Graduation date: 2006 / This research addresses sample process variance estimation on continuous domains and for non-probability samples in particular. The motivation for the research is a scenario in which a program has collected non-probability samples for which there is interest in characterizing how much an extrapolation to the domain would vary given similarly arranged collections of observations. This research does not address the risk of bias and a key assumption is that the observations could represent the response on the domain of interest. This excludes any hot-spot monitoring programs. The research is presented as a collection of three manuscripts. The first (to be published in Environmetrics (2006)) reviews and compares model- and design-based approaches for sampling and estimation in the context of continuous domains and promotes a model-assisted sample-process variance estimator. The next two manuscripts are written to be companion papers. With the objective of quantifying uncertainty of an estimator based on a non-probability sample, the proposed approach is to first characterize a class of sets of locations that are similarly arranged to the collection of locations in the non-probability sample, and then to predict variability of an estimate over that class of sets using the covariance structure indicated by the non-probability sample (assuming the covariance structure is indicative of the covariance structure on the study region). The first of the companion papers discusses characterizing classes of similarly arranged sets with the specification of a metric density. Goodness-of-fit tests are demonstrated on several types of patterns (dispersed, random and clustered) and on a non-probability collection of locations surveyed by Oregon Department of Fish & Wildlife on the Alsea River basin in Oregon. The second paper addresses predicting the variability of an estimate over sets in a class of sets (using a Monte Carlo process on a simulated response with appropriate covariance structure).

Development of embedded systems poses an increasing challenge fordevelopers largely due to increasing complexity. Several factors contribute tothe complexity challenge: • the number of extra-functional properties applying to embedded systems,such as resource usage, timing effects, safety. • the functionality of embedded systems, to a larger extent than for othersoftware, involves engineers from multiple different disciplines, such asmechanical, control, software, safety, systems and electrical engineers.Themulti-disciplinarity causes the development environments to consistof separate data, models and tools. Several engineering paradigms to handle this complexity increase havebeen suggested, including methodologies focused on architecture, models andcomponents. In systems engineering, a long-standing approach has been todescribe the system in several views, each according to a certain viewpoint.By doing so, a divide-and-conquer strategy is applied to system concerns.Unfortunately, it is hard to always find completely independent concerns:there is always some semantic overlap between the different views. Modelbaseddesign (MBD) deals with building sound abstractions that can representa system under design and be used for analysis. Component-based design(CBD) focuses on how to build reusable component models with well-definedcomposition models. In this thesis, a concept of formalized multi-viewed component models (MVCM) is proposed, which integrates the three above mentioned paradigms.Principles and guidelines for MV CMs are developed. One of the main challengesfor the proposition is to provide MV CMs that produce composabilityboth along component boundaries and viewpoint boundaries. To accomplishthis, the relations between viewpoints need to be explicitly taken into account.Further, the semantic relations between these viewpoints need to be explicitlymodeled in order to efficiently ensure that the views are kept consistent. Asa main contribution, this thesis presents the formalization of the conceptsneeded to build such component models. A proper formalization of multiviewedconcerns provides several opportunities. Given suitable tool support, itwill be feasible to automate architecture analysis and architecture exploration. The thesis includes a number of case studies that provide insight andfeedback to the problem formulation and validating the results. The casestudies include a resource-aware reconfigurable middleware, a design of anarchitecture exploration methodology, and a windshield wiper system. / <p>QC 20130527</p>

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.

In this master’s thesis, a turbine model and a common control structure for theturbocharger for SI and CI-engines is developed. To design the control structure,simulations are done on an existing diesel engine model with VGT. In order tobe able to make simulations for engines with a wastegated turbine, the model isextended to include mass flow and turbine efficiency for that configuration. Thedeveloped model has a mean absolute relative error of 3.6 % for the turbine massflow and 7.4 % for the turbine efficiency. The aim was to control the intake manifoldpressure with good transients and to use the same control structure for VGTand wastegate. By using a common structure, development and calibration timecan be reduced. The non-linearities have been reduced by using an inverted turbinemodel in the control structure, which consists of a PI-controller with feedforward.The controller can be tuned to give a fast response for CI engines and a slowerresponse but with less overshoot for SI engines, which is preferable.

The objectives in this thesis are to present a model of a variable venturi in an exhaust gas recirculation (EGR) system located in a heavy duty diesel engine. A new legislation called EURO~4 will come into force in 2005 which affects truck development and it will require an On-Board Diagnostic system in the truck. If model based diagnostic systems are to be used, one of the advantages is that the system performance will increase if a model of a variable venturi is used. Three models with different complexity are compared in ten different experiments. The experiments are performed in a steady flow rig at different percentage of EGR gases and venturi areas. The model predicts the mass flow through the venturi. The results show that the first model with fewer simplifications performs better and has fewer errors than the other two models. The simplifications that differ between the models are initial velocity before the venturi and the assumption of incompressible flow. The model that shows the best result is not proposed by known literature in this area of knowledge and technology. This thesis shows that further studies and work on this model, the model with fewer simplifications, can be advantageous.

