Utilities have been planning, building, and operating electrical distribution systems in much the same way for decades with great success. The electrical distribution system in the United States has been consistently reliable; an impressive feat considering its amazing complexity. However, in recent years, the electrical distribution system landscape has started to undergo drastic changes. Emerging applications of technologies such as distributed generation, communications, and power electronics offer both opportunities and challenges to power system operators as well as customers and developers. In this work, Graph Trace Analysis along with an integrated system model are used to develop algorithms and analysis methods necessary to facilitate the implementation of these new technologies on the electrical distribution system. A penetration limit analysis is developed to analyze the impact of distributed generation on radial distribution feeders. The analysis considers generation location, equipment rating, voltage violations, and flicker to determine the amount of generation that can be safely attached to a circuit. A real-time, hierarchical, model-based control method is developed that coordinates the operation of all control devices on electrical distribution circuits. The controller automatically compensates for changes in circuit topology as well as the addition or removal of control devices from the active circuit. Additionally, the controller allows the integration of modern, "smart" equipment with legacy control devices to facilitate incremental modernization strategies. Finally, a framework is developed to allow the testing of new analysis and control methodologies for electrical distribution systems. The framework can be used to test scenarios over multiple consecutive hourly or sub-hourly time points. The framework is used to demonstrate the effectiveness of the model-based controller versus existing operating methods for a distribution circuit test case. / Ph. D.

The Gaussian mixture model has been used for model-based clustering analysis for decades. Most model-based clustering analyses are based on the Gaussian mixture model. Model averaging approaches for Gaussian mixture models are proposed by Wei and McNicholas, based on a family of 14 Gaussian parsimonious clustering models. In this thesis, we use non-Gaussian mixture models, namely the tEigen family, for our averaging approaches. This paper studies fitting in an averaged model from a set of multivariate t-mixture models instead of fitting a best model. / Thesis / Master of Science (MSc)

Model-based clustering is a probabilistic approach that views each cluster as a component in an appropriate mixture model. The Gaussian mixture model is one of the most widely used model-based methods. However, this model tends to perform poorly when clustering high-dimensional data due to the over-parametrized solutions that arise in high-dimensional spaces. This work instead considers the approach of combining dimension reduction techniques with clustering via a mixture of generalized hyperbolic distributions. The dimension reduction techniques, principal component analysis and factor analysis along with their extensions were reviewed. Then the aforementioned dimension reduction techniques were individually paired with the mixture of generalized hyperbolic distributions in order to demonstrate the clustering performance achieved under each method using both simulated and real data sets. For a majority of the data sets, the clustering method utilizing principal component analysis exhibited better classi cation results compared to the clustering method based on the extending the factor analysis model. / Thesis / Master of Science (MSc)

Yes / We present a formalism to represent cyclic models and study di erent semantic frameworks that support testing. These models combine sequences of observable actions and the passing of (discrete) time and can be used to specify a number of classes of reactive systems, an example being robotic systems. We use implementation relations in order to formally define a notion of correctness of a system under test (SUT) with respect to a specification. As usual, the aim is to devise an extension of the classical ioco implementation relation but available timed variants of ioco are not suitable for cyclic models. This paper thus defines new implementation relations that encapsulate the discrete nature of time and take into account not only the actions that models can perform but also the ones that they can refuse. In addition to defining these relations, we study a number of their properties and provide alternative characterisations, showing that the relations are appropriate conservative extensions of trace containment. Finally, we give test derivation algorithms and prove that they are sound and also are complete in the limit. / Engineering and Physical Sciences Research Council Grant numbers: EP/R025134/2. Ministerio de Economía y Competitividad Grant numbers: RTI2018-093608-B-C31. Comunidad de Madrid Grant numbers: S2018/TCS-4314

