major origins: availability, performance, and quality. Our objective is to provide maximum information of the origins of an OEE variation and to support making the best decision for four categories users of OEE (operator, leader team, supervisor, direction). Also, basis on that model, the purpose will provides a deployment methodology to integrate with MES solution in an industrial context.