Conventional statistical methods assuming data sampled under simple random sampling are inadequate for use on complex survey data with a multilevel structure and non-independent observations. In structural equation modeling (SEM) framework, a researcher can either use the ad-hoc robust sandwich standard error estimators to correct the standard error estimates (Design-based approach) or perform multilevel analysis to model the multilevel data structure (Model-based approach) to analyze dependent data. In a cross-sectional setting, the first study aims to examine the differences between the design-based single-level confirmatory factor analysis (CFA) and the model-based multilevel CFA for model fit test statistics/fit indices, and estimates of the fixed and random effects with corresponding statistical inference when analyzing multilevel data. Several design factors were considered, including: cluster number, cluster size, intra-class correlation, and the structure equality of the between-/within-level models. The performance of a maximum modeling strategy with the saturated higher-level and true lower-level model was also examined. Simulation study showed that the design-based approach provided adequate results only under equal between/within structures. However, in the unequal between/within structure scenarios, the design-based approach produced biased fixed and random effect estimates. Maximum modeling generated consistent and unbiased within-level model parameter estimates across three different scenarios. Multilevel latent growth curve modeling (MLGCM) is a versatile tool to analyze the repeated measure sampled from a multi-stage sampling. However, researchers often adopt latent growth curve models (LGCM) without considering the multilevel structure. This second study examined the influences of different model specifications on the model fit test statistics/fit indices, between/within-level regression coefficient and random effect estimates and mean structures. Simulation suggested that design-based MLGCM incorporating the higher-level covariates produces consistent parameter estimates and statistical inferences comparable to those from the model-based MLGCM and maintain adequate statistical power even with small cluster number.

Approaches were developed for building extraction and updating from high resolution satellite imagery. The developed approaches include two main stages: (i) detecting the building patches and (ii) delineating the building boundaries. The building patches are detected from high resolution satellite imagery using the Support Vector Machines (SVM) classification, which is performed for both the building extraction and updating approaches. In the building extraction part of the study, the previously detected building patches are delineated using the Hough transform and boundary tracing based techniques. In the Hough transform based technique, the boundary delineation is carried out using the processing operations of edge detection, Hough transformation, and perceptual grouping. In the boundary tracing based technique, the detected edges are vectorized using the boundary tracing algorithm. The results are then refined through line simplification and vector filters. In the building updating part of the study, the destroyed buildings are determined through analyzing the existing building boundaries and the previously detected building patches. The new buildings are delineated using the developed model based approach, in which the building models are selected from an existing building database by utilizing the shape parameters. The developed approaches were tested in the Batikent district of Ankara, Turkey, using the IKONOS panchromatic and pan-sharpened stereo images (2002) and existing vector database (1999). The results indicate that the proposed approaches are quite satisfactory with the accuracies computed in the range from 68.60% to 98.26% for building extraction, and from 82.44% to 88.95% for building updating.

This thesis proposes an advanced architecting methodology. This methodology allows for the sizing and optimization of aircraft system architecture concepts and the establishment of subsystem development strategies. The process is implemented by an architecting team composed of subsystem experts and architects. The methodology organizes the architecture definition using the SysML language. Using meta-modeling techniques, this definition is translated into an analysis model which automatically integrates subsystem analyses in a fashion that represents the specific architecture concept described by the team. The resulting analysis automatically sizes the subsystems composing it, synthesizes their information to derive architecture-level performance and explores the architecture internal trade-offs. This process is facilitated using the Coordinated Optimization method proposed in this dissertation. This method proposes a multi-level optimization setup. An architecture-level optimizer orchestrates the subsystem sizing optimizations in order to optimize the aircraft as whole. The methodologies proposed in this thesis are tested and demonstrated on a proof of concept based on the exploration of turbo-electric propulsion aircraft concepts.

My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time functions which are similar in shape. The time functions are decomposed into spatial global and time-dependent cluster effects using a semi-parametric model. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across curves from nearby locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small number of time points, high noise level and varying dependence structures. Over all simulation settings, the spatial-functional clustering method outperforms existing model-based clustering methods. In the case study presented in this project, we focus on estimates and classifies service accessibility patterns varying over a large geographic area (California and Georgia) and over a period of 15 years. The focus of this study is on financial services but it generally applies to any other service operation. The second research project of this part studies an association analysis of space-time varying processes, which is rigorous, computational feasible and implementable with standard software. We introduce general measures to model different aspects of the temporal and spatial association between processes varying in space and time. Using a nonparametric spatiotemporal model, we show that the proposed association estimators are asymptotically unbiased and consistent. We complement the point association estimates with simultaneous confidence bands to assess the uncertainty in the point estimates. In a simulation study, we evaluate the accuracy of the association estimates with respect to the sample size as well as the coverage of the confidence bands. In the case study in this project, we investigate the association between service accessibility and income level. The primary objective of this association analysis is to assess whether there are significant changes in the income-driven equity of financial service accessibility over time and to identify potential under-served markets. The second part of the thesis discusses novel statistical methodology for analyzing multilevel functional data including a clustering method based on a functional ANOVA model and a spatio-temporal model for functional data with a nested hierarchical structure. In this part, I introduce and compare a series of clustering approaches for multilevel functional data. For brevity, I present the clustering methods for two-level data: multiple samples of random functions, each sample corresponding to a case and each random function within a sample/case corresponding to a measurement type. A cluster consists of cases which have similar within-case means (level-1 clustering) or similar between-case means (level-2 clustering). Our primary focus is to evaluate a model-based clustering to more straightforward hard clustering methods. The clustering model is based on a multilevel functional principal component analysis. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small vs. moderate number of time points, high noise level and small number of measurement types. We demonstrate the applicability of the clustering analysis to a real data set consisting of time-varying sales for multiple products sold by a large retailer in the U.S. My ongoing research work in multilevel functional data analysis is developing a statistical model for estimating temporal and spatial associations of a series of time-varying variables with an intrinsic nested hierarchical structure. This work has a great potential in many real applications where the data are areal data collected from different data sources and over geographic regions of different spatial resolution.

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.