The use of alternative fuels and advanced powertrain technologies has been increasing over the past few years as vehicle emissions and fuel economy have become prominent in both manufacturer needs and consumer demands. With more hybrids emerging from all automotive manufacturers, the use of computer modeling has quickly taken a lead in the testing of these innovative powertrain designs. Although on-vehicle testing remains an important part of the design process, modeling and simulation is proven to be an invaluable tool that can be applied anywhere from preliminary powertrain design to controller software validation. The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is applying for participation in the next Advanced Vehicle Technology Competition. EcoCAR 3 is a new four year competition sponsored by the Department of Energy and General Motors with the intention of promoting sustainable energy in the automotive sector. The goal of the competition is to guide students from universities in North America to create new and innovative technologies to reduce the environmental impact of modern day transportation. EcoCAR 3, like its predecessors, will give students hands-on experience in designing and implementing advanced technologies in a setting similar to that of current production vehicles. The primary goals of the competition are to improve upon a provided conventional, internal combustion engine production vehicle by designing and constructing a powertrain that accomplishes the following: • Reduce Energy Consumption • Reduce Well-to-Wheel (WTW) Greenhouse Gas (GHG) Emissions • Reduce Criteria Tailpipe Emissions • Maintain Consumer Acceptability in the area of Performance, Utility, and Safety • Meet Energy and Environmental Goals, while considering Cost and Innovation This paper presents a systematic approach in selecting a powertrain for HEVT to develop in the upcoming competition using model-based design. Using a base set of powertrain component models, several powertrain configurations are modeled and tested to show the progression from a basic conventional vehicle to several advanced hybrid vehicles. Each model is designed to generate energy consumption data, efficiency, emissions, as well as many other parameters that can be used to compare each of the powertrain configurations. A powertrain design is selected to meet the goals of the competition after exploring many powertrain configurations and energy sources. Three parallel powertrains are discussed to find a combination capable of meeting the target energy consumption and WTW GHG emissions while also meeting all of the performance goals. The first of these powertrains is sized to model a typical belted alternator starter (BAS) system and shows small improvements over a conventional vehicle. The next design is a parallel through the road hybrid that is sized to meet most power needs with an electric motor and a smaller IC engine. This case comes closer to the design goals, but still falls short on total energy consumption. Lastly, the battery and motor size are increased to allow a charge depleting mode, adding stored grid electricity to the energy sources. This electric energy only mode is able to displace a large amount of the fuel energy consumption based on the SAE J1711 method for determining utility factor weighted energy consumption of a plug-in hybrid vehicle. The final design is a Parallel Plug-In Hybrid Electric Vehicle using E85 fuel and a 7 kWh battery to provide an all-electric charge depleting range of 34 km (21 mi). / Master of Science

Many common clustering methods cannot be used for clustering multivariate longitudinal data when the covariance of random variables is a function of the time points. For this reason, a copula kernel mixture model (CKMM) is proposed for clustering such data. The CKMM is a finite mixture model that decomposes each mixture component’s joint density function into a copula and marginal distribution functions, where a Gaussian copula is used for its mathematical traceability. This thesis considers three scenarios: first, the CKMM is developed for balanced multivariate longitudinal data with known eigenfunctions; second, the CKMM is used to fit unbalanced data where trajectories are aligned on the time axis, and eigenfunctions are unknown; and lastly, a dynamic CKMM (DCKMM) is applied to unbalanced data where trajectories are misaligned, and eigenfunctions are unknown. Expectation-maximization type algorithms are used for parameter estimation. The performance of CKMM is demonstrated on both simulated and real data. / Thesis / Candidate in Philosophy

In this thesis, we study re-weighting when estimating totals in survey sampling. The purpose of re-weighting is to adjust the structure of the sample in order to comply with the structure of the population (with respect to given auxiliary variables). We sum up some known results for methods of the traditional desin-based approach, more attention is given to the model-based approach. We generalize known asymptotic results in the model-based theory to a wider class of weighted estimators. Further, we propose a consistent estimator of asymptotic variance, which takes into consideration weights used in estimator of the total. This is in contrast to usually recommended variance estimators derived from the design-based approach. Moreover, the estimator is robust againts particular model misspecifications. In a simulation study, we investigate how the proposed estimator behaves in comparison with variance estimators which are usually recommended in the literature or used in practice. 1

The purpose of this thesis is to investigate the applicability of a certain model based classification algorithm. The algorithm is centered around a flexible wireframe prototype that can instantiate a number of different vehicle classes such as a hatchback, pickup or a bus to mention a few. The parameters of the model are fitted using Newton minimization of errors between model line segments and observed line segments. Furthermore a number of methods for object detection based on motion are described and evaluated. Results from both experimental and real world data is presented.

<p>The purpose of this thesis is to investigate the applicability of a certain model based classification algorithm. The algorithm is centered around a flexible wireframe prototype that can instantiate a number of different vehicle classes such as a hatchback, pickup or a bus to mention a few. The parameters of the model are fitted using Newton minimization of errors between model line segments and observed line segments. Furthermore a number of methods for object detection based on motion are described and evaluated. Results from both experimental and real world data is presented.</p>

This thesis describes the AP1 system, which serves as a platform for model-based CASE technology. AP1 is a set of libraries and tools that support different activities in the software development process. It provides different layers of reusable CASE functionality, and thus facilitates CASE tool development and integration. Some key problems of software development are addressed, such as the storage and management of artifacts, their creation and modification, and the generation of program code. The main parts of the abstract platform are a typed repository for models and model data, and a generic editor that acts as an integrated software development environment (IDE). The former enables data integration; the latter serves as a basis for presentation integration. Both parts have an extensible and customizable architecture that makes it possible for developers to adapt the system to their own individual needs. The thesis discusses different data models, explaining why the parsimonious data model was chosen for the repository. A mapping onto the relational data model is given that makes it possible to leverage a RDBMS for data management. On top of the RDBMS, the AP1 system implements new mechanisms for caching, event notification and change control, resulting in a unique architecture. The thesis introduces novel concepts of robustness and reflection for user interfaces, and delineates their implementation in the generic editor. Furthermore, a concept for code generators is presented that offers a particularly high degree of type-safety, which we call generator type-safety.